system

Abstract

The system according to the embodiment aims to analyze input information from a user and provide automated operations. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an automation unit, and a provision unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit. The automation unit automates an operation based on the information analyzed by the analysis unit. The provision unit provides the results of the operation automated by the automation unit.

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 techniques have not been sufficient in efficiently analyzing input information from users and providing automated operations, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze input information from a user and provide automated operations. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an automation unit, and a provision unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit. The automation unit automates an operation based on the information analyzed by the analysis unit. The provision unit provides the results of the operation automated by the automation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze input information from a user and provide automated operations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention provides a method for using smartAIChat through system integration or a Chrome extension even when the API is not publicly available. In this system, a user first uses smartAIChat from a web screen. Next, a specific script is used to automate the web screen operations and achieve system integration. Furthermore, a Chrome extension is developed to provide an interface for using smartAIChat on a browser. This mechanism expands the use of smartAIChat and improves productivity. For example, a user uses smartAIChat from a web screen. While the user performs normal operations, a specific script runs in the background to automate the operations. For example, when the user enters a specific question, the script automatically processes the question and sends it to smartAIChat. Next, the script receives a response from smartAIChat and displays it to the user. The script then analyzes the response and performs additional operations as necessary. For example, based on the response, specific data is retrieved and provided to the user. Furthermore, a Chrome extension is developed to provide an interface for using smartAIChat on a browser. This extension allows users to use smartAIChat on web pages, and they can access it simply by clicking a specific button. For example, when a user selects text on a web page and clicks the extension button, the text is sent to smartAIChat, and a response is displayed. This mechanism expands the use of smartAIChat and improves productivity. By automating operations from the web screen, users can use smartAIChat efficiently, saving time and effort. In addition, by using the Chrome extension, smartAIChat can be easily accessed from the browser, allowing it to be used without interrupting the workflow. For example, when conducting research or gathering information, users can use smartAIChat to quickly obtain information and proceed with their work.This means that the smartAIChat system can be used efficiently through system integration and Chrome extensions, even in situations where the API is not publicly available.

[0029] The smartAIChat system according to the embodiment includes a reception unit, an analysis unit, an automation unit, and a provision unit. The reception unit receives input from a user. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit can receive input, for example, when a user selects text on a web page and clicks an extension button. The analysis unit analyzes the information received by the reception unit. The analysis unit performs analysis using, for example, a JavaScript (registered trademark) or Python (registered trademark) library. For example, the analysis unit can visualize data using the JavaScript D3.js library. The analysis unit can also build a machine learning model and perform data analysis using the Python TensorFlow library. The analysis unit can also perform numerical calculations using the Python NumPy library. The automation unit automates operations based on the information analyzed by the analysis unit. For example, the automation unit automates operations using a specific script. For example, the automation unit can automate operations on a web page using a Python script. The automation unit can also automatically execute system commands using a shell script. Furthermore, the automation unit can also automate operations on a browser using JavaScript. The providing unit provides the results of the operations automated by the automation unit. The providing unit provides the results using, for example, a Chrome extension. For example, the providing unit can provide an interface for displaying the results on a browser. The providing unit can also provide the results using a Firefox add-on. Furthermore, the providing unit can provide the results in report format or graph format. This allows the smartAIChat system according to the embodiment to efficiently analyze and automate user input and provide the results.

[0030] The analysis unit can perform analysis using a JavaScript or Python library. Examples of JavaScript or Python libraries include, but are not limited to, TensorFlow, NumPy, and D3.js. The analysis unit can visualize data using, for example, the JavaScript D3.js library. For example, the D3.js library can be used to display data as graphs or charts. The analysis unit can also build a machine learning model using Python's TensorFlow library to perform data analysis. For example, the TensorFlow library can be used to perform data classification and prediction. Furthermore, the analysis unit can perform numerical calculations using Python's NumPy library. For example, the NumPy library can be used to process large data sets and perform statistical analysis. As a result, using JavaScript or Python libraries improves the accuracy of the analysis.

[0031] The automation unit can automate operations using specific scripts. Examples of specific scripts include, but are not limited to, Python scripts and shell scripts. For example, the automation unit can automate operations on web pages using Python scripts. For example, the operation of clicking a specific button can be automated using a Python script. The automation unit can also automatically execute system commands using shell scripts. For example, the operation of taking periodic backups can be automated using a shell script. Furthermore, the automation unit can automate operations on a browser using JavaScript. For example, form input can be automated using JavaScript. This allows operations to be automated efficiently by using specific scripts.

[0032] The providing unit may provide the results using a browser extension. Examples of browser extensions include, but are not limited to, a Chrome extension and a Firefox add-on. The providing unit may provide the results using, for example, a Chrome extension. For example, the Chrome extension may provide an interface for displaying the results on a browser. The providing unit may also provide the results using a Firefox add-on. For example, the Firefox add-on may provide an interface for displaying the results on a browser. The providing unit may also provide the results in a report format or a graph format. For example, the providing unit may provide the results as a report in PDF format. The providing unit may also display the results as a graph or chart. This allows the results to be easily provided on a browser by using a Chrome extension.

[0033] The reception unit can accept input when a user selects text on a web page and clicks an extension button. Methods for selecting text on a web page include, but are not limited to, highlighting, right-click menus, and the like. The reception unit can accept input when a user highlights text on a web page and clicks an extension button. For example, when a user highlights specific text and clicks an extension button, the text is sent to smartAIChat. The reception unit can also accept input when a user right-clicks text on a web page and selects an extension option from the context menu. For example, when a user right-clicks specific text and selects "Send to smartAIChat" from the context menu, the text is sent to smartAIChat. This allows a user to easily accept input by selecting text on a web page and clicking an extension button.

[0034] The providing unit can acquire specific data based on the response content and provide it to the user. Examples of specific data include, but are not limited to, data acquired from an API or a query from a database. The providing unit can acquire data from an API, for example. For example, the providing unit can send a request to a specific API endpoint and provide the acquired data to the user. The providing unit can also query a database. For example, the providing unit can execute a query on a specific database and provide the acquired data to the user. Furthermore, the providing unit can acquire specific data based on the response content and provide an interface for providing the data to the user. For example, the providing unit can automatically acquire related data based on the response content and display it to the user. This improves user convenience by acquiring specific data based on the response content and providing it to the user.

[0035] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest input methods to be used in specific time periods based on the user's past input history. Furthermore, the reception unit can also suggest similar input methods by referring to content entered by the user in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal reception method.

[0036] The reception unit can perform filtering based on the user's current work status and areas of interest when receiving input. The reception unit can, for example, analyze the work content currently being performed by the user and receive only related input. The reception unit can also preferentially receive highly relevant input based on the user's areas of interest. Furthermore, the reception unit can filter unnecessary input according to the user's work status and provide efficient operation. As a result, efficient operation can be provided by filtering based on the user's current work status and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's work status data to a generation AI and have the generation AI perform filtering.

[0037] When receiving input, the reception unit can prioritize receiving highly relevant input by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving input related to that area. Furthermore, when the user is moving, the reception unit can also suggest optimal input based on the user's current location. Furthermore, when the user is in a specific location, the reception unit can prioritize receiving information related to that location. In this way, by taking the user's geographical location information into account, highly relevant input can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to prioritize highly relevant input.

[0038] The reception unit can analyze the user's social media activities and receive relevant inputs when receiving inputs. The reception unit can, for example, analyze the content of the user's social media posts and prioritize receiving relevant inputs. The reception unit can also suggest an optimal input method by referring to the user's social media activity history. Furthermore, the reception unit can also prioritize receiving relevant inputs based on the user's areas of interest on social media. In this way, by analyzing the user's social media activities, it is possible to prioritize receiving relevant inputs. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to a generation AI and have the generation AI execute the prioritization of relevant inputs.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit can perform a detailed analysis on input information with high importance. The analysis unit can also perform a concise analysis on input information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the input information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the input information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the input information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of input information. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. Furthermore, the analysis unit can apply a voice analysis algorithm to voice data. This improves the accuracy of analysis by applying different analysis algorithms depending on the category of input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the input information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0041] During analysis, the analysis unit can determine the analysis priority based on the submission time of the input information. For example, the analysis unit can prioritize analysis of input information submitted earlier. The analysis unit can also postpone analysis of input information submitted later. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the input information to the generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. For example, the analysis unit can prioritize analysis of highly relevant input information. The analysis unit can also postpone analysis of less relevant input information. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the input information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the input information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the input information to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The automation unit can improve the accuracy of automation by taking into account the interrelationships of input information during automation. For example, the automation unit can analyze the interrelationships of input information and automate related operations. The automation unit can also select an optimal automation procedure based on the interrelationships of input information. Furthermore, the automation unit can improve the accuracy of automation by taking into account the interrelationships of input information. As a result, the accuracy of automation is improved by taking into account the interrelationships of input information. Some or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input interrelationship data of input information to a generation AI and cause the generation AI to improve the accuracy of automation.

[0044] During automation, the automation unit can perform automation while taking into account attribute information of the submitter of the input information. The automation unit can adjust the automation procedure based on, for example, the submitter's attribute information (job title, department, etc.). The automation unit can also select the optimal automation procedure by taking into account the submitter's attribute information. Furthermore, the automation unit can improve the accuracy of automation based on the submitter's attribute information. This enables more appropriate automation by taking into account the submitter's attribute information. Some or all of the above-described processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input the submitter's attribute information data into the generation AI and have the generation AI adjust the automation procedure.

[0045] The automation unit can perform automation while taking into account the geographic distribution of the input information. For example, the automation unit can analyze the geographic distribution of the input information and automate related operations. The automation unit can also select an optimal automation procedure based on the geographic distribution of the input information. Furthermore, the automation unit can improve the accuracy of automation by taking into account the geographic distribution of the input information. This enables more appropriate automation by taking into account the geographic distribution of the input information. Some or all of the above-described processing in the automation unit can be performed using, for example, AI, or can be performed without using AI. For example, the automation unit can input geographic distribution data of the input information to the generation AI and have the generation AI select an automation procedure.

[0046] During automation, the automation unit can improve the accuracy of automation by referring to literature related to the input information. For example, the automation unit can refer to literature related to the input information and select an optimal automation procedure. The automation unit can also improve the accuracy of automation based on literature related to the input information. Furthermore, the automation unit can provide detailed automation procedures by referring to literature related to the input information. This improves the accuracy of automation by referring to related literature. Some or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input literature data related to the input information into the generation AI and have the generation AI select an automation procedure.

[0047] The providing unit can optimize the current content to be provided by referring to past provided data at the time of providing the content. The providing unit can, for example, analyze the past provided data and select the optimal content to be provided. The providing unit can also optimize the current content to be provided based on the past provided data. Furthermore, the providing unit can provide detailed content to be provided by referring to the past provided data. In this way, the current content to be provided can be optimized by referring to the past provided data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the past provided data to a generating AI and cause the generating AI to optimize the current content to be provided.

[0048] The providing unit can apply different providing methods to different categories of input information when providing the information. For example, the providing unit can apply a providing method using natural language processing to text data. The providing unit can also apply a providing method using image analysis to image data. The providing unit can also apply a providing method using voice analysis to voice data. This improves the accuracy of the provided content by applying different providing methods depending on the category of input information. Some or all of the above-described processing by the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input category data of the input information to the generation AI and cause the generation AI to apply the providing method.

[0049] The providing unit can analyze changes in the provided content based on the submission time of the input information at the time of provision. For example, the providing unit can prioritize analysis of the provided content based on input information submitted earlier. The providing unit can also postpone the provided content based on input information submitted later. Furthermore, the providing unit can dynamically analyze changes in the provided content based on the submission time. This enables more appropriate provided content to be provided by analyzing changes in the provided content based on the submission time of the input information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input submission time data of the input information to the generation AI and cause the generation AI to analyze changes in the provided content.

[0050] The provision unit can analyze the content of the provision by referring to market data related to the input information when providing the content. The provision unit can, for example, refer to market data related to the input information and select optimal content of the provision. The provision unit can also optimize the content of the provision based on the market data related to the input information. Furthermore, the provision unit can provide detailed content of the provision by referring to market data related to the input information. By referring to the related market data, the accuracy of the content of the provision is improved. Some or all of the above-described processing in the provision unit can be performed, for example, using AI or without AI. For example, the provision unit can input market data related to the input information to a generation AI and cause the generation AI to analyze the content of the provision.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can automatically search for related past data based on the user's input and present it to the user. For example, when a user inputs a specific question, the reception unit can search for similar past questions and their answers and present them to the user. Also, when a user inputs a specific keyword, the reception unit can search for past data related to the keyword and provide it to the user. Furthermore, the reception unit can automatically filter related past data based on the user's input and present the most relevant data preferentially. This allows the user to efficiently obtain information while referring to past data.

[0053] The analysis unit can retrieve information from relevant external data sources based on user input and use it in the analysis. For example, if a user asks a question about a specific topic, the analysis unit can retrieve related information from public databases and APIs on the Internet and use it in the analysis. Also, if a user specifies a specific dataset, external data related to that dataset can be automatically retrieved and incorporated into the analysis. Furthermore, the analysis unit can search for related academic papers and technical literature based on the user's input and use it in the analysis. This improves the accuracy of the analysis and provides richer information.

[0054] The automation unit can automatically schedule and execute related tasks based on user input. For example, if a user inputs a specific task, the automation unit can schedule that task to be executed at the appropriate time. Also, if a user inputs multiple tasks, the automation unit can automatically determine the priority of those tasks and execute them efficiently. Furthermore, the automation unit can automatically group related tasks based on user input and execute them together. This makes it easier for users to manage tasks and work more efficiently.

[0055] The providing unit can automatically generate related visual content based on the user's input and provide it to the user. For example, when a user inputs specific data, the providing unit can automatically generate graphs and charts based on the data and provide them to the user. Also, when a user asks a question about a specific topic, the providing unit can automatically search for images and videos related to the topic and provide them to the user. Furthermore, the providing unit can automatically generate related infographics and presentation slides based on the user's input and provide them to the user. This makes it easier for users to understand information visually and acquire information efficiently.

[0056] The reception unit can automatically generate and send to the user relevant notifications based on the user's input. For example, if the user inputs a specific task, the reception unit can periodically notify the user of the progress of that task. Also, if the user inputs a specific event, the reception unit can automatically generate and send to the user a reminder for that event. Furthermore, the reception unit can automatically generate and send to the user relevant alerts based on the user's input. This allows the user to work efficiently without missing important information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives input from the user. User input includes text input, voice input, image input, etc. For example, the user can receive input by selecting text on a web page and clicking the extension's button. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit performs the analysis using JavaScript or Python libraries. For example, the analysis unit can visualize data using the JavaScript D3.js library, build a machine learning model and perform data analysis using the Python TensorFlow library, or perform numerical calculations using the Python NumPy library. Step 3: The automation unit automates operations based on the information analyzed by the analysis unit. The automation unit automates operations using specific scripts. For example, it is possible to automate web page operations using Python scripts, automatically execute system commands using shell scripts, or automate browser operations using JavaScript. Step 4: The providing unit provides the results of the operations automated by the automation unit. The providing unit provides the results using a Chrome extension or a Firefox add-on. For example, the providing unit can provide an interface for displaying the results on a browser, or can provide the results in report or graph format.

[0059] (Example 2) A system according to an embodiment of the present invention provides a method for using smartAIChat through system integration or a Chrome extension even when the API is not publicly available. In this system, a user first uses smartAIChat from a web screen. Next, a specific script is used to automate the web screen operations and achieve system integration. Furthermore, a Chrome extension is developed to provide an interface for using smartAIChat on a browser. This mechanism expands the use of smartAIChat and improves productivity. For example, a user uses smartAIChat from a web screen. While the user performs normal operations, a specific script runs in the background to automate the operations. For example, when the user enters a specific question, the script automatically processes the question and sends it to smartAIChat. Next, the script receives a response from smartAIChat and displays it to the user. The script then analyzes the response and performs additional operations as necessary. For example, based on the response, specific data is retrieved and provided to the user. Furthermore, a Chrome extension is developed to provide an interface for using smartAIChat on a browser. This extension allows users to use smartAIChat on web pages, and they can access it simply by clicking a specific button. For example, when a user selects text on a web page and clicks the extension button, the text is sent to smartAIChat, and a response is displayed. This mechanism expands the use of smartAIChat and improves productivity. By automating operations from the web screen, users can use smartAIChat efficiently, saving time and effort. In addition, by using the Chrome extension, smartAIChat can be easily accessed from the browser, allowing it to be used without interrupting the workflow. For example, when conducting research or gathering information, users can use smartAIChat to quickly obtain information and proceed with their work.This means that the smartAIChat system can be used efficiently through system integration and Chrome extensions, even in situations where the API is not publicly available.

[0060] The smartAIChat system according to the embodiment includes a reception unit, an analysis unit, an automation unit, and a provision unit. The reception unit receives input from a user. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit can receive input, for example, when a user selects text on a web page and clicks an extension button. The analysis unit analyzes the information received by the reception unit. The analysis unit performs analysis using, for example, JavaScript or Python libraries. For example, the analysis unit can visualize data using the JavaScript D3.js library. The analysis unit can also build a machine learning model and perform data analysis using the Python TensorFlow library. The analysis unit can also perform numerical calculations using the Python NumPy library. The automation unit automates operations based on the information analyzed by the analysis unit. For example, the automation unit automates operations using a specific script. For example, the automation unit can automate web page operations using a Python script. The automation unit can also automatically execute system commands using a shell script. Furthermore, the automation unit can automate operations on a browser using JavaScript. The providing unit provides results of the operations automated by the automation unit. The providing unit provides the results using, for example, a Chrome extension. For example, the providing unit can provide an interface for displaying the results on a browser. The providing unit can also provide the results using a Firefox add-on. Furthermore, the providing unit can provide the results in report format or graph format. As a result, the smartAIChat system according to the embodiment can efficiently analyze and automate user input and provide the results.

[0061] The analysis unit can perform analysis using a JavaScript or Python library. Examples of JavaScript or Python libraries include, but are not limited to, TensorFlow, NumPy, and D3.js. The analysis unit can visualize data using, for example, the JavaScript D3.js library. For example, the D3.js library can be used to display data as graphs or charts. The analysis unit can also build a machine learning model using Python's TensorFlow library to perform data analysis. For example, the TensorFlow library can be used to perform data classification and prediction. Furthermore, the analysis unit can perform numerical calculations using Python's NumPy library. For example, the NumPy library can be used to process large data sets and perform statistical analysis. As a result, using JavaScript or Python libraries improves the accuracy of the analysis.

[0062] The automation unit can automate operations using specific scripts. Examples of specific scripts include, but are not limited to, Python scripts and shell scripts. For example, the automation unit can automate operations on web pages using Python scripts. For example, the operation of clicking a specific button can be automated using a Python script. The automation unit can also automatically execute system commands using shell scripts. For example, the operation of taking periodic backups can be automated using a shell script. Furthermore, the automation unit can automate operations on a browser using JavaScript. For example, form input can be automated using JavaScript. This allows operations to be automated efficiently by using specific scripts.

[0063] The providing unit may provide the results using a browser extension. Examples of browser extensions include, but are not limited to, a Chrome extension and a Firefox add-on. The providing unit may provide the results using, for example, a Chrome extension. For example, the Chrome extension may provide an interface for displaying the results on a browser. The providing unit may also provide the results using a Firefox add-on. For example, the Firefox add-on may provide an interface for displaying the results on a browser. The providing unit may also provide the results in a report format or a graph format. For example, the providing unit may provide the results as a report in PDF format. The providing unit may also display the results as a graph or chart. This allows the results to be easily provided on a browser by using a Chrome extension.

[0064] The reception unit can accept input when a user selects text on a web page and clicks an extension button. Methods for selecting text on a web page include, but are not limited to, highlighting, right-click menus, and the like. The reception unit can accept input when a user highlights text on a web page and clicks an extension button. For example, when a user highlights specific text and clicks an extension button, the text is sent to smartAIChat. The reception unit can also accept input when a user right-clicks text on a web page and selects an extension option from the context menu. For example, when a user right-clicks specific text and selects "Send to smartAIChat" from the context menu, the text is sent to smartAIChat. This allows a user to easily accept input by selecting text on a web page and clicking an extension button.

[0065] The providing unit can acquire specific data based on the response content and provide it to the user. Examples of specific data include, but are not limited to, data acquired from an API or a query from a database. The providing unit can acquire data from an API, for example. For example, the providing unit can send a request to a specific API endpoint and provide the acquired data to the user. The providing unit can also query a database. For example, the providing unit can execute a query on a specific database and provide the acquired data to the user. Furthermore, the providing unit can acquire specific data based on the response content and provide an interface for providing the data to the user. For example, the providing unit can automatically acquire related data based on the response content and display it to the user. This improves user convenience by acquiring specific data based on the response content and providing it to the user.

[0066] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can delay the timing of input reception and wait until the user is relaxed. Furthermore, if the user is relaxed, the reception unit can immediately accept input and provide smooth operation. Furthermore, if the user is in a hurry, the reception unit can advance the timing of input reception and quickly proceed with operation. This allows input reception at a more appropriate timing by adjusting the timing of input reception according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0067] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest input methods to be used in specific time periods based on the user's past input history. Furthermore, the reception unit can also suggest similar input methods by referring to content entered by the user in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal reception method.

[0068] The reception unit can perform filtering based on the user's current work status and areas of interest when receiving input. The reception unit can, for example, analyze the work content currently being performed by the user and receive only related input. The reception unit can also preferentially receive highly relevant input based on the user's areas of interest. Furthermore, the reception unit can filter unnecessary input according to the user's work status and provide efficient operation. As a result, efficient operation can be provided by filtering based on the user's current work status and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's work status data to a generation AI and have the generation AI perform filtering.

[0069] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can postpone less important inputs and prioritize important inputs. Furthermore, when the user is relaxed, the reception unit can process all inputs equally. Furthermore, when the user is in a hurry, the reception unit can prioritize inputs that require quick processing. Thus, by determining the priority of inputs according to the user's emotions, important inputs can be processed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the inputs.

[0070] When receiving input, the reception unit can prioritize receiving highly relevant input by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving input related to that area. Furthermore, when the user is moving, the reception unit can also suggest optimal input based on the user's current location. Furthermore, when the user is in a specific location, the reception unit can prioritize receiving information related to that location. In this way, by taking the user's geographical location information into account, highly relevant input can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to prioritize highly relevant input.

[0071] The reception unit can analyze the user's social media activities and receive relevant inputs when receiving inputs. The reception unit can, for example, analyze the content of the user's social media posts and prioritize receiving relevant inputs. The reception unit can also suggest an optimal input method by referring to the user's social media activity history. Furthermore, the reception unit can also prioritize receiving relevant inputs based on the user's areas of interest on social media. In this way, by analyzing the user's social media activities, it is possible to prioritize receiving relevant inputs. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to a generation AI and have the generation AI execute the prioritization of relevant inputs.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit can perform a detailed analysis on input information with high importance. The analysis unit can also perform a concise analysis on input information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the input information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the input information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the input information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of input information. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. Furthermore, the analysis unit can apply a voice analysis algorithm to voice data. This improves the accuracy of analysis by applying different analysis algorithms depending on the category of input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the input information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0076] During analysis, the analysis unit can determine the analysis priority based on the submission time of the input information. For example, the analysis unit can prioritize analysis of input information submitted earlier. The analysis unit can also postpone analysis of input information submitted later. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the input information to the generation AI and have the generation AI determine the analysis priority.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. For example, the analysis unit can prioritize analysis of highly relevant input information. The analysis unit can also postpone analysis of less relevant input information. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the input information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the input information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the input information to the generation AI and cause the generation AI to adjust the order of analysis.

[0078] The automation unit can estimate the user's emotions and adjust the automation criteria based on the estimated user emotions. For example, if the user is feeling stressed, the automation unit can relax the automation criteria and simplify operations. Furthermore, if the user is relaxed, the automation unit can tighten the automation criteria and perform detailed operations. Furthermore, if the user is in a hurry, the automation unit can relax the automation criteria and perform quick operations. This allows for more appropriate automation by adjusting the automation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the automation unit can be performed using, for example, an AI. For example, the automation unit can input user emotion data into the generation AI and have the generation AI adjust the automation criteria.

[0079] The automation unit can improve the accuracy of automation by taking into account the interrelationships of input information during automation. For example, the automation unit can analyze the interrelationships of input information and automate related operations. The automation unit can also select an optimal automation procedure based on the interrelationships of input information. Furthermore, the automation unit can improve the accuracy of automation by taking into account the interrelationships of input information. As a result, the accuracy of automation is improved by taking into account the interrelationships of input information. Some or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input interrelationship data of input information to a generation AI and cause the generation AI to improve the accuracy of automation.

[0080] During automation, the automation unit can perform automation while taking into account attribute information of the submitter of the input information. The automation unit can adjust the automation procedure based on, for example, the submitter's attribute information (job title, department, etc.). The automation unit can also select the optimal automation procedure by taking into account the submitter's attribute information. Furthermore, the automation unit can improve the accuracy of automation based on the submitter's attribute information. This enables more appropriate automation by taking into account the submitter's attribute information. Some or all of the above-described processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input the submitter's attribute information data into the generation AI and have the generation AI adjust the automation procedure.

[0081] The automation unit can estimate the user's emotions and adjust the order in which automation results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the automation unit can display important results first. Furthermore, if the user is relaxed, the automation unit can display all results evenly. Furthermore, if the user is in a hurry, the automation unit can display results that need to be displayed quickly first. This allows for more appropriate result display by adjusting the result display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the automation unit can be performed using, for example, an AI, or without an AI. For example, the automation unit can input user emotion data into the generation AI and have the generation AI adjust the display order of the results.

[0082] The automation unit can perform automation while taking into account the geographic distribution of the input information. For example, the automation unit can analyze the geographic distribution of the input information and automate related operations. The automation unit can also select an optimal automation procedure based on the geographic distribution of the input information. Furthermore, the automation unit can improve the accuracy of automation by taking into account the geographic distribution of the input information. This enables more appropriate automation by taking into account the geographic distribution of the input information. Some or all of the above-described processing in the automation unit can be performed using, for example, AI, or can be performed without using AI. For example, the automation unit can input geographic distribution data of the input information to the generation AI and have the generation AI select an automation procedure.

[0083] During automation, the automation unit can improve the accuracy of automation by referring to literature related to the input information. For example, the automation unit can refer to literature related to the input information and select an optimal automation procedure. The automation unit can also improve the accuracy of automation based on literature related to the input information. Furthermore, the automation unit can provide detailed automation procedures by referring to literature related to the input information. This improves the accuracy of automation by referring to related literature. Some or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input literature data related to the input information into the generation AI and have the generation AI select an automation procedure.

[0084] The providing unit can estimate the user's emotions and adjust the display method of the results to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This enables more appropriate result display by adjusting the display method of the results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the results.

[0085] The providing unit can optimize the current content to be provided by referring to past provided data at the time of providing the content. The providing unit can, for example, analyze the past provided data and select the optimal content to be provided. The providing unit can also optimize the current content to be provided based on the past provided data. Furthermore, the providing unit can provide detailed content to be provided by referring to the past provided data. In this way, the current content to be provided can be optimized by referring to the past provided data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the past provided data to a generating AI and cause the generating AI to optimize the current content to be provided.

[0086] The providing unit can apply different providing methods to different categories of input information when providing the information. For example, the providing unit can apply a providing method using natural language processing to text data. The providing unit can also apply a providing method using image analysis to image data. The providing unit can also apply a providing method using voice analysis to voice data. This improves the accuracy of the provided content by applying different providing methods depending on the category of input information. Some or all of the above-described processing by the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input category data of the input information to the generation AI and cause the generation AI to apply the providing method.

[0087] The providing unit can estimate the user's emotions and adjust the importance of the results to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide results with higher importance first. Furthermore, if the user is relaxed, the providing unit can provide all results evenly. Furthermore, if the user is in a hurry, the providing unit can provide results that need to be provided quickly first. This allows for more appropriate results to be provided by adjusting the importance of results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the importance of the results.

[0088] The providing unit can analyze changes in the provided content based on the submission time of the input information at the time of provision. For example, the providing unit can prioritize analysis of the provided content based on input information submitted earlier. The providing unit can also postpone the provided content based on input information submitted later. Furthermore, the providing unit can dynamically analyze changes in the provided content based on the submission time. This enables more appropriate provided content to be provided by analyzing changes in the provided content based on the submission time of the input information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input submission time data of the input information to the generation AI and cause the generation AI to analyze changes in the provided content.

[0089] The provision unit can analyze the content of the provision by referring to market data related to the input information when providing the content. The provision unit can, for example, refer to market data related to the input information and select optimal content of the provision. The provision unit can also optimize the content of the provision based on the market data related to the input information. Furthermore, the provision unit can provide detailed content of the provision by referring to market data related to the input information. By referring to the related market data, the accuracy of the content of the provision is improved. Some or all of the above-described processing in the provision unit can be performed, for example, using AI or without AI. For example, the provision unit can input market data related to the input information to a generation AI and cause the generation AI to analyze the content of the provision. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, automation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text input or voice input from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a JavaScript or Python library. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automates operations using a specific script. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides results using a Chrome extension. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, automation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a JavaScript or Python library. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automates operations using a specific script. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides results using a Chrome extension. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, automation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a JavaScript or Python library. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automates operations using a specific script. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides results using a Chrome extension. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, automation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a JavaScript or Python library. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automates operations using a specific script. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides results using a Chrome extension.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can automatically search for related past data based on the user's input and present it to the user. For example, when a user inputs a specific question, the reception unit can search for similar past questions and their answers and present them to the user. Also, when a user inputs a specific keyword, the reception unit can search for past data related to the keyword and provide it to the user. Furthermore, the reception unit can automatically filter related past data based on the user's input and present the most relevant data preferentially. This allows the user to efficiently obtain information while referring to past data.

[0092] The analysis unit can retrieve information from relevant external data sources based on user input and use it in the analysis. For example, if a user asks a question about a specific topic, the analysis unit can retrieve related information from public databases and APIs on the Internet and use it in the analysis. Also, if a user specifies a specific dataset, external data related to that dataset can be automatically retrieved and incorporated into the analysis. Furthermore, the analysis unit can search for related academic papers and technical literature based on the user's input and use it in the analysis. This improves the accuracy of the analysis and provides richer information.

[0093] The automation unit can automatically schedule and execute related tasks based on user input. For example, if a user inputs a specific task, the automation unit can schedule that task to be executed at the appropriate time. Also, if a user inputs multiple tasks, the automation unit can automatically determine the priority of those tasks and execute them efficiently. Furthermore, the automation unit can automatically group related tasks based on user input and execute them together. This makes it easier for users to manage tasks and work more efficiently.

[0094] The providing unit can automatically generate related visual content based on the user's input and provide it to the user. For example, when a user inputs specific data, the providing unit can automatically generate graphs and charts based on the data and provide them to the user. Also, when a user asks a question about a specific topic, the providing unit can automatically search for images and videos related to the topic and provide them to the user. Furthermore, the providing unit can automatically generate related infographics and presentation slides based on the user's input and provide them to the user. This makes it easier for users to understand information visually and acquire information efficiently.

[0095] The reception unit can automatically generate and send to the user relevant notifications based on the user's input. For example, if the user inputs a specific task, the reception unit can periodically notify the user of the progress of that task. Also, if the user inputs a specific event, the reception unit can automatically generate and send to the user a reminder for that event. Furthermore, the reception unit can automatically generate and send to the user relevant alerts based on the user's input. This allows the user to work efficiently without missing important information.

[0096] The reception unit can estimate the user's emotions and adjust the feedback of the input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide feedback in gentle words to relax the user. Also, if the user is relaxed, the reception unit can provide detailed feedback to deepen understanding of the user. Furthermore, if the user is in a hurry, the reception unit can provide concise and to-the-point feedback to encourage a quick response. In this way, appropriate feedback can be provided according to the user's emotions.

[0097] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, visually easy-to-understand results to help the user understand. If the user is relaxed, the analysis unit can provide detailed analysis results to deepen the user's knowledge. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points to support quick decision-making. This makes it possible to provide appropriate analysis results according to the user's emotions.

[0098] The automation unit can estimate the user's emotions and adjust the automation process based on the estimated user emotions. For example, if the user is feeling stressed, the automation unit can simplify the process to reduce the user's burden. Also, if the user is relaxed, the automation unit can provide a detailed process to deepen the user's understanding. Furthermore, if the user is in a hurry, the automation unit can provide a quick process to support efficient work. In this way, an appropriate automation process can be provided according to the user's emotions.

[0099] The providing unit can estimate the user's emotions and adjust the amount of information to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide the minimum amount of information necessary to reduce the user's burden. Also, if the user is relaxed, the providing unit can provide detailed information to deepen the user's knowledge. Furthermore, if the user is in a hurry, the providing unit can provide concise information that covers the main points to support quick decision-making. This makes it possible to provide appropriate information according to the user's emotions.

[0100] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide visually easy-to-understand graphs and charts to help the user understand. If the user is relaxed, the providing unit can provide detailed text information to deepen the user's knowledge. Furthermore, if the user is in a hurry, the providing unit can provide information in bullet-point format that focuses on the main points to support quick decision-making. In this way, an appropriate information format can be provided according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives input from the user. User input includes text input, voice input, image input, etc. For example, the user can receive input by selecting text on a web page and clicking the extension's button. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit performs the analysis using JavaScript or Python libraries. For example, the analysis unit can visualize data using the JavaScript D3.js library, build a machine learning model and perform data analysis using the Python TensorFlow library, or perform numerical calculations using the Python NumPy library. Step 3: The automation unit automates operations based on the information analyzed by the analysis unit. The automation unit automates operations using specific scripts. For example, it is possible to automate web page operations using Python scripts, automatically execute system commands using shell scripts, or automate browser operations using JavaScript. Step 4: The providing unit provides the results of the operations automated by the automation unit. The providing unit provides the results using a Chrome extension or a Firefox add-on. For example, the providing unit can provide an interface for displaying the results on a browser, or can provide the results in report or graph format.

[0103] 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.

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit; an automation unit that automates operations based on the information analyzed by the analysis unit; a providing unit that provides a result of the operation automated by the automation unit. A system characterized by:

2. The analysis unit Analysis using JavaScript or Python libraries 2. The system of claim 1.

3. The automation unit Automate operations using specific scripts 2. The system of claim 1.

4. The providing unit Provide results using a browser extension 2. The system of claim 1.

5. The reception unit The user selects text on a web page and clicks a button in the extension to accept input.

2. The system of claim 1.

6. The providing unit Retrieve specific data based on the response and provide it to the user 2. The system of claim 1.

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

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the optimal reception method 2. The system of claim 1.

9. The reception unit As input is received, it filters based on the user's current work situation and areas of interest.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1.

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