system

Abstract

An object of a system according to an embodiment is to support a beginner to efficiently create a robot.SOLUTION: A system according to an embodiment includes a robot creation assist unit and an FAQ response unit. The robot creation assist unit supports the user's robot creation. The FAQ response unit automatically responds to the user's question.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 technology has a problem in that it lacks support for beginners when building robots, making it difficult to build them efficiently.

[0005] The system according to the embodiment aims to support beginners in efficiently creating robots. [Means for solving the problem]

[0006] The system according to the embodiment includes a robot creation assisting unit and an FAQ responding unit. The robot creation assisting unit supports a user in creating a robot. The FAQ responding unit automatically responds to questions from the user. [Effects of the Invention]

[0007] The system according to the embodiment can support beginners in efficiently creating robots. [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) The robot creation support system according to the embodiment of the present invention is a system that plugs ChatGRP into VeloxRPA and adds robot creation assistance and FAQ functions for beginners. This allows the robot creation support system to provide support to users when creating robots and also realizes an automatic response function for frequently asked questions (FAQs).

[0029] A robot creation support system according to an embodiment includes a robot creation assist unit and an FAQ response unit. The robot creation assist unit supports a user in creating a robot. For example, if a user inputs, "I want to create a robot that extracts data," the robot creation assist unit provides specific instructions, such as, "First, select a data source. Next, set the conditions for the data to be extracted." Similarly, if a user inputs, "I want to create a robot that scrapes data from a website," the robot creation assist unit provides step-by-step guidance, such as, "First, enter the URL of the website to be scraped. Next, specify the data elements to be extracted." The FAQ response unit automatically responds to user questions. For example, if a user asks, "The robot failed to run. What should I do?" the FAQ response unit provides specific solutions, such as, "Check the robot log and check the error message. Correct the settings based on the error message." Similarly, if a user reports, "The robot cannot extract certain data," the FAQ response unit provides specific advice, such as, "Please check whether the data format is correct. If necessary, correct the data format." As a result, the robot creation support system according to the embodiment supports the user in creating a robot and automatically responds to FAQs, thereby improving user convenience.

[0030] The robot creation assist unit can analyze the user's input content in real time and dynamically update the optimal robot creation procedure. For example, ChatGRP, which is the robot creation assist unit, analyzes the user's input content in real time and dynamically updates the optimal robot creation procedure. For example, the next step is automatically suggested based on the content entered by the user. ChatGRP uses an algorithm to analyze the user's input content and dynamically update the next step. For example, natural language processing technology or a machine learning algorithm is used for the real-time analysis. The dynamic update is performed at an appropriate time depending on the user's input content. This makes it possible to dynamically update the optimal robot creation procedure based on the user's input content.

[0031] The FAQ response unit can analyze a user's past question history and generate an FAQ optimized for each individual user. For example, ChatGRP analyzes a user's past question history and generates an FAQ optimized for each individual user. For example, it automatically generates related FAQs based on the content of questions the user has asked in the past. ChatGRP analyzes a user's question history and uses an algorithm to generate an FAQ optimized for each individual user. For example, a database structure and an analysis algorithm are used to collect the past question history. The optimized FAQ is customized based on the user's attributes and the content of past questions. This makes it possible to generate an optimized FAQ based on the user's past question history.

[0032] The robot creation assist unit can customize the assistance content according to the user's skill level. The robot creation assist unit adds a function that enables ChatGRP to customize the assistance content according to the user's skill level. For example, it can provide detailed explanations of basic procedures to beginners and suggest advanced settings to advanced users. ChatGRP uses an algorithm to evaluate the user's skill level and customize the assistance content. For example, self-assessment and test results are used to evaluate the skill level. The customized assistance content differs in the type of information provided and the presentation method depending on the user's skill level. This allows the assistance content to be customized according to the user's skill level.

[0033] The FAQ response unit can automatically generate FAQs in different languages ​​to accommodate international users. For example, ChatGRP can automatically generate FAQs in different languages ​​to accommodate international users. For example, it can provide FAQs translated into multiple languages, such as English, French, and Chinese. ChatGRP uses a translation algorithm to automatically generate FAQs in different languages. The types of supported languages ​​and the method of automatic generation include, for example, the translation algorithm and the list of supported languages. To accommodate international users, local customization and cultural considerations are necessary. This allows FAQs to be automatically generated in different languages ​​to accommodate international users.

[0034] The FAQ response unit can analyze a user's voice input and provide a voice FAQ response. For example, ChatGRP analyzes a user's voice input and provides a voice FAQ response. For example, when a user inputs a question by voice, ChatGRP provides a voice answer. ChatGRP uses a voice recognition algorithm to analyze the voice input and provide a voice FAQ response. For example, a voice recognition algorithm and noise reduction technology are used to analyze the voice input. For example, a voice synthesis technology and response timing are used to provide a voice FAQ response. This allows a voice FAQ response to be provided based on the user's voice input.

[0035] The robot creation assisting unit can refer to the user's past robot creation history and suggest templates based on similar projects. For example, ChatGRP refers to the user's past robot creation history and suggests templates based on similar projects. For example, the robot creation assisting unit automatically generates a template based on the settings of a robot created in the past. ChatGRP uses an algorithm to refer to the user's past robot creation history and suggest templates based on similar projects. Collection of the past robot creation history includes, for example, the database structure and the history storage period. Template suggestions include, for example, standard templates and customizable templates. This allows templates to be suggested based on the user's past robot creation history.

[0036] The robot creation assist unit can analyze the user's voice input and support robot creation through voice guidance. For example, ChatGRP analyzes the user's voice input and supports robot creation through voice guidance. For example, the robot creation assist unit automatically guides the user through the robot creation procedure when the user gives voice instructions. ChatGRP analyzes the voice input and uses a voice recognition algorithm to support robot creation through voice guidance. For example, a voice recognition algorithm and noise removal technology are used to analyze the voice input. The voice guidance includes, for example, voice synthesis technology and a method for generating guidance content. This makes it possible to support robot creation through voice guidance based on the user's voice input.

[0037] The robot creation assist unit can learn the user's operation history, predict the next operation, and automatically suggest it. For example, ChatGRP analyzes the user's past operation history, predicts the next operation, and automatically suggests it. For example, it learns the operations that the user frequently performs and automatically suggests the next operation. ChatGRP uses an algorithm to learn the user's operation patterns and predict the next operation and automatically suggest it. For example, a database structure and a machine learning algorithm are used to collect the operation history. The automatic suggestion includes, for example, a method for generating the suggestion content and the timing of the suggestion. This makes it possible to predict the next operation and automatically suggest it based on the user's operation history.

[0038] The robot creation assist unit can analyze the user's voice input and support the user in creating a robot through voice. The robot creation assist unit uses, for example, ChatGRP to enable the user to create a robot through voice input. For example, the robot creation procedure is automatically guided when the user issues voice instructions. ChatGRP uses a voice recognition algorithm to analyze the voice input and support the user in creating a robot through voice. For example, a voice recognition algorithm and noise reduction technology are used to analyze the voice input. The voice-based robot creation includes, for example, the content of the voice guidance and a method for analyzing the voice input. This allows the user to support the user in creating a robot through voice based on the user's voice input.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The robot creation assistant can customize the assistance content according to the user's skill level. For example, it can explain basic procedures in detail to beginners and suggest advanced settings to advanced users. It can also learn the user's past operation history and predict and automatically suggest the next operation. For example, it can learn the operations that the user frequently performs and automatically suggest the next operation. This makes it possible to customize the assistance content according to the user's skill level and predict and automatically suggest the next operation based on the operation history.

[0041] The FAQ response unit can automatically generate FAQs in different languages ​​and cater to international users. For example, it can provide FAQs translated into multiple languages, such as English, French, and Chinese. It can also analyze the user's voice input and provide a spoken FAQ response. For example, when a user inputs a question by voice, it can provide a spoken answer. This allows it to automatically generate FAQs in different languages, cater to international users, and provide a spoken FAQ response based on the voice input.

[0042] The robot creation assistance unit can refer to the user's past robot creation history and suggest templates based on similar projects. For example, it can automatically generate templates based on the settings of previously created robots. It can also learn the user's operation history and predict and automatically suggest the next operation. This makes it possible to suggest templates based on the user's past robot creation history and predict and automatically suggest the next operation based on the operation history.

[0043] The robot creation assistance unit can analyze the user's voice input and support the robot creation through voice guidance. For example, the robot creation procedure can be automatically guided by the user giving voice instructions. Furthermore, it can learn the user's operation history and predict and automatically suggest the next operation. This makes it possible to support the robot creation through voice guidance based on the user's voice input, and to predict and automatically suggest the next operation based on the operation history.

[0044] The FAQ response unit can analyze a user's past question history and generate FAQs optimized for individual users. For example, it can automatically generate related FAQs based on the user's past questions. Furthermore, it can automatically generate FAQs in different languages ​​to accommodate international users. This allows it to generate optimized FAQs based on a user's past question history and automatically generate FAQs in different languages ​​to accommodate international users.

[0045] The robot creation assistance unit can analyze the user's voice input and support the creation of a robot through voice. For example, the robot creation procedure can be automatically guided by the user giving voice instructions. Furthermore, it can learn the user's operation history and predict and automatically suggest the next operation. This allows the robot creation to be supported through voice based on the user's voice input, and the next operation to be predicted and automatically suggested based on the operation history.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The robot creation assistant supports the user in creating a robot. For example, if a user inputs, "I want to create a robot that extracts data," the robot creation assistant will provide specific instructions such as, "First, select the data source. Next, set the conditions for the data to be extracted." Similarly, if a user inputs, "I want to create a robot that scrapes data from a website," the assistant will provide a step-by-step guide such as, "First, enter the URL of the website to be scraped. Next, specify the data elements to be extracted." Step 2: The FAQ responder automatically responds to user questions. For example, if a user asks, "The robot failed to run. What should I do?", the FAQ responder will provide specific solutions such as, "Check the robot log and check the error message. Correct the settings based on the error message." Also, if a user reports, "The robot cannot extract certain data," the FAQ responder will provide specific advice such as, "Check whether the data format is correct. Correct the data format if necessary."

[0048] (Example 2) The robot creation support system according to the embodiment of the present invention is a system that plugs ChatGRP into VeloxRPA and adds robot creation assistance and FAQ functions for beginners. This allows the robot creation support system to provide support to users when creating robots and also realizes an automatic response function for frequently asked questions (FAQs).

[0049] A robot creation support system according to an embodiment includes a robot creation assist unit and an FAQ response unit. The robot creation assist unit supports a user in creating a robot. For example, if a user inputs, "I want to create a robot that extracts data," the robot creation assist unit provides specific instructions, such as, "First, select a data source. Next, set the conditions for the data to be extracted." Similarly, if a user inputs, "I want to create a robot that scrapes data from a website," the robot creation assist unit provides step-by-step guidance, such as, "First, enter the URL of the website to be scraped. Next, specify the data elements to be extracted." The FAQ response unit automatically responds to user questions. For example, if a user asks, "The robot failed to run. What should I do?" the FAQ response unit provides specific solutions, such as, "Check the robot log and check the error message. Correct the settings based on the error message." Similarly, if a user reports, "The robot cannot extract certain data," the FAQ response unit provides specific advice, such as, "Please check whether the data format is correct. If necessary, correct the data format." As a result, the robot creation support system according to the embodiment supports the user in creating a robot and automatically responds to FAQs, thereby improving user convenience.

[0050] The robot creation assist unit can analyze the user's input content in real time and dynamically update the optimal robot creation procedure. For example, ChatGRP, which is the robot creation assist unit, analyzes the user's input content in real time and dynamically updates the optimal robot creation procedure. For example, the next step is automatically suggested based on the content entered by the user. ChatGRP uses an algorithm to analyze the user's input content and dynamically update the next step. For example, natural language processing technology or a machine learning algorithm is used for the real-time analysis. The dynamic update is performed at an appropriate time depending on the user's input content. This makes it possible to dynamically update the optimal robot creation procedure based on the user's input content.

[0051] The FAQ response unit can analyze a user's past question history and generate an FAQ optimized for each individual user. For example, ChatGRP analyzes a user's past question history and generates an FAQ optimized for each individual user. For example, it automatically generates related FAQs based on the content of questions the user has asked in the past. ChatGRP analyzes a user's question history and uses an algorithm to generate an FAQ optimized for each individual user. For example, a database structure and an analysis algorithm are used to collect the past question history. The optimized FAQ is customized based on the user's attributes and the content of past questions. This makes it possible to generate an optimized FAQ based on the user's past question history.

[0052] The robot creation assist unit can customize the assistance content according to the user's skill level. The robot creation assist unit adds a function that enables ChatGRP to customize the assistance content according to the user's skill level. For example, it can provide detailed explanations of basic procedures to beginners and suggest advanced settings to advanced users. ChatGRP uses an algorithm to evaluate the user's skill level and customize the assistance content. For example, self-assessment and test results are used to evaluate the skill level. The customized assistance content differs in the type of information provided and the presentation method depending on the user's skill level. This allows the assistance content to be customized according to the user's skill level.

[0053] The robot creation assist unit can estimate the user's emotions and provide assistance content to elicit positive emotions. For example, ChatGRP analyzes the user's emotions in real time and provides assistance content to elicit positive emotions. For example, ChatGRP analyzes the user's facial expressions and voice and provides assistance content to elicit positive emotions. ChatGRP uses an algorithm to estimate the user's emotions and provide assistance content to elicit positive emotions. For example, facial expression recognition and voice analysis technology are used for emotion estimation. Assistance content to elicit positive emotions includes, for example, providing encouraging messages and success stories. This makes it possible to provide assistance content to elicit positive emotions according to the user's emotions.

[0054] The FAQ response unit can automatically generate FAQs in different languages ​​to accommodate international users. For example, ChatGRP can automatically generate FAQs in different languages ​​to accommodate international users. For example, it can provide FAQs translated into multiple languages, such as English, French, and Chinese. ChatGRP uses a translation algorithm to automatically generate FAQs in different languages. The types of supported languages ​​and the method of automatic generation include, for example, the translation algorithm and the list of supported languages. To accommodate international users, local customization and cultural considerations are necessary. This allows FAQs to be automatically generated in different languages ​​to accommodate international users.

[0055] The FAQ response unit can analyze a user's voice input and provide a voice FAQ response. For example, ChatGRP analyzes a user's voice input and provides a voice FAQ response. For example, when a user inputs a question by voice, ChatGRP provides a voice answer. ChatGRP uses a voice recognition algorithm to analyze the voice input and provide a voice FAQ response. For example, a voice recognition algorithm and noise reduction technology are used to analyze the voice input. For example, a voice synthesis technology and response timing are used to provide a voice FAQ response. This allows a voice FAQ response to be provided based on the user's voice input.

[0056] The robot creation assisting unit can refer to the user's past robot creation history and suggest templates based on similar projects. For example, ChatGRP refers to the user's past robot creation history and suggests templates based on similar projects. For example, the robot creation assisting unit automatically generates a template based on the settings of a robot created in the past. ChatGRP uses an algorithm to refer to the user's past robot creation history and suggest templates based on similar projects. Collection of the past robot creation history includes, for example, the database structure and the history storage period. Template suggestions include, for example, standard templates and customizable templates. This allows templates to be suggested based on the user's past robot creation history.

[0057] The robot creation assist unit can estimate the user's emotions and send a message to motivate the user when the user hits a deadlock. The robot creation assist unit, for example, uses ChatGRP to analyze the user's emotions in real time and send a message to motivate the user when the user hits a deadlock. For example, ChatGRP analyzes the user's facial expressions and voice and sends a message to motivate the user. ChatGRP uses an algorithm to estimate the user's emotions and send a message to motivate the user when the user hits a deadlock. For example, facial expression recognition and voice analysis technology are used for emotion estimation. Messages to motivate the user include, for example, messages of encouragement and the provision of success experiences. This allows messages to motivate the user to be sent according to the user's emotions.

[0058] The robot creation assist unit can analyze the user's voice input and support robot creation through voice guidance. For example, ChatGRP analyzes the user's voice input and supports robot creation through voice guidance. For example, the robot creation assist unit automatically guides the user through the robot creation procedure when the user gives voice instructions. ChatGRP analyzes the voice input and uses a voice recognition algorithm to support robot creation through voice guidance. For example, a voice recognition algorithm and noise removal technology are used to analyze the voice input. The voice guidance includes, for example, voice synthesis technology and a method for generating guidance content. This makes it possible to support robot creation through voice guidance based on the user's voice input.

[0059] The robot creation assist unit can learn the user's operation history, predict the next operation, and automatically suggest it. For example, ChatGRP analyzes the user's past operation history, predicts the next operation, and automatically suggests it. For example, it learns the operations that the user frequently performs and automatically suggests the next operation. ChatGRP uses an algorithm to learn the user's operation patterns and predict the next operation and automatically suggest it. For example, a database structure and a machine learning algorithm are used to collect the operation history. The automatic suggestion includes, for example, a method for generating the suggestion content and the timing of the suggestion. This makes it possible to predict the next operation and automatically suggest it based on the user's operation history.

[0060] The robot creation assist unit can analyze the user's voice input and support the user in creating a robot through voice. The robot creation assist unit uses, for example, ChatGRP to enable the user to create a robot through voice input. For example, the robot creation procedure is automatically guided when the user issues voice instructions. ChatGRP uses a voice recognition algorithm to analyze the voice input and support the user in creating a robot through voice. For example, a voice recognition algorithm and noise reduction technology are used to analyze the voice input. The voice-based robot creation includes, for example, the content of the voice guidance and a method for analyzing the voice input. This allows the user to support the user in creating a robot through voice based on the user's voice input.

[0061] The robot creation assist unit can estimate the user's emotions and provide advice to relax if the user is feeling stressed. For example, ChatGRP analyzes the user's emotions in real time and provides advice to relax if the user is feeling stressed. For example, ChatGRP analyzes the user's facial expressions and voice and provides advice to relax if it determines that the user is feeling stressed. ChatGRP uses an algorithm to estimate the user's emotions and provide advice to relax if the user is feeling stressed. For example, facial expression recognition and voice analysis technologies are used to estimate the emotions. The advice to relax includes, for example, relaxation techniques and stress management methods. This allows the robot creation assist unit to provide advice to relax according to the user's emotions.

[0062] The robot creation assistance unit can estimate a user's emotions and propose an interface design that elicits positive emotions. For example, ChatGRP analyzes a user's emotions in real time and proposes an interface design that elicits positive emotions. For example, it analyzes the user's facial expressions and voice and proposes an interface design that elicits positive emotions. ChatGRP uses an algorithm to estimate a user's emotions and propose an interface design that elicits positive emotions. For example, facial expression recognition and voice analysis technology are used for emotion estimation. For example, usability testing and design principles are included in the interface design. This makes it possible to propose an interface design that elicits positive emotions according to the user's emotions.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The robot creation assistant can customize the assistance content according to the user's skill level. For example, it can explain basic procedures in detail to beginners and suggest advanced settings to advanced users. It can also learn the user's past operation history and predict and automatically suggest the next operation. For example, it can learn the operations that the user frequently performs and automatically suggest the next operation. This makes it possible to customize the assistance content according to the user's skill level and predict and automatically suggest the next operation based on the operation history.

[0065] The robot creation assist unit can estimate the user's emotions and provide assistance content to elicit positive emotions. For example, it can analyze the user's facial expressions and voice and provide assistance content to elicit positive emotions. Specifically, this includes providing encouraging messages and success stories. It can also provide advice to help the user relax if the user is feeling stressed. This makes it possible to provide assistance content to elicit positive emotions according to the user's emotions, and to provide advice to help the user relax if the user is feeling stressed.

[0066] The FAQ response unit can automatically generate FAQs in different languages ​​and cater to international users. For example, it can provide FAQs translated into multiple languages, such as English, French, and Chinese. It can also analyze the user's voice input and provide a spoken FAQ response. For example, when a user inputs a question by voice, it can provide a spoken answer. This allows it to automatically generate FAQs in different languages, cater to international users, and provide a spoken FAQ response based on the voice input.

[0067] The robot creation assistance unit can refer to the user's past robot creation history and suggest templates based on similar projects. For example, it can automatically generate templates based on the settings of previously created robots. It can also learn the user's operation history and predict and automatically suggest the next operation. This makes it possible to suggest templates based on the user's past robot creation history and predict and automatically suggest the next operation based on the operation history.

[0068] The robot creation assistant can estimate the user's emotions and send messages to motivate them when they get stuck in a task. For example, it can analyze the user's facial expressions and voice and send messages to motivate them. Specifically, this includes providing encouraging messages and success stories. It can also provide advice on how to relax if the user is feeling stressed. This makes it possible to send messages to motivate the user according to their emotions, and to provide advice on how to relax if the user is feeling stressed.

[0069] The robot creation assistance unit can analyze the user's voice input and support the robot creation through voice guidance. For example, the robot creation procedure can be automatically guided by the user giving voice instructions. Furthermore, it can learn the user's operation history and predict and automatically suggest the next operation. This makes it possible to support the robot creation through voice guidance based on the user's voice input, and to predict and automatically suggest the next operation based on the operation history.

[0070] The robot creation assistance unit can estimate the user's emotions and propose interface designs that elicit positive emotions. For example, it analyzes the user's facial expressions and voice and proposes interface designs that elicit positive emotions. Specifically, this includes usability testing and design principles. It can also provide advice on how to relax if the user is feeling stressed. This makes it possible to propose interface designs that elicit positive emotions according to the user's emotions, and to provide advice on how to relax if the user is feeling stressed.

[0071] The FAQ response unit can analyze a user's past question history and generate FAQs optimized for individual users. For example, it can automatically generate related FAQs based on the user's past questions. Furthermore, it can automatically generate FAQs in different languages ​​to accommodate international users. This allows it to generate optimized FAQs based on a user's past question history and automatically generate FAQs in different languages ​​to accommodate international users.

[0072] The robot creation assist unit can estimate the user's emotions and provide assistance content to elicit positive emotions. For example, it can analyze the user's facial expressions and voice and provide assistance content to elicit positive emotions. Specifically, this includes providing encouraging messages and success stories. It can also provide advice to help the user relax if the user is feeling stressed. This makes it possible to provide assistance content to elicit positive emotions according to the user's emotions, and to provide advice to help the user relax if the user is feeling stressed.

[0073] The robot creation assistance unit can analyze the user's voice input and support the creation of a robot through voice. For example, the robot creation procedure can be automatically guided by the user giving voice instructions. Furthermore, it can learn the user's operation history and predict and automatically suggest the next operation. This allows the robot creation to be supported through voice based on the user's voice input, and the next operation to be predicted and automatically suggested based on the operation history.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The robot creation assistant supports the user in creating a robot. For example, if a user inputs, "I want to create a robot that extracts data," the robot creation assistant will provide specific instructions such as, "First, select the data source. Next, set the conditions for the data to be extracted." Similarly, if a user inputs, "I want to create a robot that scrapes data from a website," the assistant will provide a step-by-step guide such as, "First, enter the URL of the website to be scraped. Next, specify the data elements to be extracted." Step 2: The FAQ responder automatically responds to user questions. For example, if a user asks, "The robot failed to run. What should I do?", the FAQ responder will provide specific solutions such as, "Check the robot log and check the error message. Correct the settings based on the error message." Also, if a user reports, "The robot cannot extract certain data," the FAQ responder will provide specific advice such as, "Check whether the data format is correct. Correct the data format if necessary."

[0076] 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.

[0077] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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. [Explanation of symbols]

[0143] 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 robot creation assistance unit that supports users in creating robots; an FAQ response unit that automatically responds to the user's questions; A system characterized by:

2. The robot creation assist unit The user's input is analyzed in real time, and the optimal robot creation procedure is dynamically updated.

2. The system of claim 1.

3. The FAQ response unit Analyze the user's past question history and generate FAQs optimized for each individual user 2. The system of claim 1.

4. The robot creation assist unit Customize the assistance content according to the skill level of the user.

2. The system of claim 1.

5. The robot creation assist unit Estimate the user's emotions and provide assistance content to elicit positive emotions 2. The system of claim 1.

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