System

Abstract

An object of the system according to the embodiment is to enable even a user who is not familiar with technology to easily develop an IoT service.SOLUTION: A system according to an embodiment includes a reception unit, a selection unit, a generation unit, a provision unit, and a marketplace provision unit. The reception unit receives an input of service contents from a user. The selection unit analyzes the service content received by the reception unit and selects an appropriate sensor. The generation unit automatically generates a code corresponding to the sensor selected by the selection unit. The providing unit provides the code generated by the generating unit to the user. The marketplace provider provides applications or products created by other users.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] Conventional technologies have the drawback that it is difficult for non-technical users to develop IoT services.

[0005] The system according to the embodiment aims to enable even users who are not familiar with technology to easily develop IoT services. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a selection unit, a generation unit, a provision unit, and a marketplace provision unit. The reception unit receives input of service content from a user. The selection unit analyzes the service content received by the reception unit and selects an appropriate sensor. The generation unit automatically generates code corresponding to the sensor selected by the selection unit. The provision unit provides the user with the code generated by the generation unit. The marketplace provision unit provides applications or products created by other users. [Effects of the Invention]

[0007] The system according to the embodiment enables even users who are not familiar with technology to easily develop IoT services. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An IoT service development system according to an embodiment of the present invention allows even non-technical users to easily develop IoT services. When a user enters "I want to create an IoT service for XX" on the website, a generation AI analyzes the input, automatically selects the optimal sensor, and automatically generates the necessary code. Users can easily launch an IoT service by simply purchasing a general-purpose IoT sensor from the website. Applications, products, and services using general-purpose IoT sensors and the generation AI created by other users can also be purchased on the marketplace. For example, if a user enters "I want to automate home temperature control" on the website, the generation AI analyzes the information, selects a temperature sensor, collects sensor data, and automatically generates code that triggers actions under specific conditions. Users can easily launch a temperature control IoT service using the generated code. They can also purchase temperature control applications created by other users on the marketplace and incorporate them into their own services. This allows even non-technical users to easily develop IoT services and provide more advanced services by leveraging other users' technologies and ideas.

[0029] An IoT service development system according to an embodiment includes a reception unit, a selection unit, a generation unit, a provision unit, and a marketplace provision unit. The reception unit receives input of service content from a user. For example, the user may input, "I want to automate home temperature control." The selection unit analyzes the service content received by the reception unit and selects an appropriate sensor. For example, the selection unit determines that a temperature sensor is necessary for temperature control. The generation unit automatically generates code corresponding to the sensor selected by the selection unit. For example, the generation unit collects data from the temperature sensor and generates code that triggers an action under specific conditions. The provision unit provides the code generated by the generation unit to the user. For example, the generated code is sent to the user by email. The marketplace provision unit provides applications and products created by other users. For example, a temperature control application created by another user can be purchased on the marketplace. This allows the IoT service development system according to an embodiment to perform a consistent process from user input of service content to selection of an optimal sensor, automatic code generation, and provision.

[0030] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit can automatically display service contents that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest service contents that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0031] When service content is input, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit can preferentially display service content related to the user's current project. The reception unit can also suggest related service content based on the user's area of ​​interest. The reception unit can also filter optimal service content by referring to the user's past project history. This makes it possible to suggest optimal service content based on the user's current project or area of ​​interest.

[0032] When inputting service content, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the service content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also input the service content using image analysis technology. This makes it possible to provide the optimal input means depending on the user's input method.

[0033] When selecting a sensor, the selection unit can select an appropriate sensor by referring to the user's past selection history. For example, the selection unit can suggest the optimal sensor based on sensors previously selected by the user. The selection unit can also analyze the user's past selection history and select the most appropriate sensor. The selection unit can also select a sensor that is suitable for specific conditions from the user's past selection history. This makes it possible to select the optimal sensor based on the user's past selection history.

[0034] When selecting sensors, the selection unit can filter the selection based on the user's current project or area of ​​interest. For example, the selection unit preferentially selects sensors related to the user's current project. The selection unit can also suggest related sensors based on the user's area of ​​interest. The selection unit can also select the optimal sensor by referring to the user's past project history. This makes it possible to select the optimal sensor based on the user's current project or area of ​​interest.

[0035] When selecting a sensor, the selection unit can select an appropriate selection means depending on the user's input method. For example, if the user selects voice input, the selection unit selects a sensor using voice recognition technology. If the user selects text input, the selection unit can also provide an interface that supports keyboard input. If the user selects image input, the selection unit can also select a sensor using image analysis technology. This makes it possible to provide the optimal sensor selection means depending on the user's input method.

[0036] The generator can appropriately adjust the level of detail of the code based on the importance of the sensor when generating the code. For example, the generator generates detailed code for an important sensor. The generator can also generate simple code for a general sensor. The generator can also dynamically adjust the level of detail of the code according to the importance of the sensor. This makes it possible to provide the optimal level of detail of the code according to the importance of the sensor.

[0037] The generator can apply different generation algorithms depending on the sensor category when generating code. For example, the generator can apply a specific algorithm for processing temperature data to a temperature sensor. The generator can also apply a specific algorithm for processing humidity data to a humidity sensor. The generator can also select the optimal generation algorithm depending on the category of each sensor. This makes it possible to provide the optimal generation algorithm depending on the sensor category.

[0038] When generating code, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. For example, the generation unit generates optimal code based on code generated by the user in the past. The generation unit can also analyze the user's past generation results and optimize the generation algorithm. The generation unit can also improve the accuracy of the generation by referring to the user's past generation history. This makes it possible to improve the accuracy of the generation based on the user's past generation results.

[0039] When providing a code, the providing unit can select an appropriate providing method by referring to the user's past providing history. For example, the providing unit can suggest an optimal providing method based on the providing methods used by the user in the past. The providing unit can also analyze the user's past providing history and select the most appropriate providing method. The providing unit can also select a providing method that is suitable for specific conditions from the user's past providing history. This makes it possible to select the optimal providing method based on the user's past providing history.

[0040] When providing code, the providing unit can filter the provided content based on the user's current project or area of ​​interest. For example, the providing unit can preferentially provide code related to the user's current project. The providing unit can also suggest related code based on the user's area of ​​interest. The providing unit can also provide optimal code by referring to the user's past project history. This makes it possible to provide optimal code based on the user's current project or area of ​​interest.

[0041] When providing a code, the providing unit can select an appropriate providing means depending on the user's input method. For example, if the user selects voice input, the providing unit provides the code using voice recognition technology. Furthermore, if the user selects text input, the providing unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the providing unit can also provide the code using image analysis technology. This makes it possible to provide the optimal code providing means depending on the user's input method.

[0042] When providing a marketplace, the marketplace providing unit can select an appropriate provision method by referring to the user's past purchase history. The marketplace providing unit, for example, suggests optimal products based on products the user has previously purchased. The marketplace providing unit can also analyze the user's past purchase history and select the most appropriate product. The marketplace providing unit can also select products that meet specific conditions from the user's past purchase history. This makes it possible to select the optimal provision method based on the user's past purchase history.

[0043] When providing the marketplace, the marketplace providing unit can filter the offerings based on the user's current project or area of ​​interest. For example, the marketplace providing unit can preferentially provide products related to the user's ongoing project. The marketplace providing unit can also suggest related products based on the user's area of ​​interest. The marketplace providing unit can also provide optimal products by referring to the user's past project history. This makes it possible to provide optimal products based on the user's current project or area of ​​interest.

[0044] When providing a marketplace, the marketplace providing unit can select an appropriate provision means depending on the user's input method. For example, if the user selects voice input, the marketplace providing unit can provide products using voice recognition technology. Furthermore, if the user selects text input, the marketplace providing unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the marketplace providing unit can also provide products using image analysis technology. This makes it possible to provide the optimal product provision means depending on the user's input method.

[0045] When providing the marketplace, the marketplace providing unit can prioritize providing relevant products by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the marketplace providing unit can prioritize providing products related to that area. The marketplace providing unit can also suggest optimal products based on the user's current location. The marketplace providing unit can also provide relevant products by referring to the user's geographical location information. This makes it possible to provide optimal products based on the user's geographical location information.

[0046] When providing the marketplace, the marketplace providing unit can analyze the user's social media activity and provide related products. The marketplace providing unit can suggest related products based on, for example, information shared by the user on social media. The marketplace providing unit can also analyze the user's social media activity history and suggest optimal products. The marketplace providing unit can also provide related products by taking into account the activity of the user's friends on social media. This makes it possible to provide optimal products based on the user's social media activity.

[0047] The marketplace providing unit can customize an appropriate provision method by reflecting past user feedback when providing a marketplace. The marketplace providing unit, for example, proposes an optimal provision method based on feedback provided by the user in the past. The marketplace providing unit can also customize a provision interface by referring to past user feedback. The marketplace providing unit can also optimize a provision procedure by reflecting user feedback. This makes it possible to customize an optimal provision method based on past user feedback.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The reception unit can use natural language processing technology to analyze the user's input and more accurately understand the user's intent. For example, if a user inputs "I want to automate home temperature control," the reception unit can understand the context, extract related keywords, and make more specific suggestions. The reception unit can also provide real-time feedback on the user's input and encourage corrections or additions to the input. Furthermore, the reception unit can compare the user's input with that of other users and match users with common needs. This allows users to collaborate with each other to develop more effective IoT services.

[0050] The reception unit can analyze the user's past input history and learn the user's preferences and tendencies. For example, if the user has frequently selected a particular type of sensor in the past, the reception unit will preferentially suggest that sensor. Also, if the user has preferred a particular input method (voice, text, etc.) in the past, the reception unit can set that input method as the default. Furthermore, the reception unit can predict and suggest the service content to be used during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0051] The reception unit can provide relevant information and resources based on the user's current project and areas of interest. For example, the reception unit can provide technical documents and tutorials related to the user's current project. The reception unit can also suggest related news and the latest technology trends based on the user's areas of interest. Furthermore, the reception unit can filter the most appropriate resources by referring to the user's past project history. This makes it possible to provide the most appropriate information and resources based on the user's current project and areas of interest.

[0052] The reception unit can not only select an appropriate input means according to the user's input method, but also provide auxiliary functions to improve input accuracy. For example, when voice input is selected, not only can the input content be converted into text using voice recognition technology, but also a confirmation prompt can be displayed to prevent misrecognition. When text input is selected, an automatic completion function for the input content can be provided to improve input efficiency. Furthermore, when image input is selected, the input content can be analyzed using image analysis technology and related information can be automatically completed. This makes it possible to provide the optimal input means and auxiliary functions according to the user's input method.

[0053] The selection unit can suggest the optimal sensor not only by referring to the user's past selection history but also by comparing it with the selection history of other users. For example, the selection unit can suggest sensors to the user by referring to sensors selected by other users with similar needs. The selection unit can also analyze the user's past selection history to select sensors suitable for specific conditions. Furthermore, the selection unit can use data obtained from the user's selection history to optimize the selection algorithm and make more accurate suggestions. This allows the optimal sensor to be selected based on the user's past selection history and the selection history of other users.

[0054] The selection unit can suggest not only sensor selection but also related accessories and additional functions based on the user's current project and area of ​​interest. For example, if the user is working on a temperature control project, the selection unit can suggest not only temperature sensors but also humidity sensors and air quality sensors. The selection unit can also suggest related software and applications based on the user's area of ​​interest. Furthermore, the selection unit can suggest optimal accessories and additional functions by referring to the user's past project history. This makes it possible to provide optimal sensors and related resources based on the user's current project and area of ​​interest.

[0055] The selection unit can not only select a sensor selection means according to the user's input method, but also provide an interactive tutorial to guide the user through the selection process. For example, if voice input is selected, a voice guide can be used to explain the sensor selection procedure. If text input is selected, a step-by-step guide can be displayed to help the user proceed through the selection process without hesitation. Furthermore, if image input is selected, a visual guide can be provided to guide the user through the selection process in a way that is visually easy for the user to understand. This makes it possible to provide the optimal selection means and guide according to the user's input method.

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

[0057] Step 1: The reception unit receives input of service content from the user. For example, the user may input "I want to automate temperature control in my home." Step 2: The selection unit analyzes the service content received by the reception unit and selects an appropriate sensor. For example, it determines that a temperature sensor is necessary for temperature control. Step 3: The generator automatically generates code corresponding to the sensors selected by the selector. For example, it collects data from a temperature sensor and generates code that takes action under specific conditions. Step 4: The providing unit provides the code generated by the generating unit to the user, for example, by sending the generated code to the user by email. Step 5: The marketplace provider provides applications and products created by other users. For example, a temperature control application created by another user can be purchased on the marketplace.

[0058] (Example 2) An IoT service development system according to an embodiment of the present invention allows even non-technical users to easily develop IoT services. When a user enters "I want to create an IoT service for XX" on the website, a generation AI analyzes the input, automatically selects the optimal sensor, and automatically generates the necessary code. Users can easily launch an IoT service by simply purchasing a general-purpose IoT sensor from the website. Applications, products, and services using general-purpose IoT sensors and the generation AI created by other users can also be purchased on the marketplace. For example, if a user enters "I want to automate home temperature control" on the website, the generation AI analyzes the information, selects a temperature sensor, collects sensor data, and automatically generates code that triggers actions under specific conditions. Users can easily launch a temperature control IoT service using the generated code. They can also purchase temperature control applications created by other users on the marketplace and incorporate them into their own services. This allows even non-technical users to easily develop IoT services and provide more advanced services by leveraging other users' technologies and ideas.

[0059] An IoT service development system according to an embodiment includes a reception unit, a selection unit, a generation unit, a provision unit, and a marketplace provision unit. The reception unit receives input of service content from a user. For example, the user may input, "I want to automate home temperature control." The selection unit analyzes the service content received by the reception unit and selects an appropriate sensor. For example, the selection unit determines that a temperature sensor is necessary for temperature control. The generation unit automatically generates code corresponding to the sensor selected by the selection unit. For example, the generation unit collects data from the temperature sensor and generates code that triggers an action under specific conditions. The provision unit provides the code generated by the generation unit to the user. For example, the generated code is sent to the user by email. The marketplace provision unit provides applications and products created by other users. For example, a temperature control application created by another user can be purchased on the marketplace. This allows the IoT service development system according to an embodiment to perform a consistent process from user input of service content to selection of an optimal sensor, automatic code generation, and provision.

[0060] The reception unit can estimate the user's emotions and adjust the input interface for the service content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the service content. This makes it possible to provide an optimal input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0061] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit can automatically display service contents that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest service contents that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0062] When service content is input, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit can preferentially display service content related to the user's current project. The reception unit can also suggest related service content based on the user's area of ​​interest. The reception unit can also filter optimal service content by referring to the user's past project history. This makes it possible to suggest optimal service content based on the user's current project or area of ​​interest.

[0063] When inputting service content, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the service content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also input the service content using image analysis technology. This makes it possible to provide the optimal input means depending on the user's input method.

[0064] The selection unit can estimate the user's emotions and adjust the sensor selection criteria based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can provide simple and intuitive sensor selection criteria. If the user is relaxed, the selection unit can also provide detailed sensor selection criteria. If the user is in a hurry, the selection unit can also provide sensor selection criteria that allows for quick selection. This makes it possible to provide optimal sensor selection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] When selecting a sensor, the selection unit can select an appropriate sensor by referring to the user's past selection history. For example, the selection unit can suggest the optimal sensor based on sensors previously selected by the user. The selection unit can also analyze the user's past selection history and select the most appropriate sensor. The selection unit can also select a sensor that is suitable for specific conditions from the user's past selection history. This makes it possible to select the optimal sensor based on the user's past selection history.

[0066] When selecting sensors, the selection unit can filter the selection based on the user's current project or area of ​​interest. For example, the selection unit preferentially selects sensors related to the user's current project. The selection unit can also suggest related sensors based on the user's area of ​​interest. The selection unit can also select the optimal sensor by referring to the user's past project history. This makes it possible to select the optimal sensor based on the user's current project or area of ​​interest.

[0067] When selecting a sensor, the selection unit can select an appropriate selection means depending on the user's input method. For example, if the user selects voice input, the selection unit selects a sensor using voice recognition technology. If the user selects text input, the selection unit can also provide an interface that supports keyboard input. If the user selects image input, the selection unit can also select a sensor using image analysis technology. This makes it possible to provide the optimal sensor selection means depending on the user's input method.

[0068] The generation unit can estimate the user's emotions and adjust the code generation expression method based on the estimated user emotions. For example, if the user is stressed, the generation unit can provide a simple and intuitive code generation method. If the user is relaxed, the generation unit can also provide a detailed code generation method. If the user is in a hurry, the generation unit can also provide a code generation method that can be quickly generated. This makes it possible to provide an optimal code generation expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] The generator can appropriately adjust the level of detail of the code based on the importance of the sensor when generating the code. For example, the generator generates detailed code for an important sensor. The generator can also generate simple code for a general sensor. The generator can also dynamically adjust the level of detail of the code according to the importance of the sensor. This makes it possible to provide the optimal level of detail of the code according to the importance of the sensor.

[0070] The generator can apply different generation algorithms depending on the sensor category when generating code. For example, the generator can apply a specific algorithm for processing temperature data to a temperature sensor. The generator can also apply a specific algorithm for processing humidity data to a humidity sensor. The generator can also select the optimal generation algorithm depending on the category of each sensor. This makes it possible to provide the optimal generation algorithm depending on the sensor category.

[0071] When generating code, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. For example, the generation unit generates optimal code based on code generated by the user in the past. The generation unit can also analyze the user's past generation results and optimize the generation algorithm. The generation unit can also improve the accuracy of the generation by referring to the user's past generation history. This makes it possible to improve the accuracy of the generation based on the user's past generation results.

[0072] The providing unit can estimate the user's emotions and adjust the display method of the provided code based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. If the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to provide an optimal code display method 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0073] When providing a code, the providing unit can select an appropriate providing method by referring to the user's past providing history. For example, the providing unit can suggest an optimal providing method based on the providing methods used by the user in the past. The providing unit can also analyze the user's past providing history and select the most appropriate providing method. The providing unit can also select a providing method that is suitable for specific conditions from the user's past providing history. This makes it possible to select the optimal providing method based on the user's past providing history.

[0074] When providing code, the providing unit can filter the provided content based on the user's current project or area of ​​interest. For example, the providing unit can preferentially provide code related to the user's current project. The providing unit can also suggest related code based on the user's area of ​​interest. The providing unit can also provide optimal code by referring to the user's past project history. This makes it possible to provide optimal code based on the user's current project or area of ​​interest.

[0075] When providing a code, the providing unit can select an appropriate providing means depending on the user's input method. For example, if the user selects voice input, the providing unit provides the code using voice recognition technology. Furthermore, if the user selects text input, the providing unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the providing unit can also provide the code using image analysis technology. This makes it possible to provide the optimal code providing means depending on the user's input method.

[0076] The marketplace providing unit can estimate the user's emotions and adjust the display method of the marketplace based on the estimated user emotions. For example, if the user is feeling stressed, the marketplace providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the marketplace providing unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the marketplace providing unit can also provide a display method that focuses on the main points. This makes it possible to provide an optimal marketplace display method 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] When providing a marketplace, the marketplace providing unit can select an appropriate provision method by referring to the user's past purchase history. The marketplace providing unit, for example, suggests optimal products based on products the user has previously purchased. The marketplace providing unit can also analyze the user's past purchase history and select the most appropriate product. The marketplace providing unit can also select products that meet specific conditions from the user's past purchase history. This makes it possible to select the optimal provision method based on the user's past purchase history.

[0078] When providing the marketplace, the marketplace providing unit can filter the offerings based on the user's current project or area of ​​interest. For example, the marketplace providing unit can preferentially provide products related to the user's ongoing project. The marketplace providing unit can also suggest related products based on the user's area of ​​interest. The marketplace providing unit can also provide optimal products by referring to the user's past project history. This makes it possible to provide optimal products based on the user's current project or area of ​​interest.

[0079] When providing a marketplace, the marketplace providing unit can select an appropriate provision means depending on the user's input method. For example, if the user selects voice input, the marketplace providing unit can provide products using voice recognition technology. Furthermore, if the user selects text input, the marketplace providing unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the marketplace providing unit can also provide products using image analysis technology. This makes it possible to provide the optimal product provision means depending on the user's input method.

[0080] The marketplace providing unit can estimate the user's emotions and determine marketplace priorities based on the estimated user emotions. For example, if the user is feeling stressed, the marketplace providing unit can provide important products preferentially. Furthermore, if the user is relaxed, the marketplace providing unit can sequentially provide detailed products. Furthermore, if the user is in a hurry, the marketplace providing unit can first provide the most important products. This makes it possible to provide optimal marketplace priorities 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.

[0081] When providing the marketplace, the marketplace providing unit can prioritize providing relevant products by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the marketplace providing unit can prioritize providing products related to that area. The marketplace providing unit can also suggest optimal products based on the user's current location. The marketplace providing unit can also provide relevant products by referring to the user's geographical location information. This makes it possible to provide optimal products based on the user's geographical location information.

[0082] When providing the marketplace, the marketplace providing unit can analyze the user's social media activity and provide related products. The marketplace providing unit can suggest related products based on, for example, information shared by the user on social media. The marketplace providing unit can also analyze the user's social media activity history and suggest optimal products. The marketplace providing unit can also provide related products by taking into account the activity of the user's friends on social media. This makes it possible to provide optimal products based on the user's social media activity.

[0083] The marketplace providing unit can customize an appropriate provision method by reflecting past user feedback when providing a marketplace. The marketplace providing unit, for example, proposes an optimal provision method based on feedback provided by the user in the past. The marketplace providing unit can also customize a provision interface by referring to past user feedback. The marketplace providing unit can also optimize a provision procedure by reflecting user feedback. This makes it possible to customize an optimal provision method based on past user feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, selection unit, generation unit, provision unit, and marketplace provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives input of service content from a user. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the service content received by the reception unit and selects an appropriate sensor. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically generates code corresponding to the sensor selected by the selection unit. The provision unit is realized by the output device 40 of the smart device 14 and provides the user with the code generated by the generation unit. The marketplace provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides applications and products created by other users. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, selection unit, generation unit, provision unit, and marketplace provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives input of service content from a user. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the service content received by the reception unit and selects an appropriate sensor. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically generates code corresponding to the sensor selected by the selection unit. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the user with the code generated by the generation unit. The marketplace provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides applications and products created by other users. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, selection unit, generation unit, provision unit, and marketplace provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives input of service content from a user. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the service content received by the reception unit and selects an appropriate sensor. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically generates code corresponding to the sensor selected by the selection unit. The provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides the user with the code generated by the generation unit. The marketplace provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides applications and products created by other users. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, selection unit, generation unit, provision unit, and marketplace provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives input of service content from a user. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the service content received by the reception unit and selects an appropriate sensor. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically generates code corresponding to the sensor selected by the selection unit. The provision unit is realized by the speaker 240 of the robot 414 and provides the user with the code generated by the generation unit. The marketplace provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides applications and products created by other users.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The reception unit can use natural language processing technology to analyze the user's input and more accurately understand the user's intent. For example, if a user inputs "I want to automate home temperature control," the reception unit can understand the context, extract related keywords, and make more specific suggestions. The reception unit can also provide real-time feedback on the user's input and encourage corrections or additions to the input. Furthermore, the reception unit can compare the user's input with that of other users and match users with common needs. This allows users to collaborate with each other to develop more effective IoT services.

[0086] The reception unit can estimate the user's emotions and adjust the tone and style of its response to the user based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can respond in a gentle tone to reassure the user. If the user is relaxed, the reception unit can respond in a friendly tone to increase the sense of closeness with the user. Furthermore, if the user is in a hurry, the reception unit can provide a quick and concise response, saving the user time. This makes it possible to provide an optimal response according to the user's emotions.

[0087] The reception unit can analyze the user's past input history and learn the user's preferences and tendencies. For example, if the user has frequently selected a particular type of sensor in the past, the reception unit will preferentially suggest that sensor. Also, if the user has preferred a particular input method (voice, text, etc.) in the past, the reception unit can set that input method as the default. Furthermore, the reception unit can predict and suggest the service content to be used during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0088] The reception unit can provide relevant information and resources based on the user's current project and areas of interest. For example, the reception unit can provide technical documents and tutorials related to the user's current project. The reception unit can also suggest related news and the latest technology trends based on the user's areas of interest. Furthermore, the reception unit can filter the most appropriate resources by referring to the user's past project history. This makes it possible to provide the most appropriate information and resources based on the user's current project and areas of interest.

[0089] The reception unit can not only select an appropriate input means according to the user's input method, but also provide auxiliary functions to improve input accuracy. For example, when voice input is selected, not only can the input content be converted into text using voice recognition technology, but also a confirmation prompt can be displayed to prevent misrecognition. When text input is selected, an automatic completion function for the input content can be provided to improve input efficiency. Furthermore, when image input is selected, the input content can be analyzed using image analysis technology and related information can be automatically completed. This makes it possible to provide the optimal input means and auxiliary functions according to the user's input method.

[0090] The selection unit can estimate the user's emotions and personalize the sensor selection process based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can provide a simple and intuitive interface to enable the user to easily select a sensor. Alternatively, if the user is relaxed, the selection unit can provide detailed information to enable the user to select a sensor with a deeper understanding. Furthermore, if the user is in a hurry, the selection unit can provide a quick selection option to save the user's time. This makes it possible to provide an optimal sensor selection process according to the user's emotions.

[0091] The selection unit can suggest the optimal sensor not only by referring to the user's past selection history but also by comparing it with the selection history of other users. For example, the selection unit can suggest sensors to the user by referring to sensors selected by other users with similar needs. The selection unit can also analyze the user's past selection history to select sensors suitable for specific conditions. Furthermore, the selection unit can use data obtained from the user's selection history to optimize the selection algorithm and make more accurate suggestions. This allows the optimal sensor to be selected based on the user's past selection history and the selection history of other users.

[0092] The selection unit can suggest not only sensor selection but also related accessories and additional functions based on the user's current project and area of ​​interest. For example, if the user is working on a temperature control project, the selection unit can suggest not only temperature sensors but also humidity sensors and air quality sensors. The selection unit can also suggest related software and applications based on the user's area of ​​interest. Furthermore, the selection unit can suggest optimal accessories and additional functions by referring to the user's past project history. This makes it possible to provide optimal sensors and related resources based on the user's current project and area of ​​interest.

[0093] The selection unit can not only select a sensor selection means according to the user's input method, but also provide an interactive tutorial to guide the user through the selection process. For example, if voice input is selected, a voice guide can be used to explain the sensor selection procedure. If text input is selected, a step-by-step guide can be displayed to help the user proceed through the selection process without hesitation. Furthermore, if image input is selected, a visual guide can be provided to guide the user through the selection process in a way that is visually easy for the user to understand. This makes it possible to provide the optimal selection means and guide according to the user's input method.

[0094] The generator can estimate the user's emotions and personalize the code generation process based on the estimated user emotions. For example, if the user is stressed, the generator can provide a simple and intuitive code generation interface to enable the user to easily generate code. Alternatively, if the user is relaxed, the generator can provide detailed options to enable the user to generate customizable code. Furthermore, if the user is in a hurry, the generator can provide an option to quickly generate code, saving the user time. This makes it possible to provide an optimal code generation process according to the user's emotions.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The reception unit receives input of service content from the user. For example, the user may input "I want to automate temperature control in my home." Step 2: The selection unit analyzes the service content received by the reception unit and selects an appropriate sensor. For example, it determines that a temperature sensor is necessary for temperature control. Step 3: The generator automatically generates code corresponding to the sensors selected by the selector. For example, it collects data from a temperature sensor and generates code that takes action under specific conditions. Step 4: The providing unit provides the code generated by the generating unit to the user, for example, by sending the generated code to the user by email. Step 5: The marketplace provider provides applications and products created by other users. For example, a temperature control application created by another user can be purchased on the marketplace.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0120] 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.

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

[0122] 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).

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

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0127] 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.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0136] 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.

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

[0138] 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).

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] 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.

[0168] [Explanation of symbols]

[0169] 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 of service contents from a user; a selection unit that analyzes the service content received by the reception unit and selects an appropriate sensor; a generation unit that automatically generates a code corresponding to the sensor selected by the selection unit; a providing unit that provides the code generated by the generating unit to a user; a marketplace providing unit that provides applications or products created by other users; A system characterized by:

2. The reception unit The system estimates the user's emotions and adjusts the input interface for service content based on the estimated user emotions.

2. The system of claim 1.

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

4. The reception unit When entering service offerings, filter the input based on the user's current projects or areas of interest 2. The system of claim 1.

5. The reception unit When entering service details, select the appropriate input method according to the user's input method.

2. The system of claim 1.

6. The selection unit Estimate the user's emotions and adjust the sensor selection criteria based on the estimated user emotions.

2. The system of claim 1.

7. The selection unit When selecting a sensor, the appropriate sensor is selected by referring to the user's past selection history.

2. The system of claim 1.

8. The selection unit When selecting sensors, filter your selection based on your current project or area of ​​interest 2. The system of claim 1.

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