System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a user request by voice or text; means for analyzing the received request by a voice recognition system or a text analysis system; means including generative artificial intelligence for specifying a necessary action based on an analysis result; means for executing a task using a robotic process automation system for the specified action; and means for reporting an execution result to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Work involves a wide variety of tasks, placing a heavy burden on employees' daily work, creating a need for greater efficiency. Furthermore, the use of different systems and platforms requires manual data entry and management, increasing the risk of errors and duplication. While methods for automating work processes are necessary, particularly in a society facing labor shortages and an aging population, traditional tools have struggled to provide personalized support for users. Therefore, there is a need for methods that enable efficient work execution tailored to user attributes and automation through integration with various systems. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system including: means for receiving a user request via voice or text; means for analyzing the received request using a voice recognition system or a text analysis system; means including a generative artificial intelligence (AI) for identifying necessary actions based on the analysis results; means for executing tasks using a robotic process automation system for the identified actions; and means for reporting the execution results to the user. Furthermore, by including means for proposing tasks based on the analysis results of the user request and means for executing the proposed tasks using the robotic process automation system, a series of tasks can be personalized and semi- or fully automated. Furthermore, by including means for converting the received user request into text using a voice recognition system and means for analyzing the converted text to identify necessary actions, intuitive operation via voice input is also possible.

[0006] The "means for receiving a user request by voice or text" is a mechanism having an interface for a user to input a request in voice or text format and a function for receiving the request.

[0007] A "voice recognition system" is a system that analyzes voice data and converts it into text data.

[0008] A "text analysis system" is a system that analyzes input text data and understands its content.

[0009] "Generative AI" is AI that has the ability to automatically generate processing content based on given information.

[0010] A "robotic process automation system" is a software system that automates business processes based on specific rules.

[0011] The "means for executing a business" is a means having a function for carrying out an actual business process based on a specified action.

[0012] The "means for reporting the execution results to the user" is a mechanism for notifying the user of the results of the executed task.

[0013] The "means for proposing a business" is a mechanism that proposes the most suitable business in consideration of the user's request and user attributes.

[0014] The "means for converting into text using a voice recognition system" is a mechanism for converting received voice data into text format using a voice recognition system. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention specifically realizes a personal AI secretary system that combines generative AI, robotic process automation (RPA), API gateway, and speech recognition and synthesis AI. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's attributes.

[0037] System Overview

[0038] 1. Receiving a request from a user:

[0039] The user sends a request in the form of voice or text. For example, a request might be, "Create today's sales report and register it in the sales management system."

[0040] The terminal receives this request and, if it is voice, converts it into text using a voice recognition system.

[0041] 2. Parse the request:

[0042] The server sends the received text data to the generation AI, which analyzes the input and identifies the necessary actions.

[0043] For example, the actions "Create a daily report" and "Register in SFA (Sales Management System)" are identified.

[0044] 3. Execution of Business:

[0045] The server executes the task based on the specified action using robotic process automation (RPA), which automatically executes the specified business process.

[0046] For example, sales data is entered into a template for creating daily reports, and the data is then registered in the SFA.

[0047] 4. Reporting the results:

[0048] The server passes the results of the business execution to the generation AI, which then creates a natural language text report of the results.

[0049] The device uses voice synthesis AI to convert this report into audio and report it to the user.

[0050] For example, the content might be something like, "A daily report has been created and registered in the sales management system."

[0051] Specific examples

[0052] Example 1: Creating daily reports + SFA automatic input

[0053] User: "Create today's sales report and register it in SFA."

[0054] The terminal converts the voice into text and sends the text "Create today's sales report and register it with the SFA" to the server.

[0055] The server uses the generated AI to analyze the text and identify the actions of "create daily report" and "register SFA."

[0056] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the SFA system's API.

[0057] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered with SFA."

[0058] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0059] Example 2: Meeting schedule adjustment + calendar integration

[0060] User: "Schedule our next team meeting."

[0061] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[0062] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[0063] The server instructs the RPA to use the calendar API to check the participants' free times and set the optimal date.

[0064] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time."

[0065] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0066] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests, thereby reducing the burden on employees and improving work efficiency.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] Users can send requests to their personal AI secretary via voice or text, for example, "Create tomorrow's sales report and register it in the sales management system."

[0070] Step 2:

[0071] The device receives the user's request. If it is a voice request, it uses a voice recognition system to convert the voice into text. For example, "Create tomorrow's sales report and register it in the sales management system."

[0072] Step 3:

[0073] The device sends the converted text data to the server, which then sends the text data as an HTTPS request to the server for analysis.

[0074] Step 4:

[0075] The server passes the received text data to the generation AI, which prepares to analyze the text data.

[0076] Step 5:

[0077] The generative AI analyzes the text and identifies the necessary actions, such as "create a daily report" and "register in the sales management system."

[0078] Step 6:

[0079] Based on the identified action, the server passes the appropriate RPA script to the RPA system, which initiates the business automation process.

[0080] Step 7:

[0081] The RPA executes the "Daily Report Creation" script, automatically generating a daily report based on a pre-defined template and filling in the necessary data.

[0082] Step 8:

[0083] The RPA executes the "Sales Management System Registration" script, and the generated daily report data is automatically registered in the system via the sales management system (SFA) API.

[0084] Step 9:

[0085] The RPA reports the execution results to the server, and sends log data indicating that the daily report has been created and registered in the sales management system.

[0086] Step 10:

[0087] The server passes the execution results to the generation AI, which then generates a report. The generation AI creates a natural language report such as, "A daily report has been created and registered in the sales management system."

[0088] Step 11:

[0089] The server sends the generated report to the terminal, which sends the report as an HTTPS request.

[0090] Step 12:

[0091] The device uses a speech synthesis AI to convert the report into audio, which then generates a voice file saying, "The daily report has been created and registered in the sales management system."

[0092] Step 13:

[0093] The device notifies the user of the generated audio. The audio file is reported to the user through a playback device (such as a speaker).

[0094] Through these specific processing steps, daily tasks based on user requests are automated and efficiently performed.

[0095] Example 1

[0096] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0097] Conventional business automation systems have had difficulty accurately receiving users' voice requests, appropriately analyzing the content of those requests, efficiently processing them, and quickly providing feedback on the results. Therefore, there is a need for a system that integrates speech recognition, text analysis, generative artificial intelligence, robotic process automation, and speech synthesis technologies to consistently automate tasks based on user requests.

[0098] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0099] In this invention, the server includes means for receiving user requests by voice or text, means for analyzing the received requests using a voice recognition system or a text analysis system, means including a generative artificial intelligence for identifying necessary actions based on the analysis results, means for executing tasks using a robotic process automation system for the identified actions, and means for converting the execution results into natural language text using the generative artificial intelligence and vocalizing them using a speech synthesis system to report them to the user. This makes it possible to process user requests accurately and efficiently and quickly provide feedback on the results.

[0100] A "means for receiving a voice or text user request" is a device or software for receiving a voice or text request from a user.

[0101] A "speech recognition system" is a technology or device that receives voice data as input, analyzes it, and converts it into text data.

[0102] A "text analysis system" is a technology or device that analyzes received text, understands its content, and extracts necessary information.

[0103] "Generative AI" refers to artificial intelligence techniques used to analyze text or instructions and identify appropriate actions or generate natural language text.

[0104] A "robotic process automation system" is software or a device that automatically executes business processes and efficiently handles repetitive tasks.

[0105] A "speech synthesis system" is a technology or device that converts text data into voice data and generates artificial voices.

[0106] "Natural language text" refers to text data written in a natural format, such as human speech.

[0107] A "user request" is an instruction or request that a user makes to the system.

[0108] "Analysis results" refer to the results of analysis obtained by a speech recognition system or text analysis system.

[0109] "Business execution results" refer to the results obtained after a robotic process automation system executes a business.

[0110] A "reporting means" is a method or device used to communicate the results of an execution to a user.

[0111] This invention specifically realizes a personal AI secretary system that combines generative artificial intelligence (AI), robotic process automation (RPA), an API gateway, and speech recognition and synthesis AI. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's attributes.

[0112] System Program Overview

[0113] 1. Receiving a request from a user:

[0114] Users can send requests in voice or text format, such as "Create today's sales report and register it in the sales management system."

[0115] The device receives this request and, if it is voice, converts it into text using speech recognition software, specifically Google Cloud Speech-to-Text.

[0116] 2. Analysis of the request:

[0117] The device sends the received text data to a server, which then sends the text to a generator AI for analysis. OpenAI GPT-4 is sometimes used as the generator AI.

[0118] The generative AI analyzes the input and identifies the necessary actions, such as "create a daily report" and "register in the sales management system (SFA)."

[0119] 3. Execution of Business:

[0120] The server executes the identified actions using robotic process automation (RPA), an example of which is UiPath.

[0121] Sales data is entered into a template for creating daily reports, and the data is registered in a sales management system (e.g., Salesforce).

[0122] 4. Reporting the results:

[0123] The server passes the results of the work execution to the generation AI, which generates a result report in natural language text.

[0124] The device will then convert the report into voice using a speech synthesis AI, such as Amazon Polly.

[0125] For example, a report may be made stating, "A daily report has been created and registered in the sales management system."

[0126] Specific examples

[0127] Example 1: Creating daily reports + SFA automatic input

[0128] User: "Create today's sales report and register it in SFA."

[0129] The terminal converts the voice into text and sends the text "Create today's sales report and register it with the SFA" to the server.

[0130] The server analyzes the text using the generated AI (OpenAI GPT-4) and identifies the actions of "create daily report" and "register SFA."

[0131] The server instructs the RPA (UiPath) to automatically create daily reports and execute the process of registering the daily reports in the SFA (such as Salesforce's API).

[0132] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered with SFA."

[0133] The device uses a speech synthesis AI (Amazon Polly) to convert the report into audio and notify the user.

[0134] Example 2: Meeting schedule adjustment + calendar integration

[0135] User: "Schedule our next team meeting."

[0136] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[0137] The server analyzes the text using generative AI (OpenAI GPT-4) and identifies the action "schedule a meeting."

[0138] The server instructs RPA (Automation Anywhere) to use a calendar API (such as Google Calendar API) to check the participants' free time and perform the process of setting the optimal date.

[0139] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time."

[0140] The device uses a speech synthesis AI (Amazon Polly) to convert the report into audio and notify the user.

[0141] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests, thereby enabling users to automate and streamline their tasks while saving time and effort.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] The user sends a request in voice or text format. For example, a request might be made to "create today's sales report and register it in the sales management system." The device receives this request. In the case of voice format, the device converts the voice to text using Google Cloud Speech-to-Text. The input here is voice data, and the output is the converted text data.

[0145] Specific behavior:

[0146] The user speaks to their smartphone, saying, "Create today's sales report and register it with SFA."

[0147] The device records the audio data and sends it to the Google Cloud Speech-to-Text API.

[0148] Google Cloud Speech-to-Text analyzes the audio data and generates text data such as "Create today's sales report and register it with SFA."

[0149] Step 2:

[0150] The device sends the converted text data to the server. The server passes the received text data to the generation AI (OpenAI GPT-4) for analysis. The generation AI analyzes the text content and identifies the necessary actions. The input here is the text data, and the output is a list of analyzed actions.

[0151] Specific behavior:

[0152] The terminal sends the text data "Create today's sales report and register it in the SFA" to the server via an HTTP request.

[0153] The server passes the text data to the generation AI along with a prompt saying, "Please analyze the request and identify the required action."

[0154] The generation AI analyzes the text content and identifies the actions "create daily report" and "register SFA."

[0155] Step 3:

[0156] The server executes the tasks based on the specified actions using Robotic Process Automation (RPA: UiPath). RPA automates the specified business process. The input here is a list of actions, and the output is the results of the executed tasks.

[0157] Specific behavior:

[0158] The server instructs the RPA (UiPath) API to create daily reports and register them in the SFA.

[0159] RPA opens the daily report template and automatically enters sales data into the template.

[0160] The daily reports completed by RPA are registered in the sales management system (such as Salesforce API).

[0161] Step 4:

[0162] The server passes the business execution results received from the RPA to the generation AI, which then generates a result report in natural language text. Here, the input is the business execution results, and the output is natural language text.

[0163] Specific behavior:

[0164] The server passes the prompt message to the generation AI: "Please explain the process of creating a daily report and registering it with SFA."

[0165] The generation AI generates a report stating, "A daily report has been created and registered with SFA."

[0166] Step 5:

[0167] The device receives the generated report, converts it into speech using speech synthesis software (Amazon Polly), and reports it to the user. The input here is natural language text, and the output is speech data.

[0168] Specific behavior:

[0169] The terminal receives the report "The daily report has been created and registered in the sales management system."

[0170] The device inputs the report into Amazon Polly and generates voice data.

[0171] The terminal plays back the voiced report and notifies the user that "the daily report has been created and registered in the sales management system."

[0172] (Application example 1)

[0173] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0174] Improving work efficiency and reducing costs are key challenges in modern industrial facilities. However, many tasks, such as communicating work instructions, managing maintenance schedules, and managing inventory, are still performed manually, which can be error-prone and time-consuming. Furthermore, these tasks require advanced skills, leading to labor shortages and increased training costs. The purpose of this invention is to solve these challenges and automate and streamline work in industrial facilities.

[0175] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0176] In this invention, the server includes: means for receiving user requests by voice or text; means for analyzing the received requests using a voice recognition system or a text analysis system; means including a generative artificial intelligence for identifying necessary actions based on the analysis results; means for executing tasks using a robotic process automation system for the identified actions; means for reporting the execution results to the user; means for receiving voice instructions at the industrial facility and automatically executing tasks by industrial robots based on the instructions; means for automatically creating a maintenance schedule for industrial equipment and executing necessary maintenance tasks based on the schedule; and means for automatically managing inventory within the industrial facility and automatically placing orders when inventory is low, thereby enabling improved work efficiency and cost reduction within the industrial facility.

[0177] A "voice recognition system" is a system that converts voice into a digital signal, analyzes the content, and converts it into text format.

[0178] A "text analysis system" is a system that analyzes textual information and understands its meaning and context.

[0179] "Generative AI" is AI that identifies necessary actions based on given data and generates appropriate responses and work instructions based on that content.

[0180] A "robotic process automation system" is a system that uses software robots to automate routine business processes.

[0181] An "industrial facility" is an industrial facility where manufacturing, processing, etc. is carried out, including factories and plants.

[0182] An "industrial robot" is a robot designed to perform tasks automatically in an industrial facility.

[0183] A "maintenance schedule" is a planned schedule for maintaining, inspecting, and repairing equipment and facilities.

[0184] "Inventory management" is the process of monitoring and properly managing the quantity and condition of inventory, such as parts and raw materials, used within an industrial facility.

[0185] "Placing an order" is the act of ordering needed goods or services from a supplier.

[0186] "Analysis results" are the results of interpretation and understanding of information obtained via a speech recognition system or text analysis system.

[0187] "Business proposal" is the act of proposing to a user the optimal way to execute a business based on the analysis results.

[0188] "Automation" refers to a state in which machines and systems operate autonomously with reduced human intervention.

[0189] "Result reporting" is the act of notifying the user of the results of an executed task or process.

[0190] To realize the present invention, the following hardware and software are used.

[0191] Hardware

[0192] server

[0193] Devices (smartphones, tablets, PCs, etc.)

[0194] industrial robots

[0195] Voice input device

[0196] Internet-connected devices

[0197] software

[0198] Speech recognition systems (e.g., Google Cloud Speech-to-Text)

[0199] Text analysis systems (e.g. NLU / NLP engines)

[0200] Generative artificial intelligence (e.g. OpenAI GPT-4)

[0201] Robotic process automation systems (e.g., UiPath)

[0202] API Gateway (e.g. AWS API Gateway)

[0203] Text-to-speech systems (e.g., Amazon Polly)

[0204] Specific Embodiments of the Invention

[0205] 1. Receiving a user request

[0206] The user makes a request by voice or text, which is received by the device. In the case of voice input, the request is converted into text by a speech recognition system. This text data is then sent to the server for analysis.

[0207] 2. Request Analysis

[0208] The server analyzes the received text data using a generative AI model. The analysis results in the identification of the necessary action. For example, in response to a request to "check the next maintenance schedule," the action of creating a maintenance schedule is identified.

[0209] 3. Execution of Business

[0210] Based on the identified actions, the server executes tasks using the robotic process automation system, such as creating maintenance schedules, issuing work instructions for industrial robots, and ordering inventory shortages. For example, the server sends an instruction to an industrial robot to "perform a specific task" through the API gateway.

[0211] 4. Reporting the results

[0212] The server passes the results of the task execution to the generative AI model, which then creates a natural language text report of the results. The report is then converted into voice by a speech synthesis system and notified to the user via the device. For example, a report such as "The next maintenance will be on October 2nd" is made audibly.

[0213] Examples of concrete examples and prompts

[0214] Examples:

[0215] 1. User Request: "Check the next maintenance schedule"

[0216] 2. Execution result: The server creates a maintenance schedule and reports, "The next maintenance will be on October 2nd."

[0217] Example prompt sentence:

[0218] 1. "Check the next maintenance schedule"

[0219] 2. "Check availability"

[0220] 3. "Please give instructions to the industrial robot."

[0221] In this way, by using the appropriate hardware and software at each step, it is possible to automate and streamline operations within an industrial facility.

[0222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0223] Step 1:

[0224] The user makes a request by voice or text. The device receives this request. For example, the user may say, "Check the next maintenance schedule." The device captures this voice and passes it to the voice recognition system.

[0225] Input: User's voice or text request

[0226] Output: Request converted into text by the speech recognition system

[0227] What it does: The device uses a microphone to capture the user's voice and then sends the voice data to a speech recognition system (e.g., Google Cloud Speech-to-Text) to convert it into text.

[0228] Step 2:

[0229] The terminal receives the request converted into text from the voice recognition system and sends it to the server.

[0230] Input: Text request from speech recognition system

[0231] Output: Text request received by the server

[0232] Specific operation: The terminal sends text data to the server using an HTTP request or the like.

[0233] Step 3:

[0234] The server then passes the received text request to a generative AI model for analysis. The generative AI model (e.g., OpenAI GPT-4) analyzes the text and identifies the required action.

[0235] Input: Text request details

[0236] Output: List of identified actions

[0237] Specific operation: The server inputs text data into the generative AI model, which then performs contextual analysis and keyword matching to list appropriate actions.

[0238] Step 4:

[0239] Based on the identified actions, the server issues instructions to a robotic process automation system (e.g., UiPath), which then automatically performs the necessary tasks, such as creating and executing maintenance schedules.

[0240] Input: A list of identified actions

[0241] Output: The results of the work performed

[0242] What it does: The server sends API requests to the robotic process automation system, which then executes automated scripts to create maintenance schedules and instruct industrial robots.

[0243] Step 5:

[0244] The server receives the execution results and passes them to a generative AI model to generate natural language text reporting the results.

[0245] Input: Result of the work performed

[0246] Output: Natural language text of the results report

[0247] How it works: The server receives the results from the robotic process automation system and inputs them into the generative AI model, which then generates text in natural language based on the results.

[0248] Step 6:

[0249] The server passes the generated report to a speech synthesis system (e.g., Amazon Polly) to convert it into voice, and the device notifies the user of the voiced report.

[0250] Input: Natural language text of the result report

[0251] Output: Audio report to notify the user

[0252] Specific operation: The server inputs the text from the generative AI model into the speech synthesis system to generate voice data, which the device plays back and reports to the user.

[0253] This series of steps automates and streamlines the management of work orders and maintenance schedules within industrial facilities.

[0254] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0255] This invention specifically realizes a personal AI secretary system that combines generative AI, robotic process automation (RPA), API gateway, speech recognition / synthesis AI, and an emotion engine. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's emotional state.

[0256] System Overview

[0257] 1. Receiving a request from a user:

[0258] The user sends a request in the form of voice or text. For example, a request might be, "Create tomorrow's sales report and register it in the sales management system."

[0259] The terminal receives this request and, if it is voice, converts it into text using a voice recognition system.

[0260] 2. Emotional state analysis:

[0261] The device uses an emotion engine to analyze the user's emotional state from the voice or text received, for example, to determine whether the user is stressed or relaxed.

[0262] 3. Parse the request:

[0263] The server passes the received text data to the generation AI, which analyzes the input and identifies the required action.

[0264] For example, the actions "create daily report" and "register sales management system" are identified.

[0265] 4. Business proposal:

[0266] The server then makes more appropriate work suggestions based on the user's emotional state and the analysis results. For example, if the user is feeling stressed, it will suggest simplifying the work.

[0267] 5. Execution of Business:

[0268] The server executes the task based on the specified action using robotic process automation (RPA), which automatically executes the specified business process.

[0269] For example, sales data is entered into a template for creating daily reports, and the data is then registered in a sales management system.

[0270] 6. Reporting results:

[0271] The server passes the results of the work execution to the generation AI, which then creates a natural language text report of the results. The report is generated with emotional consideration according to the situation.

[0272] The device uses speech synthesis AI to convert this report into audio and report it to the user. For example, it might say something like, "Based on your request, we have created a daily report and registered it in the sales management system. Don't worry, everything is going smoothly."

[0273] Specific examples

[0274] Example 1: Creating daily reports + automatic input to the sales management system

[0275] User: "Create tomorrow's sales report and register it in the sales management system."

[0276] The terminal converts the voice into text and sends the text "Create tomorrow's sales report and register it in the sales management system" to the server.

[0277] The server uses generation AI to analyze the text and identify the actions of "create daily report" and "register in sales management system."

[0278] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[0279] The server makes emotion-based business suggestions, for example, simplifying templates for creating daily reports.

[0280] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the sales management system's API.

[0281] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[0282] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0283] Example 2: Meeting schedule adjustment + calendar integration

[0284] User: "Schedule our next team meeting."

[0285] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[0286] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[0287] The device uses an emotion engine to analyze the user's emotional state and detect when the user is relaxed.

[0288] The server instructs the RPA to use the calendar API to check the free time of all participants and set the optimal date.

[0289] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[0290] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0291] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests and taking into account the user's emotional state, thereby reducing the burden on employees and improving work efficiency.

[0292] The processing flow will be explained below.

[0293] Example 1: Creating daily reports + automatic input into sales management system

[0294] Step 1:

[0295] The user requests, "Create tomorrow's sales report and register it in the sales management system."

[0296] Step 2:

[0297] The device receives the user's request. If the request is received by voice, it uses a voice recognition system to convert the voice into text.

[0298] Step 3:

[0299] The device converts the speech into text and sends it to the server, saying, "Create tomorrow's sales report and register it in the sales management system." This time, the data is sent via an HTTPS request.

[0300] Step 4:

[0301] The device uses an emotion engine to analyze the user's emotional state from the text data, and detects from the analysis results that the user is feeling stressed.

[0302] Step 5:

[0303] The server passes the received text data to the generation AI, which analyzes the text and identifies the actions of "create daily report" and "register in sales management system."

[0304] Step 6:

[0305] The server takes into account the user's emotional state and optimizes task suggestions. For example, if the user is feeling stressed, it will suggest ways to simplify daily report creation.

[0306] Step 7:

[0307] The server issues instructions to the Robotic Process Automation (RPA) system based on the identified actions. The necessary information is embedded in the template for "daily report creation" and the daily report is automatically generated.

[0308] Step 8:

[0309] The daily reports generated by RPA are automatically registered through the sales management system's API.

[0310] Step 9:

[0311] The RPA returns the execution results to the server as a log, indicating that the daily report has been created and registered in the sales management system.

[0312] Step 10:

[0313] The server passes the execution results to the generation AI, which then generates a report in natural language, such as, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[0314] Step 11:

[0315] The server sends the generated report to the device via an HTTPS request.

[0316] Step 12:

[0317] The device uses speech synthesis AI to convert the report into audio, generating a voice file that says, "The daily report has been created and registered in the sales management system. Things are progressing smoothly without any stress."

[0318] Step 13:

[0319] The device reports the generated audio to the user through a playback device (such as a speaker).

[0320] Example 2: Meeting schedule adjustment + calendar integration

[0321] Step 1:

[0322] A user requests, "Schedule our next team meeting."

[0323] Step 2:

[0324] The device receives the user's request. If the request is received by voice, it uses a voice recognition system to convert the voice into text.

[0325] Step 3:

[0326] The device sends the converted text "Schedule the next team meeting" to the server via an HTTPS request.

[0327] Step 4:

[0328] The device uses an emotion engine to analyze the user's emotional state from the text data, and detects from the analysis results that the user is relaxed.

[0329] Step 5:

[0330] The server passes the received text data to the generation AI, which analyzes the text and identifies the action to "schedule a meeting."

[0331] Step 6:

[0332] The server takes into account the user's emotional state and optimizes the task suggestions, for example, if the user is relaxed, it will suggest detailed schedule adjustments.

[0333] Step 7:

[0334] The server issues instructions to a robotic process automation (RPA) system based on the identified actions, and uses a calendar API to check the availability of all participants and set the optimal date.

[0335] Step 8:

[0336] RPA automatically schedules meetings through a calendar API.

[0337] Step 9:

[0338] The RPA returns the execution result to the server as a log, indicating that the meeting schedule has been arranged.

[0339] Step 10:

[0340] The server passes the execution results to the generation AI, which then generates a report in natural language, such as "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[0341] Step 11:

[0342] The server sends the generated report to the device via an HTTPS request.

[0343] Step 12:

[0344] The device uses speech synthesis AI to convert the report into audio, generating an audio file that says, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[0345] Step 13:

[0346] The device reports the generated audio to the user through a playback device (such as a speaker).

[0347] Through these specific processing steps, the task requested by the user is efficiently performed while taking into account the emotional state.

[0348] Example 2

[0349] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0350] Conventional voice assistant systems and automated systems often simply execute tasks based on user requests without considering the user's emotional state or work efficiency. As a result, they are unable to reduce the user's burden and stress, and their improvement in work efficiency is limited. Furthermore, if the execution result reports are not emotionally sensitive, they fail to provide sufficient satisfaction to the user.

[0351] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0352] In this invention, the server includes: means for receiving a user request by voice or text; means for analyzing the received request using a voice recognition system or a text analysis system; means including an emotion engine for analyzing the analysis results and the user's emotional state; means including generative artificial intelligence for identifying necessary actions based on the analysis results; means for executing tasks using a robotic process automation system for the identified actions; and means for converting the execution results into natural language text using the generative artificial intelligence and reporting them to the user using a speech synthesis system. This enables tasks to be proposed and executed taking into consideration the user's emotional state, thereby reducing the user's burden and improving work efficiency.

[0353] A "voice or text user request" is an action in which a user sends an instruction or request to the system in voice or text form.

[0354] A "speech recognition system" is a technique or device for converting voice input into text data.

[0355] A "text analysis system" is a technology or device for analyzing text data to understand its meaning and structure.

[0356] An "emotion engine" is a technique or device for analyzing data received from a user to identify their emotional state.

[0357] "Generative AI" is an AI technology that analyzes and generates data based on input data.

[0358] A "robotic process automation system" is a system that automatically carries out specified business processes.

[0359] "Natural language text" is text written in natural language that is generated by a computer system.

[0360] A "speech synthesis system" is a technology or device for converting text data into speech.

[0361] "Business proposal" is the act of proposing business optimization and procedure improvements based on the user's request and emotional state.

[0362] MODE FOR CARRYING OUT THE INVENTION

[0363] This invention specifically realizes a personal AI secretary system that combines generative artificial intelligence (generative AI), robotic process automation (RPA), an API gateway, a speech recognition and synthesis system, and an emotion engine. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's emotional state.

[0364] System Overview

[0365] First, the user issues a request in the form of voice or text. For example, a request might be, "Create tomorrow's sales report and register it in the sales management system." The device receives this request, and if it is voice, it converts it into text using a voice recognition system (e.g., voice recognition API). From the received voice or text, the device uses an emotion engine (e.g., emotion analysis API) to analyze the user's emotional state. For example, it determines whether the user is feeling stressed or relaxed.

[0366] Next, the server passes the text data to a generation AI (e.g., GPT-4), which analyzes the input and identifies the necessary actions. For example, the actions "Create a daily report" and "Register in a sales management system" may be identified. The server then makes more appropriate work suggestions based on the user's emotional state and the analysis results. For example, if the user is feeling stressed, it will make suggestions to simplify the work.

[0367] The server executes the task using robotic process automation (RPA) (e.g., RPA tool) based on the specified action. RPA automatically executes the specified business process. For example, sales data is entered into a template for creating daily reports, and the data is registered in a sales management system (e.g., business management system API).

[0368] Finally, the server passes the results of the work execution to the generation AI, which then creates a natural language text report of the results. The report is generated with emotional considerations depending on the situation. The device then converts this report into voice using a speech synthesis system (e.g., speech synthesis API) and reports it to the user. For example, it might say, "Based on your request, a daily report has been created and registered in the sales management system. Don't worry, everything is going smoothly."

[0369] Specific examples

[0370] Example 1: Creating daily reports + automatic input to the sales management system

[0371] User: "Create tomorrow's sales report and register it in the sales management system."

[0372] The terminal converts the voice into text and sends the text "Create tomorrow's sales report and register it in the sales management system" to the server.

[0373] The server uses generation AI to analyze the text and identify the actions of "create daily report" and "register in sales management system."

[0374] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[0375] The server makes emotion-based business suggestions, for example, simplifying templates for creating daily reports.

[0376] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the sales management system's API.

[0377] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[0378] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0379] Example 2: Meeting schedule adjustment + calendar integration

[0380] User: "Schedule our next team meeting."

[0381] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[0382] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[0383] The device uses an emotion engine to analyze the user's emotional state and detect when the user is relaxed.

[0384] The server instructs the RPA to use a calendar API (e.g., calendar management system API) to check the free time of all participants and set the optimal date.

[0385] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[0386] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0387] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests and taking into account the user's emotional state, thereby reducing the burden on employees and improving work efficiency.

[0388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0389] Step 1:

[0390] The user sends a request by voice or text.

[0391] Specifically, the user inputs something like "Create tomorrow's sales report and register it in the sales management system" by voice or text. The input data is an audio file or a text file.

[0392] Step 2:

[0393] The terminal receives a voice or text request from the user.

[0394] In this step, the input is the audio file or text file from step 1, and the output is the audio or text data stored on the device. Specifically, data is received using a microphone or chat interface and temporarily stored.

[0395] Step 3:

[0396] The device uses a voice recognition system to convert the speech into text.

[0397] In the conversion process, the input is audio data and the output is text data. Specifically, a speech recognition system (e.g., a speech recognition API) is used to analyze the audio file and convert it into text.

[0398] Step 4:

[0399] The text data received by the device is sent to the emotion engine for analysis.

[0400] In this step, the input is text data and the output is data about the user's emotional state. Specifically, an emotion analysis API is used to analyze the user's emotions from the text data and identify emotional states such as "stress" or "relaxation."

[0401] Step 5:

[0402] The server sends the text data to the generation AI for analysis and identifies the necessary actions.

[0403] The input is text data and emotional state data, and the output is data that specifies actions. Specifically, the prompt sentence "Create tomorrow's sales report and register it in the sales management system" is input to the generation AI (e.g., GPT-4), and the actions "Create daily report" and "Register in the sales management system" are specified.

[0404] Step 6:

[0405] The server integrates the emotion data and action data and makes business suggestions based on the user's emotions.

[0406] The input is emotion data and action data, and the output is a specific business proposal. For example, the server generates a specific business proposal such as "Suggest simplifying templates for users with high stress levels."

[0407] Step 7:

[0408] The server executes the task using robotic process automation (RPA) based on the identified actions.

[0409] The input is the specified action data, and the output is the result of the business execution. Specifically, sales data is entered into a daily report creation template using an RPA tool (e.g., UiPath), and the data is registered via the sales management system's API.

[0410] Step 8:

[0411] The server passes the results of the work execution to the generation AI, which then creates a result report.

[0412] The input is the data on the results of the work execution, and the output is a report of the results in natural language. Using a generative AI (e.g., GPT-4), a report such as "A daily report has been created and registered in the sales management system. Things are progressing without stress" is generated.

[0413] Step 9:

[0414] The device passes the generated text to a speech synthesis system to convert it into speech.

[0415] The input is natural language text and the output is audio data. Specifically, the report text is converted into audio using a speech synthesis API.

[0416] Step 10:

[0417] The terminal reports the audio data to the user.

[0418] The output is an audio report that tells the user, "Based on your request, a daily report has been created and registered in the sales management system. Don't worry, everything is going smoothly."

[0419] (Application example 2)

[0420] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0421] This invention relates to a personal AI secretary system specialized for security services, and aims to automate and streamline security-related tasks while taking into account the user's emotional state. Conventional systems perform tasks uniformly without considering the user's emotional state, which can cause anxiety and stress to the user. Furthermore, the reporting of execution results is mechanical, which does not fully satisfy the user's psychological satisfaction. There is a need to solve these problems.

[0422] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user request by voice or text, means for analyzing the received request using a voice recognition system or a text analysis system, and means including a generation AI for identifying a required action based on the analysis results. This enables a user to send a request by voice or text, and the system to automatically identify appropriate security-related tasks based on the content of the request and execute them taking into account the user's emotional state.

[0423] The system also includes a means for executing a task using a robotic process automation system for the identified action, a means for reporting the execution result to a user, an emotion engine that analyzes the user's emotional state and proposes a task based on the analyzed emotional state, and a means for executing the proposed task using the robotic process automation system. This enables security tasks to be executed and reported while taking the user's emotional state into consideration, thereby reducing the user's mental stress and increasing their satisfaction.

[0424] A "voice recognition system" refers to technology that converts voice information into text data.

[0425] A "text analysis system" refers to a technology that analyzes text data and performs processes such as semantic analysis and document classification.

[0426] "Generative AI" refers to artificial intelligence technology that analyzes user requests and generates appropriate actions and suggestions.

[0427] "Robotic process automation system" refers to a software robot that automatically executes a specified business process.

[0428] An "emotion engine" is a system that analyzes a user's emotional state from their voice and text and determines their state of stress, relaxation, etc.

[0429] A "speech synthesis system" refers to a technology that converts text data into speech to generate synthetic speech.

[0430] "Natural language text" refers to text data in the form of language used by people in everyday conversation and writing.

[0431] This invention provides a personal AI secretary system specialized for security services, and specifically realizes a system that automates and efficiently executes security-related tasks while taking into account the user's emotional state.

[0432] System configuration and operation

[0433] The system mainly consists of the following components:

[0434] 1. A device that receives user requests via voice or text

[0435] The terminal uses a speech recognition system (for example, Google Speech Recognition API) to convert the speech into text data.

[0436] 2. A server that analyzes the received request using a voice recognition system or text analysis system.

[0437] The server uses a generative AI (e.g., OpenAI's GPT-3) to analyze the received text data and identify the necessary actions.

[0438] 3. Emotion engine that analyzes emotional states

[0439] The analysis system determines emotions from the user's voice or text data, for example using a proprietary emotion engine to determine whether the user is stressed or relaxed.

[0440] 4. A server that executes tasks using a robotic process automation system for the identified actions.

[0441] Identified actions are then executed through robotic process automation (RPA) systems, for example to perform security-related tasks such as increasing security camera monitoring or activating alarm systems.

[0442] 5. Server that converts the execution results into natural language text using generation AI

[0443] The execution results are converted into natural language text by the generative AI and reported to the user.

[0444] 6. A device that converts the converted text into speech using a speech synthesis system

[0445] The text data sent from the server is converted into voice using a speech synthesis system (e.g., Pyttsx3) and reported to the user through the terminal.

[0446] Specific examples

[0447] Example 1: Request for increased security at home

[0448] User: "Tighten up the security at home tonight."

[0449] The device converts the speech into text and sends the text "Enhance home security tonight" to the server.

[0450] The server uses generative AI to analyze the text and identify the action to "strengthen security systems."

[0451] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[0452] The server makes business suggestions based on emotions, such as suggesting strengthening surveillance camera monitoring or activating an alarm system.

[0453] The server instructs the RPA to execute the process of strengthening the security system.

[0454] The server passes the execution results to the generation AI, which generates a report saying, "Security has been strengthened. Everything is going well. Rest in peace tonight."

[0455] The terminal uses a speech synthesis system to convert the report into voice and notify the user.

[0456] Prompt Sentence Examples

[0457] "User Request: Increase security at home tonight. Please identify the necessary security tasks."

[0458] In this way, the present invention provides for the automatic execution and efficient processing of security tasks based on user requests and taking into account emotional state.

[0459] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0460] Step 1:

[0461] The user inputs a request by voice or text. The input voice data is a specific request such as "Please tighten the security at my house tonight." This data is input into the voice recognition system.

[0462] Step 2:

[0463] The device uses a voice recognition system (e.g., Google Speech Recognition API) to convert the voice data into text data. The voice data, "Please strengthen the security of my home tonight," is output as text data.

[0464] Step 3:

[0465] The server inputs text data into a generative AI (e.g., OpenAI GPT-3) for analysis. The server inputs the prompt "User request: Please strengthen the security of my home tonight. Please identify the necessary security tasks" into the generative AI model and obtains the analysis result. The identified action, "Strengthen the security system," is output as the analysis result.

[0466] Step 4:

[0467] The device uses an emotion engine to analyze the user's emotional state. Text data is input into the emotion engine, which determines whether the user is feeling stressed or relaxed. The analysis result is output as "stressed."

[0468] Step 5:

[0469] The server makes business proposals based on the analysis results and the emotional state. Using a generative AI model, the server generates proposals appropriate to the emotional state. For example, specific proposals such as "strengthen surveillance camera monitoring" or "activate the alarm system" are output.

[0470] Step 6:

[0471] The server uses a robotic process automation (RPA) system to execute the proposed security tasks. The server inputs instruction data such as "strengthen surveillance camera monitoring" or "activate the alarm system" into the RPA and obtains the execution results. The execution result is output as a message that the security system has been strengthened.

[0472] Step 7:

[0473] The server uses generative AI to convert the execution results into natural language text. The execution result data is input into the generative AI model, and a report is generated that reads, "Security has been strengthened. Everything is going well. Rest easy tonight."

[0474] Step 8:

[0475] The device uses a speech synthesis system (e.g., Pyttsx3) to convert the generated report into voice and notify the user. The generated report text is input into the speech synthesis system, output as voice data, and reported to the user.

[0476] 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.

[0477] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0478] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0479] [Second embodiment]

[0480] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0481] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0482] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0483] 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.

[0484] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0485] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0486] 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.

[0487] 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.

[0488] 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 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.

[0489] 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.

[0490] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0491] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0492] This invention specifically realizes a personal AI secretary system that combines generative AI, robotic process automation (RPA), API gateway, and speech recognition and synthesis AI. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's attributes.

[0493] System Overview

[0494] 1. Receiving a request from a user:

[0495] The user sends a request in the form of voice or text. For example, a request might be, "Create today's sales report and register it in the sales management system."

[0496] The terminal receives this request and, if it is voice, converts it into text using a voice recognition system.

[0497] 2. Parse the request:

[0498] The server sends the received text data to the generation AI, which analyzes the input and identifies the necessary actions.

[0499] For example, the actions "Create a daily report" and "Register in SFA (Sales Management System)" are identified.

[0500] 3. Execution of Business:

[0501] The server executes the task based on the specified action using robotic process automation (RPA), which automatically executes the specified business process.

[0502] For example, sales data is entered into a template for creating daily reports, and the data is then registered in the SFA.

[0503] 4. Reporting the results:

[0504] The server passes the results of the business execution to the generation AI, which then creates a natural language text report of the results.

[0505] The device uses voice synthesis AI to convert this report into audio and report it to the user.

[0506] For example, the content might be something like, "A daily report has been created and registered in the sales management system."

[0507] Specific examples

[0508] Example 1: Creating daily reports + SFA automatic input

[0509] User: "Create today's sales report and register it in SFA."

[0510] The terminal converts the voice into text and sends the text "Create today's sales report and register it with the SFA" to the server.

[0511] The server uses the generated AI to analyze the text and identify the actions of "create daily report" and "register SFA."

[0512] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the SFA system's API.

[0513] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered with SFA."

[0514] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0515] Example 2: Meeting schedule adjustment + calendar integration

[0516] User: "Schedule our next team meeting."

[0517] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[0518] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[0519] The server instructs the RPA to use the calendar API to check the participants' free times and set the optimal date.

[0520] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time."

[0521] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0522] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests, thereby reducing the burden on employees and improving work efficiency.

[0523] The processing flow will be explained below.

[0524] Step 1:

[0525] Users can send requests to their personal AI secretary via voice or text, for example, "Create tomorrow's sales report and register it in the sales management system."

[0526] Step 2:

[0527] The device receives the user's request. If it is a voice request, it uses a voice recognition system to convert the voice into text. For example, "Create tomorrow's sales report and register it in the sales management system."

[0528] Step 3:

[0529] The device sends the converted text data to the server, which then sends the text data as an HTTPS request to the server for analysis.

[0530] Step 4:

[0531] The server passes the received text data to the generation AI, which prepares to analyze the text data.

[0532] Step 5:

[0533] The generative AI analyzes the text and identifies the necessary actions, such as "create a daily report" and "register in the sales management system."

[0534] Step 6:

[0535] Based on the identified action, the server passes the appropriate RPA script to the RPA system, which initiates the business automation process.

[0536] Step 7:

[0537] The RPA executes the "Daily Report Creation" script, automatically generating a daily report based on a pre-defined template and filling in the necessary data.

[0538] Step 8:

[0539] The RPA executes the "Sales Management System Registration" script, and the generated daily report data is automatically registered in the system via the sales management system (SFA) API.

[0540] Step 9:

[0541] The RPA reports the execution results to the server, and sends log data indicating that the daily report has been created and registered in the sales management system.

[0542] Step 10:

[0543] The server passes the execution results to the generation AI, which then generates a report. The generation AI creates a natural language report such as, "A daily report has been created and registered in the sales management system."

[0544] Step 11:

[0545] The server sends the generated report to the terminal, which sends the report as an HTTPS request.

[0546] Step 12:

[0547] The device uses a speech synthesis AI to convert the report into audio, which then generates a voice file saying, "The daily report has been created and registered in the sales management system."

[0548] Step 13:

[0549] The device notifies the user of the generated audio. The audio file is reported to the user through a playback device (such as a speaker).

[0550] Through these specific processing steps, daily tasks based on user requests are automated and efficiently performed.

[0551] Example 1

[0552] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0553] Conventional business automation systems have had difficulty accurately receiving users' voice requests, appropriately analyzing the content of those requests, efficiently processing them, and quickly providing feedback on the results. Therefore, there is a need for a system that integrates speech recognition, text analysis, generative artificial intelligence, robotic process automation, and speech synthesis technologies to consistently automate tasks based on user requests.

[0554] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0555] In this invention, the server includes means for receiving user requests by voice or text, means for analyzing the received requests using a voice recognition system or a text analysis system, means including a generative artificial intelligence for identifying necessary actions based on the analysis results, means for executing tasks using a robotic process automation system for the identified actions, and means for converting the execution results into natural language text using the generative artificial intelligence and vocalizing them using a speech synthesis system to report them to the user. This makes it possible to process user requests accurately and efficiently and quickly provide feedback on the results.

[0556] A "means for receiving a voice or text user request" is a device or software for receiving a voice or text request from a user.

[0557] A "speech recognition system" is a technology or device that receives voice data as input, analyzes it, and converts it into text data.

[0558] A "text analysis system" is a technology or device that analyzes received text, understands its content, and extracts necessary information.

[0559] "Generative AI" refers to artificial intelligence techniques used to analyze text or instructions and identify appropriate actions or generate natural language text.

[0560] A "robotic process automation system" is software or a device that automatically executes business processes and efficiently handles repetitive tasks.

[0561] A "speech synthesis system" is a technology or device that converts text data into voice data and generates artificial voices.

[0562] "Natural language text" refers to text data written in a natural format, such as human speech.

[0563] A "user request" is an instruction or request that a user makes to the system.

[0564] "Analysis results" refer to the results of analysis obtained by a speech recognition system or text analysis system.

[0565] "Business execution results" refer to the results obtained after a robotic process automation system executes a business.

[0566] A "reporting means" is a method or device used to communicate the results of an execution to a user.

[0567] This invention specifically realizes a personal AI secretary system that combines generative artificial intelligence (AI), robotic process automation (RPA), an API gateway, and speech recognition and synthesis AI. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's attributes.

[0568] System Program Overview

[0569] 1. Receiving a request from a user:

[0570] Users can send requests in voice or text format, such as "Create today's sales report and register it in the sales management system."

[0571] The device receives this request and, if it is voice, converts it into text using speech recognition software, specifically Google Cloud Speech-to-Text.

[0572] 2. Analysis of the request:

[0573] The device sends the received text data to a server, which then sends the text to a generator AI for analysis. OpenAI GPT-4 is sometimes used as the generator AI.

[0574] The generative AI analyzes the input and identifies the necessary actions, such as "create a daily report" and "register in the sales management system (SFA)."

[0575] 3. Execution of Business:

[0576] The server executes the identified actions using robotic process automation (RPA), an example of which is UiPath.

[0577] Sales data is entered into a template for creating daily reports, and the data is registered in a sales management system (e.g., Salesforce).

[0578] 4. Reporting the results:

[0579] The server passes the results of the work execution to the generation AI, which generates a result report in natural language text.

[0580] The device will then convert the report into voice using a speech synthesis AI, such as Amazon Polly.

[0581] For example, a report may be made stating, "A daily report has been created and registered in the sales management system."

[0582] Specific examples

[0583] Example 1: Creating daily reports + SFA automatic input

[0584] User: "Create today's sales report and register it in SFA."

[0585] The terminal converts the voice into text and sends the text "Create today's sales report and register it with the SFA" to the server.

[0586] The server analyzes the text using the generated AI (OpenAI GPT-4) and identifies the actions of "create daily report" and "register SFA."

[0587] The server instructs the RPA (UiPath) to automatically create daily reports and execute the process of registering the daily reports in the SFA (such as Salesforce's API).

[0588] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered with SFA."

[0589] The device uses a speech synthesis AI (Amazon Polly) to convert the report into audio and notify the user.

[0590] Example 2: Meeting schedule adjustment + calendar integration

[0591] User: "Schedule our next team meeting."

[0592] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[0593] The server analyzes the text using generative AI (OpenAI GPT-4) and identifies the action "schedule a meeting."

[0594] The server instructs RPA (Automation Anywhere) to use a calendar API (such as Google Calendar API) to check the participants' free time and perform the process of setting the optimal date.

[0595] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time."

[0596] The device uses a speech synthesis AI (Amazon Polly) to convert the report into audio and notify the user.

[0597] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests, thereby enabling users to automate and streamline their tasks while saving time and effort.

[0598] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0599] Step 1:

[0600] The user sends a request in voice or text format. For example, a request might be made to "create today's sales report and register it in the sales management system." The device receives this request. In the case of voice format, the device converts the voice to text using Google Cloud Speech-to-Text. The input here is voice data, and the output is the converted text data.

[0601] Specific behavior:

[0602] The user speaks to their smartphone, saying, "Create today's sales report and register it with SFA."

[0603] The device records the audio data and sends it to the Google Cloud Speech-to-Text API.

[0604] Google Cloud Speech-to-Text analyzes the audio data and generates text data such as "Create today's sales report and register it with SFA."

[0605] Step 2:

[0606] The device sends the converted text data to the server. The server passes the received text data to the generation AI (OpenAI GPT-4) for analysis. The generation AI analyzes the text content and identifies the necessary actions. The input here is the text data, and the output is a list of analyzed actions.

[0607] Specific behavior:

[0608] The terminal sends the text data "Create today's sales report and register it in the SFA" to the server via an HTTP request.

[0609] The server passes the text data to the generation AI along with a prompt saying, "Please analyze the request and identify the required action."

[0610] The generation AI analyzes the text content and identifies the actions "create daily report" and "register SFA."

[0611] Step 3:

[0612] The server executes the tasks based on the specified actions using Robotic Process Automation (RPA: UiPath). RPA automates the specified business process. The input here is a list of actions, and the output is the results of the executed tasks.

[0613] Specific behavior:

[0614] The server instructs the RPA (UiPath) API to create daily reports and register them in the SFA.

[0615] RPA opens the daily report template and automatically enters sales data into the template.

[0616] The daily reports completed by RPA are registered in the sales management system (such as Salesforce API).

[0617] Step 4:

[0618] The server passes the business execution results received from the RPA to the generation AI, which then generates a result report in natural language text. Here, the input is the business execution results, and the output is natural language text.

[0619] Specific behavior:

[0620] The server passes the prompt message to the generation AI: "Please explain the process of creating a daily report and registering it with SFA."

[0621] The generation AI generates a report stating, "A daily report has been created and registered with SFA."

[0622] Step 5:

[0623] The device receives the generated report, converts it into speech using speech synthesis software (Amazon Polly), and reports it to the user. The input here is natural language text, and the output is speech data.

[0624] Specific behavior:

[0625] The terminal receives the report "The daily report has been created and registered in the sales management system."

[0626] The device inputs the report into Amazon Polly and generates voice data.

[0627] The terminal plays back the voiced report and notifies the user that "the daily report has been created and registered in the sales management system."

[0628] (Application example 1)

[0629] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0630] Improving work efficiency and reducing costs are key challenges in modern industrial facilities. However, many tasks, such as communicating work instructions, managing maintenance schedules, and managing inventory, are still performed manually, which can be error-prone and time-consuming. Furthermore, these tasks require advanced skills, leading to labor shortages and increased training costs. The purpose of this invention is to solve these challenges and automate and streamline work in industrial facilities.

[0631] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0632] In this invention, the server includes: means for receiving user requests by voice or text; means for analyzing the received requests using a voice recognition system or a text analysis system; means including a generative artificial intelligence for identifying necessary actions based on the analysis results; means for executing tasks using a robotic process automation system for the identified actions; means for reporting the execution results to the user; means for receiving voice instructions at the industrial facility and automatically executing tasks by industrial robots based on the instructions; means for automatically creating a maintenance schedule for industrial equipment and executing necessary maintenance tasks based on the schedule; and means for automatically managing inventory within the industrial facility and automatically placing orders when inventory is low, thereby enabling improved work efficiency and cost reduction within the industrial facility.

[0633] A "voice recognition system" is a system that converts voice into a digital signal, analyzes the content, and converts it into text format.

[0634] A "text analysis system" is a system that analyzes textual information and understands its meaning and context.

[0635] "Generative AI" is AI that identifies necessary actions based on given data and generates appropriate responses and work instructions based on that content.

[0636] A "robotic process automation system" is a system that uses software robots to automate routine business processes.

[0637] An "industrial facility" is an industrial facility where manufacturing, processing, etc. is carried out, including factories and plants.

[0638] An "industrial robot" is a robot designed to perform tasks automatically in an industrial facility.

[0639] A "maintenance schedule" is a planned schedule for maintaining, inspecting, and repairing equipment and facilities.

[0640] "Inventory management" is the process of monitoring and properly managing the quantity and condition of inventory, such as parts and raw materials, used within an industrial facility.

[0641] "Placing an order" is the act of ordering needed goods or services from a supplier.

[0642] "Analysis results" are the results of interpretation and understanding of information obtained via a speech recognition system or text analysis system.

[0643] "Business proposal" is the act of proposing to a user the optimal way to execute a business based on the analysis results.

[0644] "Automation" refers to a state in which machines and systems operate autonomously with reduced human intervention.

[0645] "Result reporting" is the act of notifying the user of the results of an executed task or process.

[0646] To realize the present invention, the following hardware and software are used.

[0647] Hardware

[0648] server

[0649] Devices (smartphones, tablets, PCs, etc.)

[0650] industrial robots

[0651] Voice input device

[0652] Internet-connected devices

[0653] software

[0654] Speech recognition systems (e.g., Google Cloud Speech-to-Text)

[0655] Text analysis systems (e.g. NLU / NLP engines)

[0656] Generative artificial intelligence (e.g. OpenAI GPT-4)

[0657] Robotic process automation systems (e.g., UiPath)

[0658] API Gateway (e.g. AWS API Gateway)

[0659] Text-to-speech systems (e.g., Amazon Polly)

[0660] Specific Embodiments of the Invention

[0661] 1. Receiving a user request

[0662] The user makes a request by voice or text, which is received by the device. In the case of voice input, the request is converted into text by a speech recognition system. This text data is then sent to the server for analysis.

[0663] 2. Request Analysis

[0664] The server analyzes the received text data using a generative AI model. The analysis results in the identification of the necessary action. For example, in response to a request to "check the next maintenance schedule," the action of creating a maintenance schedule is identified.

[0665] 3. Execution of Business

[0666] Based on the identified actions, the server executes tasks using the robotic process automation system, such as creating maintenance schedules, issuing work instructions for industrial robots, and ordering inventory shortages. For example, the server sends an instruction to an industrial robot to "perform a specific task" through the API gateway.

[0667] 4. Reporting the results

[0668] The server passes the results of the task execution to the generative AI model, which then creates a natural language text report of the results. The report is then converted into voice by a speech synthesis system and notified to the user via the device. For example, a report such as "The next maintenance will be on October 2nd" is made audibly.

[0669] Examples of concrete examples and prompts

[0670] Examples:

[0671] 1. User Request: "Check the next maintenance schedule"

[0672] 2. Execution result: The server creates a maintenance schedule and reports, "The next maintenance will be on October 2nd."

[0673] Example prompt sentence:

[0674] 1. "Check the next maintenance schedule"

[0675] 2. "Check availability"

[0676] 3. "Please give instructions to the industrial robot."

[0677] In this way, by using the appropriate hardware and software at each step, it is possible to automate and streamline operations within an industrial facility.

[0678] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0679] Step 1:

[0680] The user makes a request by voice or text. The device receives this request. For example, the user may say, "Check the next maintenance schedule." The device captures this voice and passes it to the voice recognition system.

[0681] Input: User's voice or text request

[0682] Output: Request converted into text by the speech recognition system

[0683] What it does: The device uses a microphone to capture the user's voice and then sends the voice data to a speech recognition system (e.g., Google Cloud Speech-to-Text) to convert it into text.

[0684] Step 2:

[0685] The terminal receives the request converted into text from the voice recognition system and sends it to the server.

[0686] Input: Text request from speech recognition system

[0687] Output: Text request received by the server

[0688] Specific operation: The terminal sends text data to the server using an HTTP request or the like.

[0689] Step 3:

[0690] The server then passes the received text request to a generative AI model for analysis. The generative AI model (e.g., OpenAI GPT-4) analyzes the text and identifies the required action.

[0691] Input: Text request details

[0692] Output: List of identified actions

[0693] Specific operation: The server inputs text data into the generative AI model, which then performs contextual analysis and keyword matching to list appropriate actions.

[0694] Step 4:

[0695] Based on the identified actions, the server issues instructions to a robotic process automation system (e.g., UiPath), which then automatically performs the necessary tasks, such as creating and executing maintenance schedules.

[0696] Input: A list of identified actions

[0697] Output: The results of the work performed

[0698] What it does: The server sends API requests to the robotic process automation system, which then executes automated scripts to create maintenance schedules and instruct industrial robots.

[0699] Step 5:

[0700] The server receives the execution results and passes them to a generative AI model to generate natural language text reporting the results.

[0701] Input: Result of the work performed

[0702] Output: Natural language text of the results report

[0703] How it works: The server receives the results from the robotic process automation system and inputs them into the generative AI model, which then generates text in natural language based on the results.

[0704] Step 6:

[0705] The server passes the generated report to a speech synthesis system (e.g., Amazon Polly) to convert it into voice, and the device notifies the user of the voiced report.

[0706] Input: Natural language text of the result report

[0707] Output: Audio report to notify the user

[0708] Specific operation: The server inputs the text from the generative AI model into the speech synthesis system to generate voice data, which the device plays back and reports to the user.

[0709] This series of steps automates and streamlines the management of work orders and maintenance schedules within industrial facilities.

[0710] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0711] This invention specifically realizes a personal AI secretary system that combines generative AI, robotic process automation (RPA), API gateway, speech recognition / synthesis AI, and an emotion engine. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's emotional state.

[0712] System Overview

[0713] 1. Receiving a request from a user:

[0714] The user sends a request in the form of voice or text. For example, a request might be, "Create tomorrow's sales report and register it in the sales management system."

[0715] The terminal receives this request and, if it is voice, converts it into text using a voice recognition system.

[0716] 2. Emotional state analysis:

[0717] The device uses an emotion engine to analyze the user's emotional state from the voice or text received, for example, to determine whether the user is stressed or relaxed.

[0718] 3. Parse the request:

[0719] The server passes the received text data to the generation AI, which analyzes the input and identifies the required action.

[0720] For example, the actions "create daily report" and "register sales management system" are identified.

[0721] 4. Business proposal:

[0722] The server then makes more appropriate work suggestions based on the user's emotional state and the analysis results. For example, if the user is feeling stressed, it will suggest simplifying the work.

[0723] 5. Execution of Business:

[0724] The server executes the task based on the specified action using robotic process automation (RPA), which automatically executes the specified business process.

[0725] For example, sales data is entered into a template for creating daily reports, and the data is then registered in a sales management system.

[0726] 6. Reporting results:

[0727] The server passes the results of the work execution to the generation AI, which then creates a natural language text report of the results. The report is generated with emotional consideration according to the situation.

[0728] The device uses speech synthesis AI to convert this report into audio and report it to the user. For example, it might say something like, "Based on your request, we have created a daily report and registered it in the sales management system. Don't worry, everything is going smoothly."

[0729] Specific examples

[0730] Example 1: Creating daily reports + automatic input to the sales management system

[0731] User: "Create tomorrow's sales report and register it in the sales management system."

[0732] The terminal converts the voice into text and sends the text "Create tomorrow's sales report and register it in the sales management system" to the server.

[0733] The server uses generation AI to analyze the text and identify the actions of "create daily report" and "register in sales management system."

[0734] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[0735] The server makes emotion-based business suggestions, for example, simplifying templates for creating daily reports.

[0736] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the sales management system's API.

[0737] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[0738] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0739] Example 2: Meeting schedule adjustment + calendar integration

[0740] User: "Schedule our next team meeting."

[0741] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[0742] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[0743] The device uses an emotion engine to analyze the user's emotional state and detect when the user is relaxed.

[0744] The server instructs the RPA to use the calendar API to check the free time of all participants and set the optimal date.

[0745] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[0746] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0747] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests and taking into account the user's emotional state, thereby reducing the burden on employees and improving work efficiency.

[0748] The processing flow will be explained below.

[0749] Example 1: Creating daily reports + automatic input into sales management system

[0750] Step 1:

[0751] The user requests, "Create tomorrow's sales report and register it in the sales management system."

[0752] Step 2:

[0753] The device receives the user's request. If the request is received by voice, it uses a voice recognition system to convert the voice into text.

[0754] Step 3:

[0755] The device converts the speech into text and sends it to the server, saying, "Create tomorrow's sales report and register it in the sales management system." This time, the data is sent via an HTTPS request.

[0756] Step 4:

[0757] The device uses an emotion engine to analyze the user's emotional state from the text data, and detects from the analysis results that the user is feeling stressed.

[0758] Step 5:

[0759] The server passes the received text data to the generation AI, which analyzes the text and identifies the actions of "create daily report" and "register in sales management system."

[0760] Step 6:

[0761] The server takes into account the user's emotional state and optimizes task suggestions. For example, if the user is feeling stressed, it will suggest ways to simplify daily report creation.

[0762] Step 7:

[0763] The server issues instructions to the Robotic Process Automation (RPA) system based on the identified actions. The necessary information is embedded in the template for "daily report creation" and the daily report is automatically generated.

[0764] Step 8:

[0765] The daily reports generated by RPA are automatically registered through the sales management system's API.

[0766] Step 9:

[0767] The RPA returns the execution results to the server as a log, indicating that the daily report has been created and registered in the sales management system.

[0768] Step 10:

[0769] The server passes the execution results to the generation AI, which then generates a report in natural language, such as, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[0770] Step 11:

[0771] The server sends the generated report to the device via an HTTPS request.

[0772] Step 12:

[0773] The device uses speech synthesis AI to convert the report into audio, generating a voice file that says, "The daily report has been created and registered in the sales management system. Things are progressing smoothly without any stress."

[0774] Step 13:

[0775] The device reports the generated audio to the user through a playback device (such as a speaker).

[0776] Example 2: Meeting schedule adjustment + calendar integration

[0777] Step 1:

[0778] A user requests, "Schedule our next team meeting."

[0779] Step 2:

[0780] The device receives the user's request. If the request is received by voice, it uses a voice recognition system to convert the voice into text.

[0781] Step 3:

[0782] The device sends the converted text "Schedule the next team meeting" to the server via an HTTPS request.

[0783] Step 4:

[0784] The device uses an emotion engine to analyze the user's emotional state from the text data, and detects from the analysis results that the user is relaxed.

[0785] Step 5:

[0786] The server passes the received text data to the generation AI, which analyzes the text and identifies the action to "schedule a meeting."

[0787] Step 6:

[0788] The server takes into account the user's emotional state and optimizes the task suggestions, for example, if the user is relaxed, it will suggest detailed schedule adjustments.

[0789] Step 7:

[0790] The server issues instructions to a robotic process automation (RPA) system based on the identified actions, and uses a calendar API to check the availability of all participants and set the optimal date.

[0791] Step 8:

[0792] RPA automatically schedules meetings through a calendar API.

[0793] Step 9:

[0794] The RPA returns the execution result to the server as a log, indicating that the meeting schedule has been arranged.

[0795] Step 10:

[0796] The server passes the execution results to the generation AI, which then generates a report in natural language, such as "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[0797] Step 11:

[0798] The server sends the generated report to the device via an HTTPS request.

[0799] Step 12:

[0800] The device uses speech synthesis AI to convert the report into audio, generating an audio file that says, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[0801] Step 13:

[0802] The device reports the generated audio to the user through a playback device (such as a speaker).

[0803] Through these specific processing steps, the task requested by the user is efficiently performed while taking into account the emotional state.

[0804] Example 2

[0805] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0806] Conventional voice assistant systems and automated systems often simply execute tasks based on user requests without considering the user's emotional state or work efficiency. As a result, they are unable to reduce the user's burden and stress, and their improvement in work efficiency is limited. Furthermore, if the execution result reports are not emotionally sensitive, they fail to provide sufficient satisfaction to the user.

[0807] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0808] In this invention, the server includes: means for receiving a user request by voice or text; means for analyzing the received request using a voice recognition system or a text analysis system; means including an emotion engine for analyzing the analysis results and the user's emotional state; means including generative artificial intelligence for identifying necessary actions based on the analysis results; means for executing tasks using a robotic process automation system for the identified actions; and means for converting the execution results into natural language text using the generative artificial intelligence and reporting them to the user using a speech synthesis system. This enables tasks to be proposed and executed taking into consideration the user's emotional state, thereby reducing the user's burden and improving work efficiency.

[0809] A "voice or text user request" is an action in which a user sends an instruction or request to the system in voice or text form.

[0810] A "speech recognition system" is a technique or device for converting voice input into text data.

[0811] A "text analysis system" is a technology or device for analyzing text data to understand its meaning and structure.

[0812] An "emotion engine" is a technique or device for analyzing data received from a user to identify their emotional state.

[0813] "Generative AI" is an AI technology that analyzes and generates data based on input data.

[0814] A "robotic process automation system" is a system that automatically carries out specified business processes.

[0815] "Natural language text" is text written in natural language that is generated by a computer system.

[0816] A "speech synthesis system" is a technology or device for converting text data into speech.

[0817] "Business proposal" is the act of proposing business optimization and procedure improvements based on the user's request and emotional state.

[0818] MODE FOR CARRYING OUT THE INVENTION

[0819] This invention specifically realizes a personal AI secretary system that combines generative artificial intelligence (generative AI), robotic process automation (RPA), an API gateway, a speech recognition and synthesis system, and an emotion engine. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's emotional state.

[0820] System Overview

[0821] First, the user issues a request in the form of voice or text. For example, a request might be, "Create tomorrow's sales report and register it in the sales management system." The device receives this request, and if it is voice, it converts it into text using a voice recognition system (e.g., voice recognition API). From the received voice or text, the device uses an emotion engine (e.g., emotion analysis API) to analyze the user's emotional state. For example, it determines whether the user is feeling stressed or relaxed.

[0822] Next, the server passes the text data to a generation AI (e.g., GPT-4), which analyzes the input and identifies the necessary actions. For example, the actions "Create a daily report" and "Register in a sales management system" may be identified. The server then makes more appropriate work suggestions based on the user's emotional state and the analysis results. For example, if the user is feeling stressed, it will make suggestions to simplify the work.

[0823] The server executes the task using robotic process automation (RPA) (e.g., RPA tool) based on the specified action. RPA automatically executes the specified business process. For example, sales data is entered into a template for creating daily reports, and the data is registered in a sales management system (e.g., business management system API).

[0824] Finally, the server passes the results of the work execution to the generation AI, which then creates a natural language text report of the results. The report is generated with emotional considerations depending on the situation. The device then converts this report into voice using a speech synthesis system (e.g., speech synthesis API) and reports it to the user. For example, it might say, "Based on your request, a daily report has been created and registered in the sales management system. Don't worry, everything is going smoothly."

[0825] Specific examples

[0826] Example 1: Creating daily reports + automatic input to the sales management system

[0827] User: "Create tomorrow's sales report and register it in the sales management system."

[0828] The terminal converts the voice into text and sends the text "Create tomorrow's sales report and register it in the sales management system" to the server.

[0829] The server uses generation AI to analyze the text and identify the actions of "create daily report" and "register in sales management system."

[0830] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[0831] The server makes emotion-based business suggestions, for example, simplifying templates for creating daily reports.

[0832] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the sales management system's API.

[0833] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[0834] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0835] Example 2: Meeting schedule adjustment + calendar integration

[0836] User: "Schedule our next team meeting."

[0837] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[0838] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[0839] The device uses an emotion engine to analyze the user's emotional state and detect when the user is relaxed.

[0840] The server instructs the RPA to use a calendar API (e.g., calendar management system API) to check the free time of all participants and set the optimal date.

[0841] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[0842] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0843] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests and taking into account the user's emotional state, thereby reducing the burden on employees and improving work efficiency.

[0844] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0845] Step 1:

[0846] The user sends a request by voice or text.

[0847] Specifically, the user inputs something like "Create tomorrow's sales report and register it in the sales management system" by voice or text. The input data is an audio file or a text file.

[0848] Step 2:

[0849] The terminal receives a voice or text request from the user.

[0850] In this step, the input is the audio file or text file from step 1, and the output is the audio or text data stored on the device. Specifically, data is received using a microphone or chat interface and temporarily stored.

[0851] Step 3:

[0852] The device uses a voice recognition system to convert the speech into text.

[0853] In the conversion process, the input is audio data and the output is text data. Specifically, a speech recognition system (e.g., a speech recognition API) is used to analyze the audio file and convert it into text.

[0854] Step 4:

[0855] The text data received by the device is sent to the emotion engine for analysis.

[0856] In this step, the input is text data and the output is data about the user's emotional state. Specifically, an emotion analysis API is used to analyze the user's emotions from the text data and identify emotional states such as "stress" or "relaxation."

[0857] Step 5:

[0858] The server sends the text data to the generation AI for analysis and identifies the necessary actions.

[0859] The input is text data and emotional state data, and the output is data that specifies actions. Specifically, the prompt sentence "Create tomorrow's sales report and register it in the sales management system" is input to the generation AI (e.g., GPT-4), and the actions "Create daily report" and "Register in the sales management system" are specified.

[0860] Step 6:

[0861] The server integrates the emotion data and action data and makes business suggestions based on the user's emotions.

[0862] The input is emotion data and action data, and the output is a specific business proposal. For example, the server generates a specific business proposal such as "Suggest simplifying templates for users with high stress levels."

[0863] Step 7:

[0864] The server executes the task using robotic process automation (RPA) based on the identified actions.

[0865] The input is the specified action data, and the output is the result of the business execution. Specifically, sales data is entered into a daily report creation template using an RPA tool (e.g., UiPath), and the data is registered via the sales management system's API.

[0866] Step 8:

[0867] The server passes the results of the work execution to the generation AI, which then creates a result report.

[0868] The input is the data on the results of the work execution, and the output is a report of the results in natural language. Using a generative AI (e.g., GPT-4), a report such as "A daily report has been created and registered in the sales management system. Things are progressing without stress" is generated.

[0869] Step 9:

[0870] The device passes the generated text to a speech synthesis system to convert it into speech.

[0871] The input is natural language text and the output is audio data. Specifically, the report text is converted into audio using a speech synthesis API.

[0872] Step 10:

[0873] The terminal reports the audio data to the user.

[0874] The output is an audio report that tells the user, "Based on your request, a daily report has been created and registered in the sales management system. Don't worry, everything is going smoothly."

[0875] (Application example 2)

[0876] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0877] This invention relates to a personal AI secretary system specialized for security services, and aims to automate and streamline security-related tasks while taking into account the user's emotional state. Conventional systems perform tasks uniformly without considering the user's emotional state, which can cause anxiety and stress to the user. Furthermore, the reporting of execution results is mechanical, which does not fully satisfy the user's psychological satisfaction. There is a need to solve these problems.

[0878] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user request by voice or text, means for analyzing the received request using a voice recognition system or a text analysis system, and means including a generation AI for identifying a required action based on the analysis results. This enables a user to send a request by voice or text, and the system to automatically identify appropriate security-related tasks based on the content of the request and execute them taking into account the user's emotional state.

[0879] The system also includes a means for executing a task using a robotic process automation system for the identified action, a means for reporting the execution result to a user, an emotion engine that analyzes the user's emotional state and proposes a task based on the analyzed emotional state, and a means for executing the proposed task using the robotic process automation system. This enables security tasks to be executed and reported while taking the user's emotional state into consideration, thereby reducing the user's mental stress and increasing their satisfaction.

[0880] A "voice recognition system" refers to technology that converts voice information into text data.

[0881] A "text analysis system" refers to a technology that analyzes text data and performs processes such as semantic analysis and document classification.

[0882] "Generative AI" refers to artificial intelligence technology that analyzes user requests and generates appropriate actions and suggestions.

[0883] "Robotic process automation system" refers to a software robot that automatically executes a specified business process.

[0884] An "emotion engine" is a system that analyzes a user's emotional state from their voice and text and determines their state of stress, relaxation, etc.

[0885] A "speech synthesis system" refers to a technology that converts text data into speech to generate synthetic speech.

[0886] "Natural language text" refers to text data in the form of language used by people in everyday conversation and writing.

[0887] This invention provides a personal AI secretary system specialized for security services, and specifically realizes a system that automates and efficiently executes security-related tasks while taking into account the user's emotional state.

[0888] System configuration and operation

[0889] The system mainly consists of the following components:

[0890] 1. A device that receives user requests via voice or text

[0891] The terminal uses a speech recognition system (for example, Google Speech Recognition API) to convert the speech into text data.

[0892] 2. A server that analyzes the received request using a voice recognition system or text analysis system.

[0893] The server uses a generative AI (e.g., OpenAI's GPT-3) to analyze the received text data and identify the necessary actions.

[0894] 3. Emotion engine that analyzes emotional states

[0895] The analysis system determines emotions from the user's voice or text data, for example using a proprietary emotion engine to determine whether the user is stressed or relaxed.

[0896] 4. A server that executes tasks using a robotic process automation system for the identified actions.

[0897] Identified actions are then executed through robotic process automation (RPA) systems, for example to perform security-related tasks such as increasing security camera monitoring or activating alarm systems.

[0898] 5. Server that converts the execution results into natural language text using generation AI

[0899] The execution results are converted into natural language text by the generative AI and reported to the user.

[0900] 6. A device that converts the converted text into speech using a speech synthesis system

[0901] The text data sent from the server is converted into voice using a speech synthesis system (e.g., Pyttsx3) and reported to the user through the terminal.

[0902] Specific examples

[0903] Example 1: Request for increased security at home

[0904] User: "Tighten up the security at home tonight."

[0905] The device converts the speech into text and sends the text "Enhance home security tonight" to the server.

[0906] The server uses generative AI to analyze the text and identify the action to "strengthen security systems."

[0907] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[0908] The server makes business suggestions based on emotions, such as suggesting strengthening surveillance camera monitoring or activating an alarm system.

[0909] The server instructs the RPA to execute the process of strengthening the security system.

[0910] The server passes the execution results to the generation AI, which generates a report saying, "Security has been strengthened. Everything is going well. Rest in peace tonight."

[0911] The terminal uses a speech synthesis system to convert the report into voice and notify the user.

[0912] Prompt Sentence Examples

[0913] "User Request: Increase security at home tonight. Please identify the necessary security tasks."

[0914] In this way, the present invention provides for the automatic execution and efficient processing of security tasks based on user requests and taking into account emotional state.

[0915] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0916] Step 1:

[0917] The user inputs a request by voice or text. The input voice data is a specific request such as "Please tighten the security at my house tonight." This data is input into the voice recognition system.

[0918] Step 2:

[0919] The device uses a voice recognition system (e.g., Google Speech Recognition API) to convert the voice data into text data. The voice data, "Please strengthen the security of my home tonight," is output as text data.

[0920] Step 3:

[0921] The server inputs text data into a generative AI (e.g., OpenAI GPT-3) for analysis. The server inputs the prompt "User request: Please strengthen the security of my home tonight. Please identify the necessary security tasks" into the generative AI model and obtains the analysis result. The identified action, "Strengthen the security system," is output as the analysis result.

[0922] Step 4:

[0923] The device uses an emotion engine to analyze the user's emotional state. Text data is input into the emotion engine, which determines whether the user is feeling stressed or relaxed. The analysis result is output as "stressed."

[0924] Step 5:

[0925] The server makes business proposals based on the analysis results and the emotional state. Using a generative AI model, the server generates proposals appropriate to the emotional state. For example, specific proposals such as "strengthen surveillance camera monitoring" or "activate the alarm system" are output.

[0926] Step 6:

[0927] The server uses a robotic process automation (RPA) system to execute the proposed security tasks. The server inputs instruction data such as "strengthen surveillance camera monitoring" or "activate the alarm system" into the RPA and obtains the execution results. The execution result is output as a message that the security system has been strengthened.

[0928] Step 7:

[0929] The server uses generative AI to convert the execution results into natural language text. The execution result data is input into the generative AI model, and a report is generated that reads, "Security has been strengthened. Everything is going well. Rest easy tonight."

[0930] Step 8:

[0931] The device uses a speech synthesis system (e.g., Pyttsx3) to convert the generated report into voice and notify the user. The generated report text is input into the speech synthesis system, output as voice data, and reported to the user.

[0932] 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.

[0933] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0934] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0935] [Third embodiment]

[0936] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0937] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0938] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0939] 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.

[0940] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0941] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0942] 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.

[0943] 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.

[0944] 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 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.

[0945] 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.

[0946] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0947] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0948] This invention specifically realizes a personal AI secretary system that combines generative AI, robotic process automation (RPA), API gateway, and speech recognition and synthesis AI. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's attributes.

[0949] System Overview

[0950] 1. Receiving a request from a user:

[0951] The user sends a request in the form of voice or text. For example, a request might be, "Create today's sales report and register it in the sales management system."

[0952] The terminal receives this request and, if it is voice, converts it into text using a voice recognition system.

[0953] 2. Parse the request:

[0954] The server sends the received text data to the generation AI, which analyzes the input and identifies the necessary actions.

[0955] For example, the actions "Create a daily report" and "Register in SFA (Sales Management System)" are identified.

[0956] 3. Execution of Business:

[0957] The server executes the task based on the specified action using robotic process automation (RPA), which automatically executes the specified business process.

[0958] For example, sales data is entered into a template for creating daily reports, and the data is then registered in the SFA.

[0959] 4. Reporting the results:

[0960] The server passes the results of the business execution to the generation AI, which then creates a natural language text report of the results.

[0961] The device uses voice synthesis AI to convert this report into audio and report it to the user.

[0962] For example, the content might be something like, "A daily report has been created and registered in the sales management system."

[0963] Specific examples

[0964] Example 1: Creating daily reports + SFA automatic input

[0965] User: "Create today's sales report and register it in SFA."

[0966] The terminal converts the voice into text and sends the text "Create today's sales report and register it with the SFA" to the server.

[0967] The server uses the generated AI to analyze the text and identify the actions of "create daily report" and "register SFA."

[0968] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the SFA system's API.

[0969] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered with SFA."

[0970] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0971] Example 2: Meeting schedule adjustment + calendar integration

[0972] User: "Schedule our next team meeting."

[0973] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[0974] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[0975] The server instructs the RPA to use the calendar API to check the participants' free times and set the optimal date.

[0976] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time."

[0977] The device uses speech synthesis AI to convert the report into audio and notify the user.

[0978] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests, thereby reducing the burden on employees and improving work efficiency.

[0979] The processing flow will be explained below.

[0980] Step 1:

[0981] Users can send requests to their personal AI secretary via voice or text, for example, "Create tomorrow's sales report and register it in the sales management system."

[0982] Step 2:

[0983] The device receives the user's request. If it is a voice request, it uses a voice recognition system to convert the voice into text. For example, "Create tomorrow's sales report and register it in the sales management system."

[0984] Step 3:

[0985] The device sends the converted text data to the server, which then sends the text data as an HTTPS request to the server for analysis.

[0986] Step 4:

[0987] The server passes the received text data to the generation AI, which prepares to analyze the text data.

[0988] Step 5:

[0989] The generative AI analyzes the text and identifies the necessary actions, such as "create a daily report" and "register in the sales management system."

[0990] Step 6:

[0991] Based on the identified action, the server passes the appropriate RPA script to the RPA system, which initiates the business automation process.

[0992] Step 7:

[0993] The RPA executes the "Daily Report Creation" script, automatically generating a daily report based on a pre-defined template and filling in the necessary data.

[0994] Step 8:

[0995] The RPA executes the "Sales Management System Registration" script, and the generated daily report data is automatically registered in the system via the sales management system (SFA) API.

[0996] Step 9:

[0997] The RPA reports the execution results to the server, and sends log data indicating that the daily report has been created and registered in the sales management system.

[0998] Step 10:

[0999] The server passes the execution results to the generation AI, which then generates a report. The generation AI creates a natural language report such as, "A daily report has been created and registered in the sales management system."

[1000] Step 11:

[1001] The server sends the generated report to the terminal, which sends the report as an HTTPS request.

[1002] Step 12:

[1003] The device uses a speech synthesis AI to convert the report into audio, which then generates a voice file saying, "The daily report has been created and registered in the sales management system."

[1004] Step 13:

[1005] The device notifies the user of the generated audio. The audio file is reported to the user through a playback device (such as a speaker).

[1006] Through these specific processing steps, daily tasks based on user requests are automated and efficiently performed.

[1007] Example 1

[1008] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1009] Conventional business automation systems have had difficulty accurately receiving users' voice requests, appropriately analyzing the content of those requests, efficiently processing them, and quickly providing feedback on the results. Therefore, there is a need for a system that integrates speech recognition, text analysis, generative artificial intelligence, robotic process automation, and speech synthesis technologies to consistently automate tasks based on user requests.

[1010] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1011] In this invention, the server includes means for receiving user requests by voice or text, means for analyzing the received requests using a voice recognition system or a text analysis system, means including a generative artificial intelligence for identifying necessary actions based on the analysis results, means for executing tasks using a robotic process automation system for the identified actions, and means for converting the execution results into natural language text using the generative artificial intelligence and vocalizing them using a speech synthesis system to report them to the user. This makes it possible to process user requests accurately and efficiently and quickly provide feedback on the results.

[1012] A "means for receiving a voice or text user request" is a device or software for receiving a voice or text request from a user.

[1013] A "speech recognition system" is a technology or device that receives voice data as input, analyzes it, and converts it into text data.

[1014] A "text analysis system" is a technology or device that analyzes received text, understands its content, and extracts necessary information.

[1015] "Generative AI" refers to artificial intelligence techniques used to analyze text or instructions and identify appropriate actions or generate natural language text.

[1016] A "robotic process automation system" is software or a device that automatically executes business processes and efficiently handles repetitive tasks.

[1017] A "speech synthesis system" is a technology or device that converts text data into voice data and generates artificial voices.

[1018] "Natural language text" refers to text data written in a natural format, such as human speech.

[1019] A "user request" is an instruction or request that a user makes to the system.

[1020] "Analysis results" refer to the results of analysis obtained by a speech recognition system or text analysis system.

[1021] "Business execution results" refer to the results obtained after a robotic process automation system executes a business.

[1022] A "reporting means" is a method or device used to communicate the results of an execution to a user.

[1023] This invention specifically realizes a personal AI secretary system that combines generative artificial intelligence (AI), robotic process automation (RPA), an API gateway, and speech recognition and synthesis AI. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's attributes.

[1024] System Program Overview

[1025] 1. Receiving a request from a user:

[1026] Users can send requests in voice or text format, such as "Create today's sales report and register it in the sales management system."

[1027] The device receives this request and, if it is voice, converts it into text using speech recognition software, specifically Google Cloud Speech-to-Text.

[1028] 2. Analysis of the request:

[1029] The device sends the received text data to a server, which then sends the text to a generator AI for analysis. OpenAI GPT-4 is sometimes used as the generator AI.

[1030] The generative AI analyzes the input and identifies the necessary actions, such as "create a daily report" and "register in the sales management system (SFA)."

[1031] 3. Execution of Business:

[1032] The server executes the identified actions using robotic process automation (RPA), an example of which is UiPath.

[1033] Sales data is entered into a template for creating daily reports, and the data is registered in a sales management system (e.g., Salesforce).

[1034] 4. Reporting the results:

[1035] The server passes the results of the work execution to the generation AI, which generates a result report in natural language text.

[1036] The device will then convert the report into voice using a speech synthesis AI, such as Amazon Polly.

[1037] For example, a report may be made stating, "A daily report has been created and registered in the sales management system."

[1038] Specific examples

[1039] Example 1: Creating daily reports + SFA automatic input

[1040] User: "Create today's sales report and register it in SFA."

[1041] The terminal converts the voice into text and sends the text "Create today's sales report and register it with the SFA" to the server.

[1042] The server analyzes the text using the generated AI (OpenAI GPT-4) and identifies the actions of "create daily report" and "register SFA."

[1043] The server instructs the RPA (UiPath) to automatically create daily reports and execute the process of registering the daily reports in the SFA (such as Salesforce's API).

[1044] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered with SFA."

[1045] The device uses a speech synthesis AI (Amazon Polly) to convert the report into audio and notify the user.

[1046] Example 2: Meeting schedule adjustment + calendar integration

[1047] User: "Schedule our next team meeting."

[1048] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[1049] The server analyzes the text using generative AI (OpenAI GPT-4) and identifies the action "schedule a meeting."

[1050] The server instructs RPA (Automation Anywhere) to use a calendar API (such as Google Calendar API) to check the participants' free time and perform the process of setting the optimal date.

[1051] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time."

[1052] The device uses a speech synthesis AI (Amazon Polly) to convert the report into audio and notify the user.

[1053] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests, thereby enabling users to automate and streamline their tasks while saving time and effort.

[1054] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1055] Step 1:

[1056] The user sends a request in voice or text format. For example, a request might be made to "create today's sales report and register it in the sales management system." The device receives this request. In the case of voice format, the device converts the voice to text using Google Cloud Speech-to-Text. The input here is voice data, and the output is the converted text data.

[1057] Specific behavior:

[1058] The user speaks to their smartphone, saying, "Create today's sales report and register it with SFA."

[1059] The device records the audio data and sends it to the Google Cloud Speech-to-Text API.

[1060] Google Cloud Speech-to-Text analyzes the audio data and generates text data such as "Create today's sales report and register it with SFA."

[1061] Step 2:

[1062] The device sends the converted text data to the server. The server passes the received text data to the generation AI (OpenAI GPT-4) for analysis. The generation AI analyzes the text content and identifies the necessary actions. The input here is the text data, and the output is a list of analyzed actions.

[1063] Specific behavior:

[1064] The terminal sends the text data "Create today's sales report and register it in the SFA" to the server via an HTTP request.

[1065] The server passes the text data to the generation AI along with a prompt saying, "Please analyze the request and identify the required action."

[1066] The generation AI analyzes the text content and identifies the actions "create daily report" and "register SFA."

[1067] Step 3:

[1068] The server executes the tasks based on the specified actions using Robotic Process Automation (RPA: UiPath). RPA automates the specified business process. The input here is a list of actions, and the output is the results of the executed tasks.

[1069] Specific behavior:

[1070] The server instructs the RPA (UiPath) API to create daily reports and register them in the SFA.

[1071] RPA opens the daily report template and automatically enters sales data into the template.

[1072] The daily reports completed by RPA are registered in the sales management system (such as Salesforce API).

[1073] Step 4:

[1074] The server passes the business execution results received from the RPA to the generation AI, which then generates a result report in natural language text. Here, the input is the business execution results, and the output is natural language text.

[1075] Specific behavior:

[1076] The server passes the prompt message to the generation AI: "Please explain the process of creating a daily report and registering it with SFA."

[1077] The generation AI generates a report stating, "A daily report has been created and registered with SFA."

[1078] Step 5:

[1079] The device receives the generated report, converts it into speech using speech synthesis software (Amazon Polly), and reports it to the user. The input here is natural language text, and the output is speech data.

[1080] Specific behavior:

[1081] The terminal receives the report "The daily report has been created and registered in the sales management system."

[1082] The device inputs the report into Amazon Polly and generates voice data.

[1083] The terminal plays back the voiced report and notifies the user that "the daily report has been created and registered in the sales management system."

[1084] (Application example 1)

[1085] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1086] Improving work efficiency and reducing costs are key challenges in modern industrial facilities. However, many tasks, such as communicating work instructions, managing maintenance schedules, and managing inventory, are still performed manually, which can be error-prone and time-consuming. Furthermore, these tasks require advanced skills, leading to labor shortages and increased training costs. The purpose of this invention is to solve these challenges and automate and streamline work in industrial facilities.

[1087] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1088] In this invention, the server includes: means for receiving user requests by voice or text; means for analyzing the received requests using a voice recognition system or a text analysis system; means including a generative artificial intelligence for identifying necessary actions based on the analysis results; means for executing tasks using a robotic process automation system for the identified actions; means for reporting the execution results to the user; means for receiving voice instructions at the industrial facility and automatically executing tasks by industrial robots based on the instructions; means for automatically creating a maintenance schedule for industrial equipment and executing necessary maintenance tasks based on the schedule; and means for automatically managing inventory within the industrial facility and automatically placing orders when inventory is low, thereby enabling improved work efficiency and cost reduction within the industrial facility.

[1089] A "voice recognition system" is a system that converts voice into a digital signal, analyzes the content, and converts it into text format.

[1090] A "text analysis system" is a system that analyzes textual information and understands its meaning and context.

[1091] "Generative AI" is AI that identifies necessary actions based on given data and generates appropriate responses and work instructions based on that content.

[1092] A "robotic process automation system" is a system that uses software robots to automate routine business processes.

[1093] An "industrial facility" is an industrial facility where manufacturing, processing, etc. is carried out, including factories and plants.

[1094] An "industrial robot" is a robot designed to perform tasks automatically in an industrial facility.

[1095] A "maintenance schedule" is a planned schedule for maintaining, inspecting, and repairing equipment and facilities.

[1096] "Inventory management" is the process of monitoring and properly managing the quantity and condition of inventory, such as parts and raw materials, used within an industrial facility.

[1097] "Placing an order" is the act of ordering needed goods or services from a supplier.

[1098] "Analysis results" are the results of interpretation and understanding of information obtained via a speech recognition system or text analysis system.

[1099] "Business proposal" is the act of proposing to a user the optimal way to execute a business based on the analysis results.

[1100] "Automation" refers to a state in which machines and systems operate autonomously with reduced human intervention.

[1101] "Result reporting" is the act of notifying the user of the results of an executed task or process.

[1102] To realize the present invention, the following hardware and software are used.

[1103] Hardware

[1104] server

[1105] Devices (smartphones, tablets, PCs, etc.)

[1106] industrial robots

[1107] Voice input device

[1108] Internet-connected devices

[1109] software

[1110] Speech recognition systems (e.g., Google Cloud Speech-to-Text)

[1111] Text analysis systems (e.g. NLU / NLP engines)

[1112] Generative artificial intelligence (e.g. OpenAI GPT-4)

[1113] Robotic process automation systems (e.g., UiPath)

[1114] API Gateway (e.g. AWS API Gateway)

[1115] Text-to-speech systems (e.g., Amazon Polly)

[1116] Specific Embodiments of the Invention

[1117] 1. Receiving a user request

[1118] The user makes a request by voice or text, which is received by the device. In the case of voice input, the request is converted into text by a speech recognition system. This text data is then sent to the server for analysis.

[1119] 2. Request Analysis

[1120] The server analyzes the received text data using a generative AI model. The analysis results in the identification of the necessary action. For example, in response to a request to "check the next maintenance schedule," the action of creating a maintenance schedule is identified.

[1121] 3. Execution of Business

[1122] Based on the identified actions, the server executes tasks using the robotic process automation system, such as creating maintenance schedules, issuing work instructions for industrial robots, and ordering inventory shortages. For example, the server sends an instruction to an industrial robot to "perform a specific task" through the API gateway.

[1123] 4. Reporting the results

[1124] The server passes the results of the task execution to the generative AI model, which then creates a natural language text report of the results. The report is then converted into voice by a speech synthesis system and notified to the user via the device. For example, a report such as "The next maintenance will be on October 2nd" is made audibly.

[1125] Examples of concrete examples and prompts

[1126] Examples:

[1127] 1. User Request: "Check the next maintenance schedule"

[1128] 2. Execution result: The server creates a maintenance schedule and reports, "The next maintenance will be on October 2nd."

[1129] Example prompt sentence:

[1130] 1. "Check the next maintenance schedule"

[1131] 2. "Check availability"

[1132] 3. "Please give instructions to the industrial robot."

[1133] In this way, by using the appropriate hardware and software at each step, it is possible to automate and streamline operations within an industrial facility.

[1134] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1135] Step 1:

[1136] The user makes a request by voice or text. The device receives this request. For example, the user may say, "Check the next maintenance schedule." The device captures this voice and passes it to the voice recognition system.

[1137] Input: User's voice or text request

[1138] Output: Request converted into text by the speech recognition system

[1139] What it does: The device uses a microphone to capture the user's voice and then sends the voice data to a speech recognition system (e.g., Google Cloud Speech-to-Text) to convert it into text.

[1140] Step 2:

[1141] The terminal receives the request converted into text from the voice recognition system and sends it to the server.

[1142] Input: Text request from speech recognition system

[1143] Output: Text request received by the server

[1144] Specific operation: The terminal sends text data to the server using an HTTP request or the like.

[1145] Step 3:

[1146] The server then passes the received text request to a generative AI model for analysis. The generative AI model (e.g., OpenAI GPT-4) analyzes the text and identifies the required action.

[1147] Input: Text request details

[1148] Output: List of identified actions

[1149] Specific operation: The server inputs text data into the generative AI model, which then performs contextual analysis and keyword matching to list appropriate actions.

[1150] Step 4:

[1151] Based on the identified actions, the server issues instructions to a robotic process automation system (e.g., UiPath), which then automatically performs the necessary tasks, such as creating and executing maintenance schedules.

[1152] Input: A list of identified actions

[1153] Output: The results of the work performed

[1154] What it does: The server sends API requests to the robotic process automation system, which then executes automated scripts to create maintenance schedules and instruct industrial robots.

[1155] Step 5:

[1156] The server receives the execution results and passes them to a generative AI model to generate natural language text reporting the results.

[1157] Input: Result of the work performed

[1158] Output: Natural language text of the results report

[1159] How it works: The server receives the results from the robotic process automation system and inputs them into the generative AI model, which then generates text in natural language based on the results.

[1160] Step 6:

[1161] The server passes the generated report to a speech synthesis system (e.g., Amazon Polly) to convert it into voice, and the device notifies the user of the voiced report.

[1162] Input: Natural language text of the result report

[1163] Output: Audio report to notify the user

[1164] Specific operation: The server inputs the text from the generative AI model into the speech synthesis system to generate voice data, which the device plays back and reports to the user.

[1165] This series of steps automates and streamlines the management of work orders and maintenance schedules within industrial facilities.

[1166] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1167] This invention specifically realizes a personal AI secretary system that combines generative AI, robotic process automation (RPA), API gateway, speech recognition / synthesis AI, and an emotion engine. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's emotional state.

[1168] System Overview

[1169] 1. Receiving a request from a user:

[1170] The user sends a request in the form of voice or text. For example, a request might be, "Create tomorrow's sales report and register it in the sales management system."

[1171] The terminal receives this request and, if it is voice, converts it into text using a voice recognition system.

[1172] 2. Emotional state analysis:

[1173] The device uses an emotion engine to analyze the user's emotional state from the voice or text received, for example, to determine whether the user is stressed or relaxed.

[1174] 3. Parse the request:

[1175] The server passes the received text data to the generation AI, which analyzes the input and identifies the required action.

[1176] For example, the actions "create daily report" and "register sales management system" are identified.

[1177] 4. Business proposal:

[1178] The server then makes more appropriate work suggestions based on the user's emotional state and the analysis results. For example, if the user is feeling stressed, it will suggest simplifying the work.

[1179] 5. Execution of Business:

[1180] The server executes the task based on the specified action using robotic process automation (RPA), which automatically executes the specified business process.

[1181] For example, sales data is entered into a template for creating daily reports, and the data is then registered in a sales management system.

[1182] 6. Reporting results:

[1183] The server passes the results of the work execution to the generation AI, which then creates a natural language text report of the results. The report is generated with emotional consideration according to the situation.

[1184] The device uses speech synthesis AI to convert this report into audio and report it to the user. For example, it might say something like, "Based on your request, we have created a daily report and registered it in the sales management system. Don't worry, everything is going smoothly."

[1185] Specific examples

[1186] Example 1: Creating daily reports + automatic input to the sales management system

[1187] User: "Create tomorrow's sales report and register it in the sales management system."

[1188] The terminal converts the voice into text and sends the text "Create tomorrow's sales report and register it in the sales management system" to the server.

[1189] The server uses generation AI to analyze the text and identify the actions of "create daily report" and "register in sales management system."

[1190] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[1191] The server makes emotion-based business suggestions, for example, simplifying templates for creating daily reports.

[1192] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the sales management system's API.

[1193] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[1194] The device uses speech synthesis AI to convert the report into audio and notify the user.

[1195] Example 2: Meeting schedule adjustment + calendar integration

[1196] User: "Schedule our next team meeting."

[1197] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[1198] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[1199] The device uses an emotion engine to analyze the user's emotional state and detect when the user is relaxed.

[1200] The server instructs the RPA to use the calendar API to check the free time of all participants and set the optimal date.

[1201] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[1202] The device uses speech synthesis AI to convert the report into audio and notify the user.

[1203] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests and taking into account the user's emotional state, thereby reducing the burden on employees and improving work efficiency.

[1204] The processing flow will be explained below.

[1205] Example 1: Creating daily reports + automatic input into sales management system

[1206] Step 1:

[1207] The user requests, "Create tomorrow's sales report and register it in the sales management system."

[1208] Step 2:

[1209] The device receives the user's request. If the request is received by voice, it uses a voice recognition system to convert the voice into text.

[1210] Step 3:

[1211] The device converts the speech into text and sends it to the server, saying, "Create tomorrow's sales report and register it in the sales management system." This time, the data is sent via an HTTPS request.

[1212] Step 4:

[1213] The device uses an emotion engine to analyze the user's emotional state from the text data, and detects from the analysis results that the user is feeling stressed.

[1214] Step 5:

[1215] The server passes the received text data to the generation AI, which analyzes the text and identifies the actions of "create daily report" and "register in sales management system."

[1216] Step 6:

[1217] The server takes into account the user's emotional state and optimizes task suggestions. For example, if the user is feeling stressed, it will suggest ways to simplify daily report creation.

[1218] Step 7:

[1219] The server issues instructions to the Robotic Process Automation (RPA) system based on the identified actions. The necessary information is embedded in the template for "daily report creation" and the daily report is automatically generated.

[1220] Step 8:

[1221] The daily reports generated by RPA are automatically registered through the sales management system's API.

[1222] Step 9:

[1223] The RPA returns the execution results to the server as a log, indicating that the daily report has been created and registered in the sales management system.

[1224] Step 10:

[1225] The server passes the execution results to the generation AI, which then generates a report in natural language, such as, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[1226] Step 11:

[1227] The server sends the generated report to the device via an HTTPS request.

[1228] Step 12:

[1229] The device uses speech synthesis AI to convert the report into audio, generating a voice file that says, "The daily report has been created and registered in the sales management system. Things are progressing smoothly without any stress."

[1230] Step 13:

[1231] The device reports the generated audio to the user through a playback device (such as a speaker).

[1232] Example 2: Meeting schedule adjustment + calendar integration

[1233] Step 1:

[1234] A user requests, "Schedule our next team meeting."

[1235] Step 2:

[1236] The device receives the user's request. If the request is received by voice, it uses a voice recognition system to convert the voice into text.

[1237] Step 3:

[1238] The device sends the converted text "Schedule the next team meeting" to the server via an HTTPS request.

[1239] Step 4:

[1240] The device uses an emotion engine to analyze the user's emotional state from the text data, and detects from the analysis results that the user is relaxed.

[1241] Step 5:

[1242] The server passes the received text data to the generation AI, which analyzes the text and identifies the action to "schedule a meeting."

[1243] Step 6:

[1244] The server takes into account the user's emotional state and optimizes the task suggestions, for example, if the user is relaxed, it will suggest detailed schedule adjustments.

[1245] Step 7:

[1246] The server issues instructions to a robotic process automation (RPA) system based on the identified actions, and uses a calendar API to check the availability of all participants and set the optimal date.

[1247] Step 8:

[1248] RPA automatically schedules meetings through a calendar API.

[1249] Step 9:

[1250] The RPA returns the execution result to the server as a log, indicating that the meeting schedule has been arranged.

[1251] Step 10:

[1252] The server passes the execution results to the generation AI, which then generates a report in natural language, such as "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[1253] Step 11:

[1254] The server sends the generated report to the device via an HTTPS request.

[1255] Step 12:

[1256] The device uses speech synthesis AI to convert the report into audio, generating an audio file that says, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[1257] Step 13:

[1258] The device reports the generated audio to the user through a playback device (such as a speaker).

[1259] Through these specific processing steps, the task requested by the user is efficiently performed while taking into account the emotional state.

[1260] Example 2

[1261] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1262] Conventional voice assistant systems and automated systems often simply execute tasks based on user requests without considering the user's emotional state or work efficiency. As a result, they are unable to reduce the user's burden and stress, and their improvement in work efficiency is limited. Furthermore, if the execution result reports are not emotionally sensitive, they fail to provide sufficient satisfaction to the user.

[1263] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1264] In this invention, the server includes: means for receiving a user request by voice or text; means for analyzing the received request using a voice recognition system or a text analysis system; means including an emotion engine for analyzing the analysis results and the user's emotional state; means including generative artificial intelligence for identifying necessary actions based on the analysis results; means for executing tasks using a robotic process automation system for the identified actions; and means for converting the execution results into natural language text using the generative artificial intelligence and reporting them to the user using a speech synthesis system. This enables tasks to be proposed and executed taking into consideration the user's emotional state, thereby reducing the user's burden and improving work efficiency.

[1265] A "voice or text user request" is an action in which a user sends an instruction or request to the system in voice or text form.

[1266] A "speech recognition system" is a technique or device for converting voice input into text data.

[1267] A "text analysis system" is a technology or device for analyzing text data to understand its meaning and structure.

[1268] An "emotion engine" is a technique or device for analyzing data received from a user to identify their emotional state.

[1269] "Generative AI" is an AI technology that analyzes and generates data based on input data.

[1270] A "robotic process automation system" is a system that automatically carries out specified business processes.

[1271] "Natural language text" is text written in natural language that is generated by a computer system.

[1272] A "speech synthesis system" is a technology or device for converting text data into speech.

[1273] "Business proposal" is the act of proposing business optimization and procedure improvements based on the user's request and emotional state.

[1274] MODE FOR CARRYING OUT THE INVENTION

[1275] This invention specifically realizes a personal AI secretary system that combines generative artificial intelligence (generative AI), robotic process automation (RPA), an API gateway, a speech recognition and synthesis system, and an emotion engine. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's emotional state.

[1276] System Overview

[1277] First, the user issues a request in the form of voice or text. For example, a request might be, "Create tomorrow's sales report and register it in the sales management system." The device receives this request, and if it is voice, it converts it into text using a voice recognition system (e.g., voice recognition API). From the received voice or text, the device uses an emotion engine (e.g., emotion analysis API) to analyze the user's emotional state. For example, it determines whether the user is feeling stressed or relaxed.

[1278] Next, the server passes the text data to a generation AI (e.g., GPT-4), which analyzes the input and identifies the necessary actions. For example, the actions "Create a daily report" and "Register in a sales management system" may be identified. The server then makes more appropriate work suggestions based on the user's emotional state and the analysis results. For example, if the user is feeling stressed, it will make suggestions to simplify the work.

[1279] The server executes the task using robotic process automation (RPA) (e.g., RPA tool) based on the specified action. RPA automatically executes the specified business process. For example, sales data is entered into a template for creating daily reports, and the data is registered in a sales management system (e.g., business management system API).

[1280] Finally, the server passes the results of the work execution to the generation AI, which then creates a natural language text report of the results. The report is generated with emotional considerations depending on the situation. The device then converts this report into voice using a speech synthesis system (e.g., speech synthesis API) and reports it to the user. For example, it might say, "Based on your request, a daily report has been created and registered in the sales management system. Don't worry, everything is going smoothly."

[1281] Specific examples

[1282] Example 1: Creating daily reports + automatic input to the sales management system

[1283] User: "Create tomorrow's sales report and register it in the sales management system."

[1284] The terminal converts the voice into text and sends the text "Create tomorrow's sales report and register it in the sales management system" to the server.

[1285] The server uses generation AI to analyze the text and identify the actions of "create daily report" and "register in sales management system."

[1286] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[1287] The server makes emotion-based business suggestions, for example, simplifying templates for creating daily reports.

[1288] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the sales management system's API.

[1289] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[1290] The device uses speech synthesis AI to convert the report into audio and notify the user.

[1291] Example 2: Meeting schedule adjustment + calendar integration

[1292] User: "Schedule our next team meeting."

[1293] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[1294] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[1295] The device uses an emotion engine to analyze the user's emotional state and detect when the user is relaxed.

[1296] The server instructs the RPA to use a calendar API (e.g., calendar management system API) to check the free time of all participants and set the optimal date.

[1297] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[1298] The device uses speech synthesis AI to convert the report into audio and notify the user.

[1299] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests and taking into account the user's emotional state, thereby reducing the burden on employees and improving work efficiency.

[1300] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1301] Step 1:

[1302] The user sends a request by voice or text.

[1303] Specifically, the user inputs something like "Create tomorrow's sales report and register it in the sales management system" by voice or text. The input data is an audio file or a text file.

[1304] Step 2:

[1305] The terminal receives a voice or text request from the user.

[1306] In this step, the input is the audio file or text file from step 1, and the output is the audio or text data stored on the device. Specifically, data is received using a microphone or chat interface and temporarily stored.

[1307] Step 3:

[1308] The device uses a voice recognition system to convert the speech into text.

[1309] In the conversion process, the input is audio data and the output is text data. Specifically, a speech recognition system (e.g., a speech recognition API) is used to analyze the audio file and convert it into text.

[1310] Step 4:

[1311] The text data received by the device is sent to the emotion engine for analysis.

[1312] In this step, the input is text data and the output is data about the user's emotional state. Specifically, an emotion analysis API is used to analyze the user's emotions from the text data and identify emotional states such as "stress" or "relaxation."

[1313] Step 5:

[1314] The server sends the text data to the generation AI for analysis and identifies the necessary actions.

[1315] The input is text data and emotional state data, and the output is data that specifies actions. Specifically, the prompt sentence "Create tomorrow's sales report and register it in the sales management system" is input to the generation AI (e.g., GPT-4), and the actions "Create daily report" and "Register in the sales management system" are specified.

[1316] Step 6:

[1317] The server integrates the emotion data and action data and makes business suggestions based on the user's emotions.

[1318] The input is emotion data and action data, and the output is a specific business proposal. For example, the server generates a specific business proposal such as "Suggest simplifying templates for users with high stress levels."

[1319] Step 7:

[1320] The server executes the task using robotic process automation (RPA) based on the identified actions.

[1321] The input is the specified action data, and the output is the result of the business execution. Specifically, sales data is entered into a daily report creation template using an RPA tool (e.g., UiPath), and the data is registered via the sales management system's API.

[1322] Step 8:

[1323] The server passes the results of the work execution to the generation AI, which then creates a result report.

[1324] The input is the data on the results of the work execution, and the output is a report of the results in natural language. Using a generative AI (e.g., GPT-4), a report such as "A daily report has been created and registered in the sales management system. Things are progressing without stress" is generated.

[1325] Step 9:

[1326] The device passes the generated text to a speech synthesis system to convert it into speech.

[1327] The input is natural language text and the output is audio data. Specifically, the report text is converted into audio using a speech synthesis API.

[1328] Step 10:

[1329] The terminal reports the audio data to the user.

[1330] The output is an audio report that tells the user, "Based on your request, a daily report has been created and registered in the sales management system. Don't worry, everything is going smoothly."

[1331] (Application example 2)

[1332] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1333] This invention relates to a personal AI secretary system specialized for security services, and aims to automate and streamline security-related tasks while taking into account the user's emotional state. Conventional systems perform tasks uniformly without considering the user's emotional state, which can cause anxiety and stress to the user. Furthermore, the reporting of execution results is mechanical, which does not fully satisfy the user's psychological satisfaction. There is a need to solve these problems.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user request by voice or text, means for analyzing the received request using a voice recognition system or a text analysis system, and means including a generation AI for identifying a required action based on the analysis results. This enables a user to send a request by voice or text, and the system to automatically identify appropriate security-related tasks based on the content of the request and execute them taking into account the user's emotional state.

[1335] The system also includes a means for executing a task using a robotic process automation system for the identified action, a means for reporting the execution result to a user, an emotion engine that analyzes the user's emotional state and proposes a task based on the analyzed emotional state, and a means for executing the proposed task using the robotic process automation system. This enables security tasks to be executed and reported while taking the user's emotional state into consideration, thereby reducing the user's mental stress and increasing their satisfaction.

[1336] A "voice recognition system" refers to technology that converts voice information into text data.

[1337] A "text analysis system" refers to a technology that analyzes text data and performs processes such as semantic analysis and document classification.

[1338] "Generative AI" refers to artificial intelligence technology that analyzes user requests and generates appropriate actions and suggestions.

[1339] "Robotic process automation system" refers to a software robot that automatically executes a specified business process.

[1340] An "emotion engine" is a system that analyzes a user's emotional state from their voice and text and determines their state of stress, relaxation, etc.

[1341] A "speech synthesis system" refers to a technology that converts text data into speech to generate synthetic speech.

[1342] "Natural language text" refers to text data in the form of language used by people in everyday conversation and writing.

[1343] This invention provides a personal AI secretary system specialized for security services, and specifically realizes a system that automates and efficiently executes security-related tasks while taking into account the user's emotional state.

[1344] System configuration and operation

[1345] The system mainly consists of the following components:

[1346] 1. A device that receives user requests via voice or text

[1347] The terminal uses a speech recognition system (for example, Google Speech Recognition API) to convert the speech into text data.

[1348] 2. A server that analyzes the received request using a voice recognition system or text analysis system.

[1349] The server uses a generative AI (e.g., OpenAI's GPT-3) to analyze the received text data and identify the necessary actions.

[1350] 3. Emotion engine that analyzes emotional states

[1351] The analysis system determines emotions from the user's voice or text data, for example using a proprietary emotion engine to determine whether the user is stressed or relaxed.

[1352] 4. A server that executes tasks using a robotic process automation system for the identified actions.

[1353] Identified actions are then executed through robotic process automation (RPA) systems, for example to perform security-related tasks such as increasing security camera monitoring or activating alarm systems.

[1354] 5. Server that converts the execution results into natural language text using generation AI

[1355] The execution results are converted into natural language text by the generative AI and reported to the user.

[1356] 6. A device that converts the converted text into speech using a speech synthesis system

[1357] The text data sent from the server is converted into voice using a speech synthesis system (e.g., Pyttsx3) and reported to the user through the terminal.

[1358] Specific examples

[1359] Example 1: Request for increased security at home

[1360] User: "Tighten up the security at home tonight."

[1361] The device converts the speech into text and sends the text "Enhance home security tonight" to the server.

[1362] The server uses generative AI to analyze the text and identify the action to "strengthen security systems."

[1363] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[1364] The server makes business suggestions based on emotions, such as suggesting strengthening surveillance camera monitoring or activating an alarm system.

[1365] The server instructs the RPA to execute the process of strengthening the security system.

[1366] The server passes the execution results to the generation AI, which generates a report saying, "Security has been strengthened. Everything is going well. Rest in peace tonight."

[1367] The terminal uses a speech synthesis system to convert the report into voice and notify the user.

[1368] Prompt Sentence Examples

[1369] "User Request: Increase security at home tonight. Please identify the necessary security tasks."

[1370] In this way, the present invention provides for the automatic execution and efficient processing of security tasks based on user requests and taking into account emotional state.

[1371] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1372] Step 1:

[1373] The user inputs a request by voice or text. The input voice data is a specific request such as "Please tighten the security at my house tonight." This data is input into the voice recognition system.

[1374] Step 2:

[1375] The device uses a voice recognition system (e.g., Google Speech Recognition API) to convert the voice data into text data. The voice data, "Please strengthen the security of my home tonight," is output as text data.

[1376] Step 3:

[1377] The server inputs text data into a generative AI (e.g., OpenAI GPT-3) for analysis. The server inputs the prompt "User request: Please strengthen the security of my home tonight. Please identify the necessary security tasks" into the generative AI model and obtains the analysis result. The identified action, "Strengthen the security system," is output as the analysis result.

[1378] Step 4:

[1379] The device uses an emotion engine to analyze the user's emotional state. Text data is input into the emotion engine, which determines whether the user is feeling stressed or relaxed. The analysis result is output as "stressed."

[1380] Step 5:

[1381] The server makes business proposals based on the analysis results and the emotional state. Using a generative AI model, the server generates proposals appropriate to the emotional state. For example, specific proposals such as "strengthen surveillance camera monitoring" or "activate the alarm system" are output.

[1382] Step 6:

[1383] The server uses a robotic process automation (RPA) system to execute the proposed security tasks. The server inputs instruction data such as "strengthen surveillance camera monitoring" or "activate the alarm system" into the RPA and obtains the execution results. The execution result is output as a message that the security system has been strengthened.

[1384] Step 7:

[1385] The server uses generative AI to convert the execution results into natural language text. The execution result data is input into the generative AI model, and a report is generated that reads, "Security has been strengthened. Everything is going well. Rest easy tonight."

[1386] Step 8:

[1387] The device uses a speech synthesis system (e.g., Pyttsx3) to convert the generated report into voice and notify the user. The generated report text is input into the speech synthesis system, output as voice data, and reported to the user.

[1388] 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.

[1389] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1390] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1391] [Fourth embodiment]

[1392] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1393] 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.

[1394] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1395] 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.

[1396] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1397] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1398] 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.

[1399] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1400] 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.

[1401] 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 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.

[1402] 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.

[1403] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1404] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1405] This invention specifically realizes a personal AI secretary system that combines generative AI, robotic process automation (RPA), API gateway, and speech recognition and synthesis AI. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's attributes.

[1406] System Overview

[1407] 1. Receiving a request from a user:

[1408] The user sends a request in the form of voice or text. For example, a request might be, "Create today's sales report and register it in the sales management system."

[1409] The terminal receives this request and, if it is voice, converts it into text using a voice recognition system.

[1410] 2. Parse the request:

[1411] The server sends the received text data to the generation AI, which analyzes the input and identifies the necessary actions.

[1412] For example, the actions "Create a daily report" and "Register in SFA (Sales Management System)" are identified.

[1413] 3. Execution of Business:

[1414] The server executes the task based on the specified action using robotic process automation (RPA), which automatically executes the specified business process.

[1415] For example, sales data is entered into a template for creating daily reports, and the data is then registered in the SFA.

[1416] 4. Reporting the results:

[1417] The server passes the results of the business execution to the generation AI, which then creates a natural language text report of the results.

[1418] The device uses voice synthesis AI to convert this report into audio and report it to the user.

[1419] For example, the content might be something like, "A daily report has been created and registered in the sales management system."

[1420] Specific examples

[1421] Example 1: Creating daily reports + SFA automatic input

[1422] User: "Create today's sales report and register it in SFA."

[1423] The terminal converts the voice into text and sends the text "Create today's sales report and register it with the SFA" to the server.

[1424] The server uses the generated AI to analyze the text and identify the actions of "create daily report" and "register SFA."

[1425] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the SFA system's API.

[1426] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered with SFA."

[1427] The device uses speech synthesis AI to convert the report into audio and notify the user.

[1428] Example 2: Meeting schedule adjustment + calendar integration

[1429] User: "Schedule our next team meeting."

[1430] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[1431] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[1432] The server instructs the RPA to use the calendar API to check the participants' free times and set the optimal date.

[1433] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time."

[1434] The device uses speech synthesis AI to convert the report into audio and notify the user.

[1435] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests, thereby reducing the burden on employees and improving work efficiency.

[1436] The processing flow will be explained below.

[1437] Step 1:

[1438] Users can send requests to their personal AI secretary via voice or text, for example, "Create tomorrow's sales report and register it in the sales management system."

[1439] Step 2:

[1440] The device receives the user's request. If it is a voice request, it uses a voice recognition system to convert the voice into text. For example, "Create tomorrow's sales report and register it in the sales management system."

[1441] Step 3:

[1442] The device sends the converted text data to the server, which then sends the text data as an HTTPS request to the server for analysis.

[1443] Step 4:

[1444] The server passes the received text data to the generation AI, which prepares to analyze the text data.

[1445] Step 5:

[1446] The generative AI analyzes the text and identifies the necessary actions, such as "create a daily report" and "register in the sales management system."

[1447] Step 6:

[1448] Based on the identified action, the server passes the appropriate RPA script to the RPA system, which initiates the business automation process.

[1449] Step 7:

[1450] The RPA executes the "Daily Report Creation" script, automatically generating a daily report based on a pre-defined template and filling in the necessary data.

[1451] Step 8:

[1452] The RPA executes the "Sales Management System Registration" script, and the generated daily report data is automatically registered in the system via the sales management system (SFA) API.

[1453] Step 9:

[1454] The RPA reports the execution results to the server, and sends log data indicating that the daily report has been created and registered in the sales management system.

[1455] Step 10:

[1456] The server passes the execution results to the generation AI, which then generates a report. The generation AI creates a natural language report such as, "A daily report has been created and registered in the sales management system."

[1457] Step 11:

[1458] The server sends the generated report to the terminal, which sends the report as an HTTPS request.

[1459] Step 12:

[1460] The device uses a speech synthesis AI to convert the report into audio, which then generates a voice file saying, "The daily report has been created and registered in the sales management system."

[1461] Step 13:

[1462] The device notifies the user of the generated audio. The audio file is reported to the user through a playback device (such as a speaker).

[1463] Through these specific processing steps, daily tasks based on user requests are automated and efficiently performed.

[1464] Example 1

[1465] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1466] Conventional business automation systems have had difficulty accurately receiving users' voice requests, appropriately analyzing the content of those requests, efficiently processing them, and quickly providing feedback on the results. Therefore, there is a need for a system that integrates speech recognition, text analysis, generative artificial intelligence, robotic process automation, and speech synthesis technologies to consistently automate tasks based on user requests.

[1467] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1468] In this invention, the server includes means for receiving user requests by voice or text, means for analyzing the received requests using a voice recognition system or a text analysis system, means including a generative artificial intelligence for identifying necessary actions based on the analysis results, means for executing tasks using a robotic process automation system for the identified actions, and means for converting the execution results into natural language text using the generative artificial intelligence and vocalizing them using a speech synthesis system to report them to the user. This makes it possible to process user requests accurately and efficiently and quickly provide feedback on the results.

[1469] A "means for receiving a voice or text user request" is a device or software for receiving a voice or text request from a user.

[1470] A "speech recognition system" is a technology or device that receives voice data as input, analyzes it, and converts it into text data.

[1471] A "text analysis system" is a technology or device that analyzes received text, understands its content, and extracts necessary information.

[1472] "Generative AI" refers to artificial intelligence techniques used to analyze text or instructions and identify appropriate actions or generate natural language text.

[1473] A "robotic process automation system" is software or a device that automatically executes business processes and efficiently handles repetitive tasks.

[1474] A "speech synthesis system" is a technology or device that converts text data into voice data and generates artificial voices.

[1475] "Natural language text" refers to text data written in a natural format, such as human speech.

[1476] A "user request" is an instruction or request that a user makes to the system.

[1477] "Analysis results" refer to the results of analysis obtained by a speech recognition system or text analysis system.

[1478] "Business execution results" refer to the results obtained after a robotic process automation system executes a business.

[1479] A "reporting means" is a method or device used to communicate the results of an execution to a user.

[1480] This invention specifically realizes a personal AI secretary system that combines generative artificial intelligence (AI), robotic process automation (RPA), an API gateway, and speech recognition and synthesis AI. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's attributes.

[1481] System Program Overview

[1482] 1. Receiving a request from a user:

[1483] Users can send requests in voice or text format, such as "Create today's sales report and register it in the sales management system."

[1484] The device receives this request and, if it is voice, converts it into text using speech recognition software, specifically Google Cloud Speech-to-Text.

[1485] 2. Analysis of the request:

[1486] The device sends the received text data to a server, which then sends the text to a generator AI for analysis. OpenAI GPT-4 is sometimes used as the generator AI.

[1487] The generative AI analyzes the input and identifies the necessary actions, such as "create a daily report" and "register in the sales management system (SFA)."

[1488] 3. Execution of Business:

[1489] The server executes the identified actions using robotic process automation (RPA), an example of which is UiPath.

[1490] Sales data is entered into a template for creating daily reports, and the data is registered in a sales management system (e.g., Salesforce).

[1491] 4. Reporting the results:

[1492] The server passes the results of the work execution to the generation AI, which generates a result report in natural language text.

[1493] The device will then convert the report into voice using a speech synthesis AI, such as Amazon Polly.

[1494] For example, a report may be made stating, "A daily report has been created and registered in the sales management system."

[1495] Specific examples

[1496] Example 1: Creating daily reports + SFA automatic input

[1497] User: "Create today's sales report and register it in SFA."

[1498] The terminal converts the voice into text and sends the text "Create today's sales report and register it with the SFA" to the server.

[1499] The server analyzes the text using the generated AI (OpenAI GPT-4) and identifies the actions of "create daily report" and "register SFA."

[1500] The server instructs the RPA (UiPath) to automatically create daily reports and execute the process of registering the daily reports in the SFA (such as Salesforce's API).

[1501] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered with SFA."

[1502] The device uses a speech synthesis AI (Amazon Polly) to convert the report into audio and notify the user.

[1503] Example 2: Meeting schedule adjustment + calendar integration

[1504] User: "Schedule our next team meeting."

[1505] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[1506] The server analyzes the text using generative AI (OpenAI GPT-4) and identifies the action "schedule a meeting."

[1507] The server instructs RPA (Automation Anywhere) to use a calendar API (such as Google Calendar API) to check the participants' free time and perform the process of setting the optimal date.

[1508] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time."

[1509] The device uses a speech synthesis AI (Amazon Polly) to convert the report into audio and notify the user.

[1510] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests, thereby enabling users to automate and streamline their tasks while saving time and effort.

[1511] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1512] Step 1:

[1513] The user sends a request in voice or text format. For example, a request might be made to "create today's sales report and register it in the sales management system." The device receives this request. In the case of voice format, the device converts the voice to text using Google Cloud Speech-to-Text. The input here is voice data, and the output is the converted text data.

[1514] Specific behavior:

[1515] The user speaks to their smartphone, saying, "Create today's sales report and register it with SFA."

[1516] The device records the audio data and sends it to the Google Cloud Speech-to-Text API.

[1517] Google Cloud Speech-to-Text analyzes the audio data and generates text data such as "Create today's sales report and register it with SFA."

[1518] Step 2:

[1519] The device sends the converted text data to the server. The server passes the received text data to the generation AI (OpenAI GPT-4) for analysis. The generation AI analyzes the text content and identifies the necessary actions. The input here is the text data, and the output is a list of analyzed actions.

[1520] Specific behavior:

[1521] The terminal sends the text data "Create today's sales report and register it in the SFA" to the server via an HTTP request.

[1522] The server passes the text data to the generation AI along with a prompt saying, "Please analyze the request and identify the required action."

[1523] The generation AI analyzes the text content and identifies the actions "create daily report" and "register SFA."

[1524] Step 3:

[1525] The server executes the tasks based on the specified actions using Robotic Process Automation (RPA: UiPath). RPA automates the specified business process. The input here is a list of actions, and the output is the results of the executed tasks.

[1526] Specific behavior:

[1527] The server instructs the RPA (UiPath) API to create daily reports and register them in the SFA.

[1528] RPA opens the daily report template and automatically enters sales data into the template.

[1529] The daily reports completed by RPA are registered in the sales management system (such as Salesforce API).

[1530] Step 4:

[1531] The server passes the business execution results received from the RPA to the generation AI, which then generates a result report in natural language text. Here, the input is the business execution results, and the output is natural language text.

[1532] Specific behavior:

[1533] The server passes the prompt message to the generation AI: "Please explain the process of creating a daily report and registering it with SFA."

[1534] The generation AI generates a report stating, "A daily report has been created and registered with SFA."

[1535] Step 5:

[1536] The device receives the generated report, converts it into speech using speech synthesis software (Amazon Polly), and reports it to the user. The input here is natural language text, and the output is speech data.

[1537] Specific behavior:

[1538] The terminal receives the report "The daily report has been created and registered in the sales management system."

[1539] The device inputs the report into Amazon Polly and generates voice data.

[1540] The terminal plays back the voiced report and notifies the user that "the daily report has been created and registered in the sales management system."

[1541] (Application example 1)

[1542] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1543] Improving work efficiency and reducing costs are key challenges in modern industrial facilities. However, many tasks, such as communicating work instructions, managing maintenance schedules, and managing inventory, are still performed manually, which can be error-prone and time-consuming. Furthermore, these tasks require advanced skills, leading to labor shortages and increased training costs. The purpose of this invention is to solve these challenges and automate and streamline work in industrial facilities.

[1544] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1545] In this invention, the server includes: means for receiving user requests by voice or text; means for analyzing the received requests using a voice recognition system or a text analysis system; means including a generative artificial intelligence for identifying necessary actions based on the analysis results; means for executing tasks using a robotic process automation system for the identified actions; means for reporting the execution results to the user; means for receiving voice instructions at the industrial facility and automatically executing tasks by industrial robots based on the instructions; means for automatically creating a maintenance schedule for industrial equipment and executing necessary maintenance tasks based on the schedule; and means for automatically managing inventory within the industrial facility and automatically placing orders when inventory is low, thereby enabling improved work efficiency and cost reduction within the industrial facility.

[1546] A "voice recognition system" is a system that converts voice into a digital signal, analyzes the content, and converts it into text format.

[1547] A "text analysis system" is a system that analyzes textual information and understands its meaning and context.

[1548] "Generative AI" is AI that identifies necessary actions based on given data and generates appropriate responses and work instructions based on that content.

[1549] A "robotic process automation system" is a system that uses software robots to automate routine business processes.

[1550] An "industrial facility" is an industrial facility where manufacturing, processing, etc. is carried out, including factories and plants.

[1551] An "industrial robot" is a robot designed to perform tasks automatically in an industrial facility.

[1552] A "maintenance schedule" is a planned schedule for maintaining, inspecting, and repairing equipment and facilities.

[1553] "Inventory management" is the process of monitoring and properly managing the quantity and condition of inventory, such as parts and raw materials, used within an industrial facility.

[1554] "Placing an order" is the act of ordering needed goods or services from a supplier.

[1555] "Analysis results" are the results of interpretation and understanding of information obtained via a speech recognition system or text analysis system.

[1556] "Business proposal" is the act of proposing to a user the optimal way to execute a business based on the analysis results.

[1557] "Automation" refers to a state in which machines and systems operate autonomously with reduced human intervention.

[1558] "Result reporting" is the act of notifying the user of the results of an executed task or process.

[1559] To realize the present invention, the following hardware and software are used.

[1560] Hardware

[1561] server

[1562] Devices (smartphones, tablets, PCs, etc.)

[1563] industrial robots

[1564] Voice input device

[1565] Internet-connected devices

[1566] software

[1567] Speech recognition systems (e.g., Google Cloud Speech-to-Text)

[1568] Text analysis systems (e.g. NLU / NLP engines)

[1569] Generative artificial intelligence (e.g. OpenAI GPT-4)

[1570] Robotic process automation systems (e.g., UiPath)

[1571] API Gateway (e.g. AWS API Gateway)

[1572] Text-to-speech systems (e.g., Amazon Polly)

[1573] Specific Embodiments of the Invention

[1574] 1. Receiving a user request

[1575] The user makes a request by voice or text, which is received by the device. In the case of voice input, the request is converted into text by a speech recognition system. This text data is then sent to the server for analysis.

[1576] 2. Request Analysis

[1577] The server analyzes the received text data using a generative AI model. The analysis results in the identification of the necessary action. For example, in response to a request to "check the next maintenance schedule," the action of creating a maintenance schedule is identified.

[1578] 3. Execution of Business

[1579] Based on the identified actions, the server executes tasks using the robotic process automation system, such as creating maintenance schedules, issuing work instructions for industrial robots, and ordering inventory shortages. For example, the server sends an instruction to an industrial robot to "perform a specific task" through the API gateway.

[1580] 4. Reporting the results

[1581] The server passes the results of the task execution to the generative AI model, which then creates a natural language text report of the results. The report is then converted into voice by a speech synthesis system and notified to the user via the device. For example, a report such as "The next maintenance will be on October 2nd" is made audibly.

[1582] Examples of concrete examples and prompts

[1583] Examples:

[1584] 1. User Request: "Check the next maintenance schedule"

[1585] 2. Execution result: The server creates a maintenance schedule and reports, "The next maintenance will be on October 2nd."

[1586] Example prompt sentence:

[1587] 1. "Check the next maintenance schedule"

[1588] 2. "Check availability"

[1589] 3. "Please give instructions to the industrial robot."

[1590] In this way, by using the appropriate hardware and software at each step, it is possible to automate and streamline operations within an industrial facility.

[1591] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1592] Step 1:

[1593] The user makes a request by voice or text. The device receives this request. For example, the user may say, "Check the next maintenance schedule." The device captures this voice and passes it to the voice recognition system.

[1594] Input: User's voice or text request

[1595] Output: Request converted into text by the speech recognition system

[1596] What it does: The device uses a microphone to capture the user's voice and then sends the voice data to a speech recognition system (e.g., Google Cloud Speech-to-Text) to convert it into text.

[1597] Step 2:

[1598] The terminal receives the request converted into text from the voice recognition system and sends it to the server.

[1599] Input: Text request from speech recognition system

[1600] Output: Text request received by the server

[1601] Specific operation: The terminal sends text data to the server using an HTTP request or the like.

[1602] Step 3:

[1603] The server then passes the received text request to a generative AI model for analysis. The generative AI model (e.g., OpenAI GPT-4) analyzes the text and identifies the required action.

[1604] Input: Text request details

[1605] Output: List of identified actions

[1606] Specific operation: The server inputs text data into the generative AI model, which then performs contextual analysis and keyword matching to list appropriate actions.

[1607] Step 4:

[1608] Based on the identified actions, the server issues instructions to a robotic process automation system (e.g., UiPath), which then automatically performs the necessary tasks, such as creating and executing maintenance schedules.

[1609] Input: A list of identified actions

[1610] Output: The results of the work performed

[1611] What it does: The server sends API requests to the robotic process automation system, which then executes automated scripts to create maintenance schedules and instruct industrial robots.

[1612] Step 5:

[1613] The server receives the execution results and passes them to a generative AI model to generate natural language text reporting the results.

[1614] Input: Result of the work performed

[1615] Output: Natural language text of the results report

[1616] How it works: The server receives the results from the robotic process automation system and inputs them into the generative AI model, which then generates text in natural language based on the results.

[1617] Step 6:

[1618] The server passes the generated report to a speech synthesis system (e.g., Amazon Polly) to convert it into voice, and the device notifies the user of the voiced report.

[1619] Input: Natural language text of the result report

[1620] Output: Audio report to notify the user

[1621] Specific operation: The server inputs the text from the generative AI model into the speech synthesis system to generate voice data, which the device plays back and reports to the user.

[1622] This series of steps automates and streamlines the management of work orders and maintenance schedules within industrial facilities.

[1623] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1624] This invention specifically realizes a personal AI secretary system that combines generative AI, robotic process automation (RPA), API gateway, speech recognition / synthesis AI, and an emotion engine. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's emotional state.

[1625] System Overview

[1626] 1. Receiving a request from a user:

[1627] The user sends a request in the form of voice or text. For example, a request might be, "Create tomorrow's sales report and register it in the sales management system."

[1628] The terminal receives this request and, if it is voice, converts it into text using a voice recognition system.

[1629] 2. Emotional state analysis:

[1630] The device uses an emotion engine to analyze the user's emotional state from the voice or text received, for example, to determine whether the user is stressed or relaxed.

[1631] 3. Parse the request:

[1632] The server passes the received text data to the generation AI, which analyzes the input and identifies the required action.

[1633] For example, the actions "create daily report" and "register sales management system" are identified.

[1634] 4. Business proposal:

[1635] The server then makes more appropriate work suggestions based on the user's emotional state and the analysis results. For example, if the user is feeling stressed, it will suggest simplifying the work.

[1636] 5. Execution of Business:

[1637] The server executes the task based on the specified action using robotic process automation (RPA), which automatically executes the specified business process.

[1638] For example, sales data is entered into a template for creating daily reports, and the data is then registered in a sales management system.

[1639] 6. Reporting results:

[1640] The server passes the results of the work execution to the generation AI, which then creates a natural language text report of the results. The report is generated with emotional consideration according to the situation.

[1641] The device uses speech synthesis AI to convert this report into audio and report it to the user. For example, it might say something like, "Based on your request, we have created a daily report and registered it in the sales management system. Don't worry, everything is going smoothly."

[1642] Specific examples

[1643] Example 1: Creating daily reports + automatic input to the sales management system

[1644] User: "Create tomorrow's sales report and register it in the sales management system."

[1645] The terminal converts the voice into text and sends the text "Create tomorrow's sales report and register it in the sales management system" to the server.

[1646] The server uses generation AI to analyze the text and identify the actions of "create daily report" and "register in sales management system."

[1647] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[1648] The server makes emotion-based business suggestions, for example, simplifying templates for creating daily reports.

[1649] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the sales management system's API.

[1650] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[1651] The device uses speech synthesis AI to convert the report into audio and notify the user.

[1652] Example 2: Meeting schedule adjustment + calendar integration

[1653] User: "Schedule our next team meeting."

[1654] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[1655] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[1656] The device uses an emotion engine to analyze the user's emotional state and detect when the user is relaxed.

[1657] The server instructs the RPA to use the calendar API to check the free time of all participants and set the optimal date.

[1658] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[1659] The device uses speech synthesis AI to convert the report into audio and notify the user.

[1660] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests and taking into account the user's emotional state, thereby reducing the burden on employees and improving work efficiency.

[1661] The processing flow will be explained below.

[1662] Example 1: Creating daily reports + automatic input into sales management system

[1663] Step 1:

[1664] The user requests, "Create tomorrow's sales report and register it in the sales management system."

[1665] Step 2:

[1666] The device receives the user's request. If the request is received by voice, it uses a voice recognition system to convert the voice into text.

[1667] Step 3:

[1668] The device converts the speech into text and sends it to the server, saying, "Create tomorrow's sales report and register it in the sales management system." This time, the data is sent via an HTTPS request.

[1669] Step 4:

[1670] The device uses an emotion engine to analyze the user's emotional state from the text data, and detects from the analysis results that the user is feeling stressed.

[1671] Step 5:

[1672] The server passes the received text data to the generation AI, which analyzes the text and identifies the actions of "create daily report" and "register in sales management system."

[1673] Step 6:

[1674] The server takes into account the user's emotional state and optimizes task suggestions. For example, if the user is feeling stressed, it will suggest ways to simplify daily report creation.

[1675] Step 7:

[1676] The server issues instructions to the Robotic Process Automation (RPA) system based on the identified actions. The necessary information is embedded in the template for "daily report creation" and the daily report is automatically generated.

[1677] Step 8:

[1678] The daily reports generated by RPA are automatically registered through the sales management system's API.

[1679] Step 9:

[1680] The RPA returns the execution results to the server as a log, indicating that the daily report has been created and registered in the sales management system.

[1681] Step 10:

[1682] The server passes the execution results to the generation AI, which then generates a report in natural language, such as, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[1683] Step 11:

[1684] The server sends the generated report to the device via an HTTPS request.

[1685] Step 12:

[1686] The device uses speech synthesis AI to convert the report into audio, generating a voice file that says, "The daily report has been created and registered in the sales management system. Things are progressing smoothly without any stress."

[1687] Step 13:

[1688] The device reports the generated audio to the user through a playback device (such as a speaker).

[1689] Example 2: Meeting schedule adjustment + calendar integration

[1690] Step 1:

[1691] A user requests, "Schedule our next team meeting."

[1692] Step 2:

[1693] The device receives the user's request. If the request is received by voice, it uses a voice recognition system to convert the voice into text.

[1694] Step 3:

[1695] The device sends the converted text "Schedule the next team meeting" to the server via an HTTPS request.

[1696] Step 4:

[1697] The device uses an emotion engine to analyze the user's emotional state from the text data, and detects from the analysis results that the user is relaxed.

[1698] Step 5:

[1699] The server passes the received text data to the generation AI, which analyzes the text and identifies the action to "schedule a meeting."

[1700] Step 6:

[1701] The server takes into account the user's emotional state and optimizes the task suggestions, for example, if the user is relaxed, it will suggest detailed schedule adjustments.

[1702] Step 7:

[1703] The server issues instructions to a robotic process automation (RPA) system based on the identified actions, and uses a calendar API to check the availability of all participants and set the optimal date.

[1704] Step 8:

[1705] RPA automatically schedules meetings through a calendar API.

[1706] Step 9:

[1707] The RPA returns the execution result to the server as a log, indicating that the meeting schedule has been arranged.

[1708] Step 10:

[1709] The server passes the execution results to the generation AI, which then generates a report in natural language, such as "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[1710] Step 11:

[1711] The server sends the generated report to the device via an HTTPS request.

[1712] Step 12:

[1713] The device uses speech synthesis AI to convert the report into audio, generating an audio file that says, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[1714] Step 13:

[1715] The device reports the generated audio to the user through a playback device (such as a speaker).

[1716] Through these specific processing steps, the task requested by the user is efficiently performed while taking into account the emotional state.

[1717] Example 2

[1718] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1719] Conventional voice assistant systems and automated systems often simply execute tasks based on user requests without considering the user's emotional state or work efficiency. As a result, they are unable to reduce the user's burden and stress, and their improvement in work efficiency is limited. Furthermore, if the execution result reports are not emotionally sensitive, they fail to provide sufficient satisfaction to the user.

[1720] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1721] In this invention, the server includes: means for receiving a user request by voice or text; means for analyzing the received request using a voice recognition system or a text analysis system; means including an emotion engine for analyzing the analysis results and the user's emotional state; means including generative artificial intelligence for identifying necessary actions based on the analysis results; means for executing tasks using a robotic process automation system for the identified actions; and means for converting the execution results into natural language text using the generative artificial intelligence and reporting them to the user using a speech synthesis system. This enables tasks to be proposed and executed taking into consideration the user's emotional state, thereby reducing the user's burden and improving work efficiency.

[1722] A "voice or text user request" is an action in which a user sends an instruction or request to the system in voice or text form.

[1723] A "speech recognition system" is a technique or device for converting voice input into text data.

[1724] A "text analysis system" is a technology or device for analyzing text data to understand its meaning and structure.

[1725] An "emotion engine" is a technique or device for analyzing data received from a user to identify their emotional state.

[1726] "Generative AI" is an AI technology that analyzes and generates data based on input data.

[1727] A "robotic process automation system" is a system that automatically carries out specified business processes.

[1728] "Natural language text" is text written in natural language that is generated by a computer system.

[1729] A "speech synthesis system" is a technology or device for converting text data into speech.

[1730] "Business proposal" is the act of proposing business optimization and procedure improvements based on the user's request and emotional state.

[1731] MODE FOR CARRYING OUT THE INVENTION

[1732] This invention specifically realizes a personal AI secretary system that combines generative artificial intelligence (generative AI), robotic process automation (RPA), an API gateway, a speech recognition and synthesis system, and an emotion engine. This system is assigned to each user individually and aims to automate and streamline daily tasks while taking into account the user's emotional state.

[1733] System Overview

[1734] First, the user issues a request in the form of voice or text. For example, a request might be, "Create tomorrow's sales report and register it in the sales management system." The device receives this request, and if it is voice, it converts it into text using a voice recognition system (e.g., voice recognition API). From the received voice or text, the device uses an emotion engine (e.g., emotion analysis API) to analyze the user's emotional state. For example, it determines whether the user is feeling stressed or relaxed.

[1735] Next, the server passes the text data to a generation AI (e.g., GPT-4), which analyzes the input and identifies the necessary actions. For example, the actions "Create a daily report" and "Register in a sales management system" may be identified. The server then makes more appropriate work suggestions based on the user's emotional state and the analysis results. For example, if the user is feeling stressed, it will make suggestions to simplify the work.

[1736] The server executes the task using robotic process automation (RPA) (e.g., RPA tool) based on the specified action. RPA automatically executes the specified business process. For example, sales data is entered into a template for creating daily reports, and the data is registered in a sales management system (e.g., business management system API).

[1737] Finally, the server passes the results of the work execution to the generation AI, which then creates a natural language text report of the results. The report is generated with emotional considerations depending on the situation. The device then converts this report into voice using a speech synthesis system (e.g., speech synthesis API) and reports it to the user. For example, it might say, "Based on your request, a daily report has been created and registered in the sales management system. Don't worry, everything is going smoothly."

[1738] Specific examples

[1739] Example 1: Creating daily reports + automatic input to the sales management system

[1740] User: "Create tomorrow's sales report and register it in the sales management system."

[1741] The terminal converts the voice into text and sends the text "Create tomorrow's sales report and register it in the sales management system" to the server.

[1742] The server uses generation AI to analyze the text and identify the actions of "create daily report" and "register in sales management system."

[1743] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[1744] The server makes emotion-based business suggestions, for example, simplifying templates for creating daily reports.

[1745] The server instructs the RPA to automatically create daily reports and executes the process of registering the daily reports via the sales management system's API.

[1746] The server passes the execution results to the generation AI, which generates a report stating, "A daily report has been created and registered in the sales management system. Things are progressing without stress."

[1747] The device uses speech synthesis AI to convert the report into audio and notify the user.

[1748] Example 2: Meeting schedule adjustment + calendar integration

[1749] User: "Schedule our next team meeting."

[1750] The device converts the speech to text and sends the text "Schedule the next team meeting" to the server.

[1751] The server uses generative AI to analyze the text and identify the action "schedule a meeting."

[1752] The device uses an emotion engine to analyze the user's emotional state and detect when the user is relaxed.

[1753] The server instructs the RPA to use a calendar API (e.g., calendar management system API) to check the free time of all participants and set the optimal date.

[1754] The server passes the execution results to the generation AI, which generates a report saying, "The next meeting has been scheduled for XX / XX / XX time. Please take your time to prepare."

[1755] The device uses speech synthesis AI to convert the report into audio and notify the user.

[1756] In this way, the present invention provides a system that automates and efficiently processes various tasks based on user requests and taking into account the user's emotional state, thereby reducing the burden on employees and improving work efficiency.

[1757] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1758] Step 1:

[1759] The user sends a request by voice or text.

[1760] Specifically, the user inputs something like "Create tomorrow's sales report and register it in the sales management system" by voice or text. The input data is an audio file or a text file.

[1761] Step 2:

[1762] The terminal receives a voice or text request from the user.

[1763] In this step, the input is the audio file or text file from step 1, and the output is the audio or text data stored on the device. Specifically, data is received using a microphone or chat interface and temporarily stored.

[1764] Step 3:

[1765] The device uses a voice recognition system to convert the speech into text.

[1766] In the conversion process, the input is audio data and the output is text data. Specifically, a speech recognition system (e.g., a speech recognition API) is used to analyze the audio file and convert it into text.

[1767] Step 4:

[1768] The text data received by the device is sent to the emotion engine for analysis.

[1769] In this step, the input is text data and the output is data about the user's emotional state. Specifically, an emotion analysis API is used to analyze the user's emotions from the text data and identify emotional states such as "stress" or "relaxation."

[1770] Step 5:

[1771] The server sends the text data to the generation AI for analysis and identifies the necessary actions.

[1772] The input is text data and emotional state data, and the output is data that specifies actions. Specifically, the prompt sentence "Create tomorrow's sales report and register it in the sales management system" is input to the generation AI (e.g., GPT-4), and the actions "Create daily report" and "Register in the sales management system" are specified.

[1773] Step 6:

[1774] The server integrates the emotion data and action data and makes business suggestions based on the user's emotions.

[1775] The input is emotion data and action data, and the output is a specific business proposal. For example, the server generates a specific business proposal such as "Suggest simplifying templates for users with high stress levels."

[1776] Step 7:

[1777] The server executes the task using robotic process automation (RPA) based on the identified actions.

[1778] The input is the specified action data, and the output is the result of the business execution. Specifically, sales data is entered into a daily report creation template using an RPA tool (e.g., UiPath), and the data is registered via the sales management system's API.

[1779] Step 8:

[1780] The server passes the results of the work execution to the generation AI, which then creates a result report.

[1781] The input is the data on the results of the work execution, and the output is a report of the results in natural language. Using a generative AI (e.g., GPT-4), a report such as "A daily report has been created and registered in the sales management system. Things are progressing without stress" is generated.

[1782] Step 9:

[1783] The device passes the generated text to a speech synthesis system to convert it into speech.

[1784] The input is natural language text and the output is audio data. Specifically, the report text is converted into audio using a speech synthesis API.

[1785] Step 10:

[1786] The terminal reports the audio data to the user.

[1787] The output is an audio report that tells the user, "Based on your request, a daily report has been created and registered in the sales management system. Don't worry, everything is going smoothly."

[1788] (Application example 2)

[1789] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1790] This invention relates to a personal AI secretary system specialized for security services, and aims to automate and streamline security-related tasks while taking into account the user's emotional state. Conventional systems perform tasks uniformly without considering the user's emotional state, which can cause anxiety and stress to the user. Furthermore, the reporting of execution results is mechanical, which does not fully satisfy the user's psychological satisfaction. There is a need to solve these problems.

[1791] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user request by voice or text, means for analyzing the received request using a voice recognition system or a text analysis system, and means including a generation AI for identifying a required action based on the analysis results. This enables a user to send a request by voice or text, and the system to automatically identify appropriate security-related tasks based on the content of the request and execute them taking into account the user's emotional state.

[1792] The system also includes a means for executing a task using a robotic process automation system for the identified action, a means for reporting the execution result to a user, an emotion engine that analyzes the user's emotional state and proposes a task based on the analyzed emotional state, and a means for executing the proposed task using the robotic process automation system. This enables security tasks to be executed and reported while taking the user's emotional state into consideration, thereby reducing the user's mental stress and increasing their satisfaction.

[1793] A "voice recognition system" refers to technology that converts voice information into text data.

[1794] A "text analysis system" refers to a technology that analyzes text data and performs processes such as semantic analysis and document classification.

[1795] "Generative AI" refers to artificial intelligence technology that analyzes user requests and generates appropriate actions and suggestions.

[1796] "Robotic process automation system" refers to a software robot that automatically executes a specified business process.

[1797] An "emotion engine" is a system that analyzes a user's emotional state from their voice and text and determines their state of stress, relaxation, etc.

[1798] A "speech synthesis system" refers to a technology that converts text data into speech to generate synthetic speech.

[1799] "Natural language text" refers to text data in the form of language used by people in everyday conversation and writing.

[1800] This invention provides a personal AI secretary system specialized for security services, and specifically realizes a system that automates and efficiently executes security-related tasks while taking into account the user's emotional state.

[1801] System configuration and operation

[1802] The system mainly consists of the following components:

[1803] 1. A device that receives user requests via voice or text

[1804] The terminal uses a speech recognition system (for example, Google Speech Recognition API) to convert the speech into text data.

[1805] 2. A server that analyzes the received request using a voice recognition system or text analysis system.

[1806] The server uses a generative AI (e.g., OpenAI's GPT-3) to analyze the received text data and identify the necessary actions.

[1807] 3. Emotion engine that analyzes emotional states

[1808] The analysis system determines emotions from the user's voice or text data, for example using a proprietary emotion engine to determine whether the user is stressed or relaxed.

[1809] 4. A server that executes tasks using a robotic process automation system for the identified actions.

[1810] Identified actions are then executed through robotic process automation (RPA) systems, for example to perform security-related tasks such as increasing security camera monitoring or activating alarm systems.

[1811] 5. Server that converts the execution results into natural language text using generation AI

[1812] The execution results are converted into natural language text by the generative AI and reported to the user.

[1813] 6. A device that converts the converted text into speech using a speech synthesis system

[1814] The text data sent from the server is converted into voice using a speech synthesis system (e.g., Pyttsx3) and reported to the user through the terminal.

[1815] Specific examples

[1816] Example 1: Request for increased security at home

[1817] User: "Tighten up the security at home tonight."

[1818] The device converts the speech into text and sends the text "Enhance home security tonight" to the server.

[1819] The server uses generative AI to analyze the text and identify the action to "strengthen security systems."

[1820] The device uses an emotion engine to analyze the user's emotional state and detect when the user is feeling stressed.

[1821] The server makes business suggestions based on emotions, such as suggesting strengthening surveillance camera monitoring or activating an alarm system.

[1822] The server instructs the RPA to execute the process of strengthening the security system.

[1823] The server passes the execution results to the generation AI, which generates a report saying, "Security has been strengthened. Everything is going well. Rest in peace tonight."

[1824] The terminal uses a speech synthesis system to convert the report into voice and notify the user.

[1825] Prompt Sentence Examples

[1826] "User Request: Increase security at home tonight. Please identify the necessary security tasks."

[1827] In this way, the present invention provides for the automatic execution and efficient processing of security tasks based on user requests and taking into account emotional state.

[1828] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1829] Step 1:

[1830] The user inputs a request by voice or text. The input voice data is a specific request such as "Please tighten the security at my house tonight." This data is input into the voice recognition system.

[1831] Step 2:

[1832] The device uses a voice recognition system (e.g., Google Speech Recognition API) to convert the voice data into text data. The voice data, "Please strengthen the security of my home tonight," is output as text data.

[1833] Step 3:

[1834] The server inputs text data into a generative AI (e.g., OpenAI GPT-3) for analysis. The server inputs the prompt "User request: Please strengthen the security of my home tonight. Please identify the necessary security tasks" into the generative AI model and obtains the analysis result. The identified action, "Strengthen the security system," is output as the analysis result.

[1835] Step 4:

[1836] The device uses an emotion engine to analyze the user's emotional state. Text data is input into the emotion engine, which determines whether the user is feeling stressed or relaxed. The analysis result is output as "stressed."

[1837] Step 5:

[1838] The server makes business proposals based on the analysis results and the emotional state. Using a generative AI model, the server generates proposals appropriate to the emotional state. For example, specific proposals such as "strengthen surveillance camera monitoring" or "activate the alarm system" are output.

[1839] Step 6:

[1840] The server uses a robotic process automation (RPA) system to execute the proposed security tasks. The server inputs instruction data such as "strengthen surveillance camera monitoring" or "activate the alarm system" into the RPA and obtains the execution results. The execution result is output as a message that the security system has been strengthened.

[1841] Step 7:

[1842] The server uses generative AI to convert the execution results into natural language text. The execution result data is input into the generative AI model, and a report is generated that reads, "Security has been strengthened. Everything is going well. Rest easy tonight."

[1843] Step 8:

[1844] The device uses a speech synthesis system (e.g., Pyttsx3) to convert the generated report into voice and notify the user. The generated report text is input into the speech synthesis system, output as voice data, and reported to the user.

[1845] 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.

[1846] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1847] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1848] 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.

[1849] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1850] 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.

[1851] 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).

[1852] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1853] 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."

[1854] 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.

[1855] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1856] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1857] 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.

[1858] 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.

[1859] 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.

[1860] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1861] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] The following is further disclosed regarding the above embodiment.

[1867] (Claim 1)

[1868] means for receiving a user request by voice or text;

[1869] means for analyzing the received request using a voice recognition system or a text analysis system;

[1870] a means including generative artificial intelligence for identifying necessary actions based on the analysis results;

[1871] a means for executing a task using a robotic process automation system in response to the identified action;

[1872] a means for reporting the execution results to the user;

[1873] A system including:

[1874] (Claim 2)

[1875] A means for making business proposals based on the results of analyzing user requests;

[1876] a means for executing the proposed work using a robotic process automation system;

[1877] 10. The system of claim 1, comprising:

[1878] (Claim 3)

[1879] means for receiving a user request and converting it into text using a speech recognition system;

[1880] A means of analyzing the converted text to identify required actions; and

[1881] 10. The system of claim 1, comprising:

[1882] "Example 1"

[1883] (Claim 1)

[1884] means for receiving a user request by voice or text;

[1885] means for analyzing the received request using a voice recognition system or a text analysis system;

[1886] a means including generative artificial intelligence for identifying necessary actions based on the analysis results;

[1887] a means for executing a task using a robotic process automation system in response to the identified action;

[1888] A means for converting the execution result into natural language text using a generation artificial intelligence, converting it into voice using a voice synthesis system, and reporting it to the user;

[1889] A system including:

[1890] (Claim 2)

[1891] A means for making business proposals based on the results of analyzing user requests;

[1892] a means for executing the proposed work using a robotic process automation system;

[1893] 10. The system of claim 1, comprising:

[1894] (Claim 3)

[1895] means for receiving a user request and converting it into text using a speech recognition system;

[1896] A means of analyzing the converted text to identify required actions; and

[1897] A means for analyzing the converted text using generative artificial intelligence to identify necessary actions;

[1898] 10. The system of claim 1, comprising:

[1899] "Application Example 1"

[1900] (Claim 1)

[1901] means for receiving a user request by voice or text;

[1902] means for analyzing the received request using a voice recognition system or a text analysis system;

[1903] a means including generative artificial intelligence for identifying necessary actions based on the analysis results;

[1904] a means for executing a task using a robotic process automation system in response to the identified action;

[1905] a means for reporting the execution results to the user;

[1906] means for receiving voice instructions in the industrial facility and automatically performing tasks on the industrial robot based on the instructions;

[1907] a means for automatically creating a maintenance schedule for industrial equipment and performing necessary maintenance work based on the schedule;

[1908] A means for automating inventory management within an industrial facility and automatically placing orders when inventory is low;

[1909] A system including:

[1910] (Claim 2)

[1911] A means for making business proposals based on the results of analyzing user requests;

[1912] a means for executing the proposed work using a robotic process automation system;

[1913] A means for proposing an optimal work process based on work instructions for an industrial robot;

[1914] A means for optimizing maintenance schedules for industrial equipment;

[1915] 10. The system of claim 1, comprising:

[1916] (Claim 3)

[1917] means for receiving a user request and converting it into text using a speech recognition system;

[1918] A means of analyzing the converted text to identify required actions; and

[1919] a means for monitoring the condition of the industrial equipment and identifying necessary actions based on the condition;

[1920] a means for analyzing inventory data at an industrial facility to identify inventory shortages;

[1921] 10. The system of claim 1, comprising:

[1922] "Example 2: Combining Emotion Engines"

[1923] (Claim 1)

[1924] means for receiving a user request by voice or text;

[1925] means for analyzing the received request using a voice recognition system or a text analysis system;

[1926] means including an emotion engine for analyzing the analysis results and the user's emotional state;

[1927] a means including generative artificial intelligence for identifying necessary actions based on the analysis results;

[1928] a means for executing a task using a robotic process automation system in response to the identified action;

[1929] a means for converting the execution result into a natural language text using a generative artificial intelligence and reporting it to a user using a speech synthesis system;

[1930] A system including:

[1931] (Claim 2)

[1932] A means for making business proposals based on the analysis results and emotion data;

[1933] a means for executing the proposed work using a robotic process automation system;

[1934] 10. The system of claim 1, comprising:

[1935] (Claim 3)

[1936] means for receiving a user request and converting it into text using a speech recognition system;

[1937] a means for analyzing the converted text using generative artificial intelligence to identify necessary actions;

[1938] 10. The system of claim 1, comprising:

[1939] "Application example 2 when combining emotion engines"

[1940] (Claim 1)

[1941] means for receiving a user request by voice or text;

[1942] means for analyzing the received request using a voice recognition system or a text analysis system;

[1943] a means including generative artificial intelligence for identifying necessary actions based on the analysis results;

[1944] a means for executing a task using a robotic process automation system in response to the identified action;

[1945] a means for reporting the execution results to the user;

[1946] a means for providing a business proposal based on the analyzed emotional state, the means including an emotional engine for analyzing the emotional state of the user;

[1947] a means for executing the proposed work using a robotic process automation system;

[1948] A system including:

[1949] (Claim 2)

[1950] The system of claim 1, further comprising: means for analyzing the emotional state, and then proposing a task based on the analysis results; means for executing the proposed task using a robotic process automation system; and means for converting the execution results into natural language text using a generative AI.

[1951] (Claim 3)

[1952] means for receiving a user request and converting it into text using a speech recognition system;

[1953] A means of analyzing the converted text to identify required actions; and

[1954] a means for making business suggestions based on the analyzed emotional state;

[1955] a means for executing the proposed work using a robotic process automation system;

[1956] A means for converting the execution results into natural language text using generative AI;

[1957] a means for converting the converted text into voice using a speech synthesis system and reporting the converted text to a user;

[1958] 10. The system of claim 1, comprising: [Explanation of symbols]

[1959] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a user request by voice or text; means for analyzing the received request using a voice recognition system or a text analysis system; a means including generative artificial intelligence for identifying necessary actions based on the analysis results; a means for executing a task using a robotic process automation system in response to the identified action; a means for reporting the execution results to the user; A system including:

2. A means for making business proposals based on the results of analyzing user requests; a means for executing the proposed work using a robotic process automation system; The system of claim 1 , comprising:

3. means for receiving a user request and converting it into text using a speech recognition system; A means of analyzing the converted text to identify required actions; and The system of claim 1 , comprising:

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