System

Abstract

An object of a system according to an embodiment is to estimate an interaction between a plurality of applications and automatically complete a user's input.SOLUTION: A system according to an embodiment includes a reception unit, an estimation unit, and a complementary unit. The receiving unit receives an input from a user. The estimation unit estimates the interaction between the applications on the basis of the information received by the reception unit. The complementing unit automatically complements an input to each application on the basis of the interaction estimated by the estimating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] In conventional technology, when multiple applications are used at the same time, each application operates independently and there is a lack of interaction, which causes a problem of complicated user operations.

[0005] The system according to the embodiment aims to predict interactions between multiple applications and automatically complete user input. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, an inferring unit, and a completing unit. The receiving unit receives user input. The inferring unit infers interactions between applications based on the information received by the receiving unit. The completing unit automatically completes input to each application based on the interactions inferred by the inferring unit. [Effects of the Invention]

[0007] The system according to the embodiment can infer interactions between multiple applications and auto-complete user input. [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) A system according to an embodiment of the present invention integrates multiple xR applications. This system uses a generation AI to predict interactions between applications and automatically complete user inputs for each application, enabling the integration of multiple applications. For example, a user launches multiple xR applications. The generation AI then analyzes the behavior of each application and predicts interactions between the applications. For example, if one application requires the user's location information, it can use location information provided by other applications. In this way, the generation AI predicts interactions between applications and automatically completes the necessary information. Furthermore, the generation AI analyzes user inputs and automatically completes inputs for each application. For example, when a user performs a specific operation in one application, inputs for other applications are automatically generated based on that operation. This allows users to seamlessly use multiple applications. This allows the system to create synergies between applications and enhance the potential of each application. For example, linking educational applications with entertainment applications can improve learning outcomes. Linking business applications with communication applications can also improve work efficiency.

[0029] The collaboration system according to the embodiment includes a reception unit, an estimation unit, and a completion unit. The reception unit receives user input. The user input includes, but is not limited to, text input, speech input, and gesture input. The reception unit receives speech input using, for example, speech recognition technology. The reception unit can also receive text input using keyboard input. The reception unit can also receive gesture input using gesture recognition technology. The estimation unit uses a generation AI to estimate interactions between applications based on the information received by the reception unit. For example, the generation AI estimates data sharing and functional collaboration between applications. The estimation unit can also estimate interactions between applications using a prediction algorithm. The estimation unit can also learn past data to improve the accuracy of interactions between applications. The completion unit uses the generation AI to automatically complete inputs to each application based on the interactions estimated by the estimation unit. For example, the generation AI analyzes user input and automatically generates inputs to each application. The completion unit also enables the generation AI to automatically generate input for other applications based on user operations. The completion unit also enables the generation AI to learn from past data to improve the accuracy of automatic completion of user input. This allows the collaboration system according to the embodiment to realize collaboration between multiple xR applications and enhance synergy between the applications.

[0030] The prediction unit can predict interactions between applications using a generative AI. Examples of generative AI include, but are not limited to, a transformer-based model. For example, the prediction unit predicts data sharing and functional collaboration between applications. The prediction unit can also predict interactions between applications using a predictive algorithm. The prediction unit can also learn from past data to improve the accuracy of the generative AI's prediction of interactions between applications. As a result, the use of the generative AI improves the accuracy of predictions of interactions between applications.

[0031] The completion unit can use a generation AI to analyze user input and automatically complete input for each application. Examples of the generation AI include, but are not limited to, a transformer-based model. For example, the completion unit uses the generation AI to analyze user input and automatically generate input for each application. The completion unit can also use the generation AI to automatically generate input for other applications based on user operations. The completion unit can also learn from past data to improve the accuracy of the generation AI's automatic completion of user input. This improves the accuracy of automatic completion of user input by using the generation AI.

[0032] When an application requires user location information, the estimation unit can use location information provided by other applications. Examples of location information include, but are not limited to, GPS data and Wi-Fi location information. For example, when an application requires user location information, the estimation unit can use location information provided by other applications. The estimation unit can also use a generation AI to estimate whether location information will be shared. This improves the accuracy of interactions by sharing location information between applications.

[0033] When a user performs a specific operation in a certain application, the completion unit can automatically generate input for another application based on the operation. Specific operations include, but are not limited to, for example, clicking a button or swiping. For example, when a user performs a specific operation in a certain application, the completion unit can automatically generate input for another application based on the operation. The completion unit can also automatically generate input for another application based on the user's operation using a generation AI. This enables seamless operation by automatically generating input for another application based on the user's operation.

[0034] The estimation unit can link educational applications with entertainment applications. Examples of educational applications include, but are not limited to, learning management systems and educational games. Examples of entertainment applications include, but are not limited to, game apps and video distribution apps. For example, the estimation unit links educational applications with entertainment applications. The estimation unit can also use a generation AI to estimate the link between educational applications and entertainment applications. This can improve learning effectiveness by linking educational applications with entertainment applications.

[0035] The complementing unit can link business applications and communication applications. Examples of business applications include, but are not limited to, project management tools and accounting software. Examples of communication applications include, but are not limited to, chat apps and video conferencing tools. The complementing unit, for example, links business applications and communication applications. The complementing unit can also use a generative AI to predict the link between business applications and communication applications. This allows business applications and communication applications to be linked, thereby improving business efficiency.

[0036] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The reception unit can also suggest similar input methods by referring to content that the user has input in the past. In this way, the optimal reception method can be provided by analyzing the user's past input history.

[0037] The reception unit can perform filtering based on the user's current task or area of ​​interest when receiving input. For example, the reception unit can receive only input related to the task the user is currently performing. The reception unit can also preferentially receive related input based on the user's area of ​​interest. The reception unit can also combine the user's current task and area of ​​interest to filter the most appropriate input. In this way, by filtering the input based on the user's current task and area of ​​interest, it is possible to receive more relevant input.

[0038] When receiving input, the reception unit can select the optimal reception means depending on the user's input method. For example, when the user uses voice input, the reception unit receives the input using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also preferentially receive keyboard input. Furthermore, when the user uses gesture input, the reception unit can also receive input using gesture recognition technology. This allows for smooth operation by selecting the optimal reception means depending on the user's input method.

[0039] When receiving input, the reception unit can prioritize receiving highly relevant input by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving input related to that location. Furthermore, when the user is moving, the reception unit can also suggest optimal input based on the user's current location. Furthermore, when the user is in a specific area, the reception unit can also prioritize receiving information related to that area. In this way, by taking into account the user's geographical location information, highly relevant input can be prioritized and received.

[0040] The reception unit can analyze the user's social media activity and receive related input when receiving input. For example, the reception unit preferentially receives input related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related input. The reception unit can also receive related input by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related input can be preferentially received.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit can suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially receive a specific input method based on the user's past feedback. The reception unit can also reflect the user's feedback and continuously improve the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0042] The inference unit can adjust the level of detail of the inference based on the importance of the application during inference. For example, the inference unit infers detailed interactions for highly important applications. The inference unit can also infer simple interactions for less important applications. The inference unit can also dynamically adjust the level of detail of the inference depending on the importance of the application. In this way, by adjusting the level of detail of the inference depending on the importance of the application, more appropriate interactions can be inferred.

[0043] The inference unit can apply different inference algorithms depending on the application category when making the inference. For example, in the case of an educational application, the inference unit infers interactions that enhance learning effects. In addition, in the case of an entertainment application, the inference unit can infer interactions that emphasize enjoyment. In addition, in the case of a business application, the inference unit can infer interactions that improve work efficiency. Thus, by applying different inference algorithms depending on the application category, more appropriate interactions can be inferred.

[0044] The inference unit can improve the accuracy of the inference by referring to the user's past inference results when making an inference. For example, the inference unit infers interactions with high accuracy based on the user's past inference results. The inference unit can also extract specific patterns from the user's past inference results and reflect them in the inference. The inference unit can also continuously learn the user's past inference results and improve the accuracy of the inference. In this way, the accuracy of the inference is improved by referring to the user's past inference results.

[0045] The estimation unit can determine the priority of estimation based on the submission time of the application when making estimation. For example, the estimation unit gives priority to an application that will be submitted soon. The estimation unit can also postpone an application that will be submitted further away. The estimation unit can also dynamically adjust the priority of estimation according to the submission time. This enables more appropriate estimation by determining the priority of estimation based on the submission time of the application.

[0046] The inference unit can adjust the order of inference based on the relevance of applications when making an inference. For example, the inference unit prioritizes inferring highly relevant applications. The inference unit can also postpone less relevant applications. The inference unit can also dynamically adjust the order of inference according to the relevance of applications. This allows for more appropriate inference by adjusting the order of inference based on the relevance of applications.

[0047] The inference unit can adjust the use of technical terms in the inference depending on the user's level of expertise when making an inference. For example, if the user has technical expertise, the inference unit uses a lot of technical terms. Also, if the user does not have technical expertise, the inference unit can avoid technical terms. Also, the inference unit can dynamically adjust the use of technical terms in the inference depending on the user's level of expertise. In this way, adjusting the use of technical terms in the inference depending on the user's level of expertise enables more appropriate inference.

[0048] The completion unit can select the optimal completion method by analyzing the user's past input behavior when completing the input. For example, the completion unit can propose the optimal completion method based on the user's past input behavior. The completion unit can also extract specific patterns from the user's past input behavior and reflect them in the completion. The completion unit can also continuously learn the user's past input behavior and improve the accuracy of the completion. In this way, the optimal completion method can be provided by analyzing the user's past input behavior.

[0049] The completion unit can customize the completion means based on the user's current task at the time of completion. For example, the completion unit performs completion related to the task the user is currently performing. The completion unit can also suggest the optimal completion means based on the user's current task. The completion unit can also customize the completion means by combining the user's current task and past input behavior. This allows for more appropriate completion by customizing the completion means based on the user's current task.

[0050] The completion unit can improve the completion method by reflecting user feedback during completion. For example, the completion unit continuously improves the completion method based on user feedback. The completion unit can also preferentially adopt a specific completion method based on user feedback. The completion unit can also customize the completion method by reflecting user feedback. In this way, the completion method can be continuously improved by reflecting user feedback.

[0051] The completion unit can select the optimal completion method by taking into account the user's geographical location information when completing the information. For example, if the user is in a specific location, the completion unit performs completion related to that location. Furthermore, if the user is moving, the completion unit can also suggest the optimal completion method based on the user's current location. Furthermore, if the user is in a specific region, the completion unit can also complete information related to that region. In this way, the optimal completion method can be provided by taking into account the user's geographical location information.

[0052] During completion, the completion unit can analyze the user's social media activity and suggest completion means. For example, the completion unit performs completion related to places where the user has checked in on social media. The completion unit can also analyze the content of the user's social media posts and suggest related completions. The completion unit can also perform related completions by referring to the activities of the user's friends on social media. In this way, it is possible to provide related completion means by analyzing the user's social media activity.

[0053] The completion unit can customize the completion method by reflecting the user's past feedback when completing a text. For example, the completion unit can suggest the optimal completion method based on the user's past feedback. The completion unit can also preferentially adopt a specific completion method based on the user's past feedback. The completion unit can also reflect the user's feedback and continuously improve the completion method. In this way, the optimal completion method can be provided by reflecting the user's past feedback.

[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] When accepting user input, the reception unit can monitor the user's current health condition and suggest the optimal input method. For example, if the user is tired, it can preferentially suggest voice input. Also, if the user is concentrating, it can suggest text input. Furthermore, if the user is in a situation where they cannot use their hands, it can suggest gesture input. This makes it possible to reduce the burden on the user by providing the optimal input method according to the user's health condition.

[0056] The prediction unit can analyze a user's past behavioral patterns and predict future behavior. For example, if a user has a habit of using a specific application every morning, the prediction unit can automatically launch that application. Also, if a user tends to perform a specific operation at a specific time of day, the prediction unit can prepare that operation in advance. Furthermore, if a user uses a specific application at a specific location, the prediction unit can automatically launch the application when the user arrives at that location. This makes it possible to predict future behavior based on the user's behavioral patterns and achieve seamless operations.

[0057] The prediction unit can analyze the user's current task and automatically suggest an application related to the task. For example, if the user is creating a document, a document editing application can be suggested. If the user is analyzing data, a data analysis application can be suggested. Furthermore, if the user is preparing a presentation, a presentation creation application can be suggested. This makes it possible to improve work efficiency by suggesting the optimal application according to the user's current task.

[0058] The prediction unit can analyze the user's social media activity and suggest related applications. For example, if the user is attending a specific event, applications related to the event can be suggested. Also, if the user posts about a specific topic, applications related to the topic can be suggested. Furthermore, if the user frequently interacts with a specific friend, applications related to the friend can be suggested. This can improve the user experience by suggesting optimal applications based on the user's social media activity.

[0059] The prediction unit can analyze the user's past feedback and improve the prediction accuracy of the application based on the feedback. For example, the prediction algorithm can be adjusted based on the feedback provided by the user in the past. It can also extract specific patterns from the user's feedback and reflect them in the prediction. Furthermore, it can continuously learn from the user's feedback and improve the prediction accuracy. By reflecting the user's past feedback, the prediction accuracy can be improved, making it possible to suggest more appropriate applications.

[0060] The prediction unit can suggest optimal applications taking into account the user's current geographic location information. For example, if the user is in a specific location, it can suggest applications related to that location. Also, if the user is moving, it can suggest optimal applications based on the user's current location. Furthermore, if the user is in a specific area, it can suggest applications that provide information related to that area. This can improve the user experience by suggesting optimal applications based on the user's geographic location information.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The accepting unit accepts user input. User input includes text input, voice input, gesture input, etc. For example, voice input can be accepted using voice recognition technology, text input can be accepted using keyboard input, and gesture input can be accepted using gesture recognition technology. Step 2: The prediction unit uses the generation AI to predict interactions between applications based on the information received by the reception unit. The generation AI can predict data sharing and functional collaboration between applications and predict interactions using a predictive algorithm. It can also improve the accuracy of interactions by learning from past data. Step 3: The completion unit uses the generation AI to automatically complete inputs for each application based on the interactions inferred by the prediction unit. The generation AI analyzes the user's input and automatically generates inputs for each application. It can also automatically generate inputs for other applications based on the user's operations. Furthermore, it can improve the accuracy of auto-completion by learning from past data.

[0063] (Example 2) A system according to an embodiment of the present invention integrates multiple xR applications. This system uses a generation AI to predict interactions between applications and automatically complete user inputs for each application, enabling the integration of multiple applications. For example, a user launches multiple xR applications. The generation AI then analyzes the behavior of each application and predicts interactions between the applications. For example, if one application requires the user's location information, it can use location information provided by other applications. In this way, the generation AI predicts interactions between applications and automatically completes the necessary information. Furthermore, the generation AI analyzes user inputs and automatically completes inputs for each application. For example, when a user performs a specific operation in one application, inputs for other applications are automatically generated based on that operation. This allows users to seamlessly use multiple applications. This allows the system to create synergies between applications and enhance the potential of each application. For example, linking educational applications with entertainment applications can improve learning outcomes. Linking business applications with communication applications can also improve work efficiency.

[0064] The collaboration system according to the embodiment includes a reception unit, an estimation unit, and a completion unit. The reception unit receives user input. The user input includes, but is not limited to, text input, speech input, and gesture input. The reception unit receives speech input using, for example, speech recognition technology. The reception unit can also receive text input using keyboard input. The reception unit can also receive gesture input using gesture recognition technology. The estimation unit uses a generation AI to estimate interactions between applications based on the information received by the reception unit. For example, the generation AI estimates data sharing and functional collaboration between applications. The estimation unit can also estimate interactions between applications using a prediction algorithm. The estimation unit can also learn past data to improve the accuracy of interactions between applications. The completion unit uses the generation AI to automatically complete inputs to each application based on the interactions estimated by the estimation unit. For example, the generation AI analyzes user input and automatically generates inputs to each application. The completion unit also enables the generation AI to automatically generate input for other applications based on user operations. The completion unit also enables the generation AI to learn from past data to improve the accuracy of automatic completion of user input. This allows the collaboration system according to the embodiment to realize collaboration between multiple xR applications and enhance synergy between the applications.

[0065] The prediction unit can predict interactions between applications using a generative AI. Examples of generative AI include, but are not limited to, a transformer-based model. For example, the prediction unit predicts data sharing and functional collaboration between applications. The prediction unit can also predict interactions between applications using a predictive algorithm. The prediction unit can also learn from past data to improve the accuracy of the generative AI's prediction of interactions between applications. As a result, the use of the generative AI improves the accuracy of predictions of interactions between applications.

[0066] The completion unit can use a generation AI to analyze user input and automatically complete input for each application. Examples of the generation AI include, but are not limited to, a transformer-based model. For example, the completion unit uses the generation AI to analyze user input and automatically generate input for each application. The completion unit can also use the generation AI to automatically generate input for other applications based on user operations. The completion unit can also learn from past data to improve the accuracy of the generation AI's automatic completion of user input. This improves the accuracy of automatic completion of user input by using the generation AI.

[0067] When an application requires user location information, the estimation unit can use location information provided by other applications. Examples of location information include, but are not limited to, GPS data and Wi-Fi location information. For example, when an application requires user location information, the estimation unit can use location information provided by other applications. The estimation unit can also use a generation AI to estimate whether location information will be shared. This improves the accuracy of interactions by sharing location information between applications.

[0068] When a user performs a specific operation in a certain application, the completion unit can automatically generate input for another application based on the operation. Specific operations include, but are not limited to, for example, clicking a button or swiping. For example, when a user performs a specific operation in a certain application, the completion unit can automatically generate input for another application based on the operation. The completion unit can also automatically generate input for another application based on the user's operation using a generation AI. This enables seamless operation by automatically generating input for another application based on the user's operation.

[0069] The estimation unit can link educational applications with entertainment applications. Examples of educational applications include, but are not limited to, learning management systems and educational games. Examples of entertainment applications include, but are not limited to, game apps and video distribution apps. For example, the estimation unit links educational applications with entertainment applications. The estimation unit can also use a generation AI to estimate the link between educational applications and entertainment applications. This can improve learning effectiveness by linking educational applications with entertainment applications.

[0070] The complementing unit can link business applications and communication applications. Examples of business applications include, but are not limited to, project management tools and accounting software. Examples of communication applications include, but are not limited to, chat apps and video conferencing tools. The complementing unit, for example, links business applications and communication applications. The complementing unit can also use a generative AI to predict the link between business applications and communication applications. This allows business applications and communication applications to be linked, thereby improving business efficiency.

[0071] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can delay the timing of input reception to provide a relaxing environment. Furthermore, if the user is relaxed, the reception unit can accelerate the timing of input reception to promote smooth operation. Furthermore, if the user is in a hurry, the reception unit can immediately accelerate the timing of input reception to enable quick operation. This allows for more appropriate input reception by adjusting the timing of input reception according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The reception unit can also suggest similar input methods by referring to content that the user has input in the past. In this way, the optimal reception method can be provided by analyzing the user's past input history.

[0073] The reception unit can perform filtering based on the user's current task or area of ​​interest when receiving input. For example, the reception unit can receive only input related to the task the user is currently performing. The reception unit can also preferentially receive related input based on the user's area of ​​interest. The reception unit can also combine the user's current task and area of ​​interest to filter the most appropriate input. In this way, by filtering the input based on the user's current task and area of ​​interest, it is possible to receive more relevant input.

[0074] When receiving input, the reception unit can select the optimal reception means depending on the user's input method. For example, when the user uses voice input, the reception unit receives the input using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also preferentially receive keyboard input. Furthermore, when the user uses gesture input, the reception unit can also receive input using gesture recognition technology. This allows for smooth operation by selecting the optimal reception means depending on the user's input method.

[0075] The reception unit can estimate the user's emotion and determine the priority of inputs to be received based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit postpones inputs of lower importance. Furthermore, if the user is relaxed, the reception unit can also prioritize receiving inputs of higher importance. Furthermore, if the user is in a hurry, the reception unit can also prioritize receiving inputs of higher urgency. This enables more appropriate input reception by determining the priority of inputs according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] When receiving input, the reception unit can prioritize receiving highly relevant input by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving input related to that location. Furthermore, when the user is moving, the reception unit can also suggest optimal input based on the user's current location. Furthermore, when the user is in a specific area, the reception unit can also prioritize receiving information related to that area. In this way, by taking into account the user's geographical location information, highly relevant input can be prioritized and received.

[0077] The reception unit can analyze the user's social media activity and receive related input when receiving input. For example, the reception unit preferentially receives input related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related input. The reception unit can also receive related input by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related input can be preferentially received.

[0078] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit can suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially receive a specific input method based on the user's past feedback. The reception unit can also reflect the user's feedback and continuously improve the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0079] The estimation unit can estimate the user's emotion and adjust the interaction estimation method based on the estimated user emotion. For example, if the user is relaxed, the estimation unit can estimate a detailed interaction. If the user is in a hurry, the estimation unit can also estimate a brief interaction. If the user is excited, the estimation unit can also estimate a visually stimulating interaction. This allows for more appropriate interaction estimation by adjusting the interaction estimation method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0080] The inference unit can adjust the level of detail of the inference based on the importance of the application during inference. For example, the inference unit infers detailed interactions for highly important applications. The inference unit can also infer simple interactions for less important applications. The inference unit can also dynamically adjust the level of detail of the inference depending on the importance of the application. In this way, by adjusting the level of detail of the inference depending on the importance of the application, more appropriate interactions can be inferred.

[0081] The inference unit can apply different inference algorithms depending on the application category when making the inference. For example, in the case of an educational application, the inference unit infers interactions that enhance learning effects. In addition, in the case of an entertainment application, the inference unit can infer interactions that emphasize enjoyment. In addition, in the case of a business application, the inference unit can infer interactions that improve work efficiency. Thus, by applying different inference algorithms depending on the application category, more appropriate interactions can be inferred.

[0082] The inference unit can improve the accuracy of the inference by referring to the user's past inference results when making an inference. For example, the inference unit infers interactions with high accuracy based on the user's past inference results. The inference unit can also extract specific patterns from the user's past inference results and reflect them in the inference. The inference unit can also continuously learn the user's past inference results and improve the accuracy of the inference. In this way, the accuracy of the inference is improved by referring to the user's past inference results.

[0083] The estimation unit can estimate the user's emotions and determine the priority of estimations based on the estimated user emotions. For example, if the user is feeling stressed, the estimation unit postpones less important estimations. Furthermore, if the user is relaxed, the estimation unit can prioritize more important estimations. Furthermore, if the user is in a hurry, the estimation unit can prioritize more urgent estimations. This enables more appropriate estimations by determining the priority of estimations according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The estimation unit can determine the priority of estimation based on the submission time of the application when making estimation. For example, the estimation unit gives priority to an application that will be submitted soon. The estimation unit can also postpone an application that will be submitted further away. The estimation unit can also dynamically adjust the priority of estimation according to the submission time. This enables more appropriate estimation by determining the priority of estimation based on the submission time of the application.

[0085] The inference unit can adjust the order of inference based on the relevance of applications when making an inference. For example, the inference unit prioritizes inferring highly relevant applications. The inference unit can also postpone less relevant applications. The inference unit can also dynamically adjust the order of inference according to the relevance of applications. This allows for more appropriate inference by adjusting the order of inference based on the relevance of applications.

[0086] The inference unit can adjust the use of technical terms in the inference depending on the user's level of expertise when making an inference. For example, if the user has technical expertise, the inference unit uses a lot of technical terms. Also, if the user does not have technical expertise, the inference unit can avoid technical terms. Also, the inference unit can dynamically adjust the use of technical terms in the inference depending on the user's level of expertise. In this way, adjusting the use of technical terms in the inference depending on the user's level of expertise enables more appropriate inference.

[0087] The completion unit can estimate the user's emotion and adjust the completion method based on the estimated user's emotion. For example, if the user is relaxed, the completion unit can perform detailed completion. If the user is in a hurry, the completion unit can also perform concise completion. If the user is excited, the completion unit can also perform visually stimulating completion. This allows for more appropriate completion by adjusting the completion method according to the user's emotion. 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.

[0088] The completion unit can select the optimal completion method by analyzing the user's past input behavior when completing the input. For example, the completion unit can propose the optimal completion method based on the user's past input behavior. The completion unit can also extract specific patterns from the user's past input behavior and reflect them in the completion. The completion unit can also continuously learn the user's past input behavior and improve the accuracy of the completion. In this way, the optimal completion method can be provided by analyzing the user's past input behavior.

[0089] The completion unit can customize the completion means based on the user's current task at the time of completion. For example, the completion unit performs completion related to the task the user is currently performing. The completion unit can also suggest the optimal completion means based on the user's current task. The completion unit can also customize the completion means by combining the user's current task and past input behavior. This allows for more appropriate completion by customizing the completion means based on the user's current task.

[0090] The completion unit can improve the completion method by reflecting user feedback during completion. For example, the completion unit continuously improves the completion method based on user feedback. The completion unit can also preferentially adopt a specific completion method based on user feedback. The completion unit can also customize the completion method by reflecting user feedback. In this way, the completion method can be continuously improved by reflecting user feedback.

[0091] The completion unit can estimate the user's emotions and determine the priority of completion based on the estimated user's emotions. For example, if the user is feeling stressed, the completion unit can postpone completion of less important importance. Furthermore, if the user is relaxed, the completion unit can prioritize completion of more important importance. Furthermore, if the user is in a hurry, the completion unit can prioritize completion of more urgent importance. This enables more appropriate completion by determining the priority of completion according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The completion unit can select the optimal completion method by taking into account the user's geographical location information when completing the information. For example, if the user is in a specific location, the completion unit performs completion related to that location. Furthermore, if the user is moving, the completion unit can also suggest the optimal completion method based on the user's current location. Furthermore, if the user is in a specific region, the completion unit can also complete information related to that region. In this way, the optimal completion method can be provided by taking into account the user's geographical location information.

[0093] During completion, the completion unit can analyze the user's social media activity and suggest completion means. For example, the completion unit performs completion related to places where the user has checked in on social media. The completion unit can also analyze the content of the user's social media posts and suggest related completions. The completion unit can also perform related completions by referring to the activities of the user's friends on social media. In this way, it is possible to provide related completion means by analyzing the user's social media activity.

[0094] The completion unit can customize the completion method by reflecting the user's past feedback when completing a text. For example, the completion unit can suggest the optimal completion method based on the user's past feedback. The completion unit can also preferentially adopt a specific completion method based on the user's past feedback. The completion unit can also reflect the user's feedback and continuously improve the completion method. In this way, the optimal completion method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, estimation unit, and completion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives user input using a touch panel 38A or a microphone 38B. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates interactions between applications using a generative AI. The completion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically completes input to each application based on the interactions estimated by the estimation unit. The reception unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the estimation unit and completion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, estimation unit, and completion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 or the camera 42 of the smart glasses 214. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates interactions between applications using a generative AI. The completion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically completes input to each application based on the interactions estimated by the estimation unit. The reception unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the estimation unit and completion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, estimation unit, and completion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 or the camera 42 of the headset-type terminal 314. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates interactions between applications using a generative AI. The completion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically completes input to each application based on the interactions estimated by the estimation unit. The reception unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the estimation unit and completion unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, estimation unit, and completion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 or camera 42 of the robot 414. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates interactions between applications using a generative AI. The completion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically completes input to each application based on the interactions estimated by the estimation unit. The reception unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the estimation unit and completion unit may be realized, for example, by the control unit 46A of the robot 414.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] When accepting user input, the reception unit can monitor the user's current health condition and suggest the optimal input method. For example, if the user is tired, it can preferentially suggest voice input. Also, if the user is concentrating, it can suggest text input. Furthermore, if the user is in a situation where they cannot use their hands, it can suggest gesture input. This makes it possible to reduce the burden on the user by providing the optimal input method according to the user's health condition.

[0097] The prediction unit can analyze a user's past behavioral patterns and predict future behavior. For example, if a user has a habit of using a specific application every morning, the prediction unit can automatically launch that application. Also, if a user tends to perform a specific operation at a specific time of day, the prediction unit can prepare that operation in advance. Furthermore, if a user uses a specific application at a specific location, the prediction unit can automatically launch the application when the user arrives at that location. This makes it possible to predict future behavior based on the user's behavioral patterns and achieve seamless operations.

[0098] The completion unit can estimate the user's emotions and dynamically change the application interface based on the estimated emotions. For example, if the user is feeling stressed, the interface can be simplified to make the operation easier. If the user is feeling relaxed, the interface can be made more detailed to provide more information. Furthermore, if the user is excited, the interface can be made more visually stimulating. This makes it possible to improve the user experience by providing an optimal interface according to the user's emotions.

[0099] The prediction unit can analyze the user's current task and automatically suggest an application related to the task. For example, if the user is creating a document, a document editing application can be suggested. If the user is analyzing data, a data analysis application can be suggested. Furthermore, if the user is preparing a presentation, a presentation creation application can be suggested. This makes it possible to improve work efficiency by suggesting the optimal application according to the user's current task.

[0100] The complementing unit can estimate the user's emotions and adjust the frequency of notifications based on the estimated emotions. For example, if the user is feeling stressed, the frequency of notifications can be reduced to avoid disrupting concentration. Also, if the user is relaxed, the frequency of notifications can be increased to proactively provide information. Furthermore, if the user is in a hurry, only important notifications can be displayed preferentially. This can improve the user experience by providing an optimal notification frequency according to the user's emotions.

[0101] The prediction unit can analyze the user's social media activity and suggest related applications. For example, if the user is attending a specific event, applications related to the event can be suggested. Also, if the user posts about a specific topic, applications related to the topic can be suggested. Furthermore, if the user frequently interacts with a specific friend, applications related to the friend can be suggested. This can improve the user experience by suggesting optimal applications based on the user's social media activity.

[0102] The complementing unit can estimate the user's emotion and adjust the application's operation speed based on the estimated emotion. For example, if the user is feeling stressed, the application's operation speed can be increased to enable quick operation. Alternatively, if the user is relaxed, the application's operation speed can be returned to normal to provide stable operation. Furthermore, if the user is in a hurry, the application's operation speed can be maximized to enable quick response. This can improve the user experience by providing an optimal application operation speed according to the user's emotion.

[0103] The prediction unit can analyze the user's past feedback and improve the prediction accuracy of the application based on the feedback. For example, the prediction algorithm can be adjusted based on the feedback provided by the user in the past. It can also extract specific patterns from the user's feedback and reflect them in the prediction. Furthermore, it can continuously learn from the user's feedback and improve the prediction accuracy. By reflecting the user's past feedback, the prediction accuracy can be improved, making it possible to suggest more appropriate applications.

[0104] The completion unit can estimate the user's emotion and customize the application based on the estimated emotion. For example, if the user is feeling stressed, the color tone of the application can be changed to a calmer tone. If the user is feeling relaxed, the color tone of the application can be changed to a brighter tone. Furthermore, if the user is excited, the color tone of the application can be changed to a more visually stimulating tone. This makes it possible to provide optimal application customization according to the user's emotion, thereby improving the user experience.

[0105] The prediction unit can suggest optimal applications taking into account the user's current geographic location information. For example, if the user is in a specific location, it can suggest applications related to that location. Also, if the user is moving, it can suggest optimal applications based on the user's current location. Furthermore, if the user is in a specific area, it can suggest applications that provide information related to that area. This can improve the user experience by suggesting optimal applications based on the user's geographic location information.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The accepting unit accepts user input. User input includes text input, voice input, gesture input, etc. For example, voice input can be accepted using voice recognition technology, text input can be accepted using keyboard input, and gesture input can be accepted using gesture recognition technology. Step 2: The prediction unit uses the generation AI to predict interactions between applications based on the information received by the reception unit. The generation AI can predict data sharing and functional collaboration between applications and predict interactions using a predictive algorithm. It can also improve the accuracy of interactions by learning from past data. Step 3: The completion unit uses the generation AI to automatically complete inputs for each application based on the interactions inferred by the prediction unit. The generation AI analyzes the user's input and automatically generates inputs for each application. It can also automatically generate inputs for other applications based on the user's operations. Furthermore, it can improve the accuracy of auto-completion by learning from past data.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 AI 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 AI 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 AI 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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, in order to avoid confusion and to 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; an inference unit that infers interactions between applications based on the information received by the reception unit; a completion unit that automatically completes input to each application based on the interaction inferred by the inference unit; Equipped with A system characterized by:

2. The estimation unit Using generative AI to predict interactions between applications 2. The system of claim 1.

3. The complementing unit Analyze user input using generative AI and auto-complete input for each application 2. The system of claim 1.

4. The estimation unit When an application needs the user's location information, it uses the location information provided by other applications.

2. The system of claim 1.

5. The complementing unit Automatically generate input for other applications based on a user's actions in one application 2. The system of claim 1.

6. The estimation unit Connecting educational and entertainment applications 2. The system of claim 1.

7. The complementing unit Connect your business and communication applications 2. The system of claim 1.

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

2. The system of claim 1.

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