system

Abstract

We provide the system. [Solution] Voice input means and, A means of converting speech into text, A means of communication for transmitting character data, Search methods for searching text data, A generation means for generating answers based on search results, A receiving means for receiving the generated response, A display and audio output means for displaying and outputting the received response, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In optical fiber line construction for corporations, there is a current situation where on-site workers call the in-house communication support window to inquire about troubles and questions that occur. However, this method takes time and increases the load on the window, making it difficult to promptly respond to all inquiries. Also, when on-site workers cannot immediately obtain a solution, the work efficiency significantly decreases. Therefore, there is a need for a method that allows on-site workers to quickly solve problems and reduces the load on the support window.

Means for Solving the Problems

[0005] The present invention provides a system that allows field workers to input voice data into a mobile terminal, convert the voice into text, and send the text data to a server to automatically obtain an appropriate response. Specifically, the system solves the above problem by comprising a voice input means, a conversion means for converting voice into text, a communication means for transmitting text data, a search means for searching text data, a generation means for generating a response based on the search results, a receiving means for receiving the generated response, and a display / audio output means for displaying and outputting the received response. As a result, field workers can quickly obtain the necessary information and significantly reduce inquiries to support desks.

[0006] "Voice input means" refers to a device or method for receiving voice signals emitted by a user.

[0007] "A means of converting speech to text" refers to technologies and systems for converting received speech data into text data.

[0008] "Communication means for transmitting character data" refers to devices or methods for transmitting converted text data to other devices or servers.

[0009] A "search method for searching text data" refers to a technology or system that uses received text data to query databases and information sources to find relevant information.

[0010] "Generative means for generating answers based on search results" refers to technologies and systems for constructing answers that users are looking for, based on the information they have searched for.

[0011] "Receiving means for receiving generated responses" refers to a device or method for receiving response data sent from a server.

[0012] "Display and audio output means for displaying and audio outputting received responses" refers to a device for visually displaying received response data and a technology or system for playing it back with sound. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system that allows field workers to input voice data into a mobile device, convert the voice into text, and send that text data to a server to automatically obtain an appropriate response. The system operates as follows:

[0035] Receiving voice input

[0036] The user speaks a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device has a microphone that can receive voice commands.

[0037] Speech-to-text conversion

[0038] The device uses a speech recognition API to convert the received audio data into text data. This conversion generates text data such as, "Please tell me how to install a fiber optic cable."

[0039] Sending text data

[0040] The converted text data is sent from the terminal to the server. The communication method typically used is an HTTP request via the internet.

[0041] Searching for questions and generating answers

[0042] Based on the received text data, the server searches a pre-prepared FAQ database. For example, the database contains pre-registered answers to the question "How to install a fiber optic cable," and the server searches for and retrieves the relevant entry. Based on the search results, the server generates an answer such as, "The procedure for installing a fiber optic cable is: (1) confirm the installation location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0043] Submit your response

[0044] The generated response is sent from the server to the terminal. This is also done via communication means.

[0045] Display of answers and audio output

[0046] The terminal displays the received response to the user and also provides audio output. This allows on-site workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing the fiber optic cable is: (1) Confirm the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," while the same content is simultaneously played aloud.

[0047] Specific example

[0048] For example, suppose a worker is installing fiber optic cables at a new site. The worker says into the terminal, "Please tell me how to install fiber optic cables." The terminal receives the voice and converts it into text data using a speech recognition API. Then, it sends the converted text data to a server. The server searches its FAQ database for the relevant information, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer and simultaneously outputs it as audio, providing the worker with an immediate response and improving work efficiency.

[0049] Thus, by providing information quickly, the present invention can improve the work efficiency of on-site workers and significantly reduce inquiries to support desks.

[0050] The following describes the processing flow.

[0051] Step 1:

[0052] The user speaks a question into their mobile device. They input a question by voice, such as "Please tell me how to install a fiber optic cable."

[0053] Step 2:

[0054] The device receives audio using its microphone. The user's voice data is acquired using a voice input method.

[0055] Step 3:

[0056] The device uses a speech recognition API to convert received audio data into text data. For example, it uses the speech recognition API to convert the audio "Please tell me how to install a fiber optic cable" into text data.

[0057] Step 4:

[0058] The terminal sends the converted text data to the server. The text data is sent to the server using communication methods such as HTTP requests.

[0059] Step 5:

[0060] The server searches the FAQ database based on the text data it receives. It uses keywords and phrases contained in the text data to search the FAQ database for related questions and their answers.

[0061] Step 6:

[0062] The server generates appropriate answers based on the search results. For example, in response to the question "How to install a fiber optic cable," it might generate an answer such as, "The procedure for installing a fiber optic cable is: (1) Check the installation location of the equipment, (2) Connect the cables, and (3) Configure the network settings."

[0063] Step 7:

[0064] The server sends the generated response to the terminal. The generated response data is sent to the terminal via a communication method.

[0065] Step 8:

[0066] The device displays the received response to the user. By displaying the response on the screen, it provides the user with information visually.

[0067] Step 9:

[0068] The device simultaneously outputs the received responses as audio. Using speech synthesis technology, the displayed responses are played back as audio, providing the user with information aurally as well.

[0069] This series of steps allows on-site workers, who are the users, to quickly obtain the necessary information, improve work efficiency, and significantly reduce inquiries to support desks.

[0070] (Example 1)

[0071] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0072] Traditional methods for obtaining necessary information quickly on-site by field workers were inefficient due to the time-consuming nature of information retrieval. Furthermore, frequent inquiries to support desks increased operational costs. There was also a need for accuracy and speed in voice-to-text conversion and subsequent information retrieval and response provision.

[0073] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0074] In this invention, the server includes voice input means, conversion means for converting speech to text, communication means for transmitting text data, search means for searching text data, generation means for generating answers based on search results, receiving means for receiving the generated answers, and display / speech output means for displaying and outputting the received answers. This enables field workers to quickly obtain necessary information through voice input, improving work efficiency and significantly reducing inquiries to support desks.

[0075] "Voice input means" refers to devices or interfaces that users use to input questions or commands verbally.

[0076] "Conversion means" refers to the technology or algorithm used to convert received audio data into text data.

[0077] "Communication methods" refer to internet connections and protocols used to transmit text data and other information to remote servers.

[0078] "Search methods" refer to technologies and algorithms that retrieve relevant information or answers from a database based on received text data.

[0079] "Generation means" refers to the technology or section that constructs and generates appropriate answers based on search results.

[0080] "Receiving means" refers to the technology or interface used to receive data or responses sent from a server on a terminal.

[0081] "Display and audio output means" refers to displays and speech synthesis technologies that visually display received responses to the user and also output them as audio.

[0082] "Speech recognition API" refers to an application programming interface for converting speech data into text data.

[0083] An "FAQ database" refers to a collection of information that stores frequently asked questions (FAQs) and their answers.

[0084] A "speech synthesis API" refers to an application programming interface for converting text data into speech data.

[0085] An "HTTP request" refers to a form of communication protocol in which a web browser or other client requests a specific action from a web server.

[0086] This invention relates to a system in which a field worker inputs voice data into a mobile device, which is then converted into text and sent to a server to automatically obtain an appropriate response. The system operates as follows:

[0087] Hardware and software to be used

[0088] The device used is a mobile device with a built-in microphone (e.g., smartphone, tablet). This device converts speech to text using a speech recognition API. Specifically, the Google® Cloud Speech-to-Text API is used. The device also uses the Google Cloud Text-to-Speech API to convert text to speech and provide information to the user in audio format.

[0089] The server uses either cloud-based or on-premises servers. This server searches an FAQ database based on the received text data and generates answers based on the search results. An optimized database search algorithm is used as the search method.

[0090] System operation

[0091] The user voice-inputs a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device uses its built-in microphone to capture this voice. Then, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text. Specifically, this voice data is converted into the text "Please tell me how to install a fiber optic cable."

[0092] The converted text data is sent from the terminal to the server. HTTP requests over the internet are the commonly used means of communication. The server searches a pre-prepared FAQ database based on the received text data. For example, the database has answers registered in advance to the question "How to install a fiber optic cable?". The server searches for the relevant entry and generates an answer such as, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0093] The generated response is sent from the server to the terminal. The terminal displays the received response to the user and also outputs it as audio using the Google Cloud Text-to-Speech API. This allows field workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing a fiber optic cable is: (1) Confirm the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," and the same content is played aloud through the speaker.

[0094] Specific example

[0095] For example, suppose a worker is installing fiber optic cables at a new site. The worker speaks into a terminal and says, "Please tell me how to install fiber optic cables." The terminal receives the audio and converts it into text data using the Google Cloud Speech-to-Text API. The converted text data is then sent to a server. The server searches its FAQ database for the relevant information, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer and also outputs it as audio using the Google Cloud Text-to-Speech API, providing the worker with an immediate response. This increases work efficiency.

[0096] This system can improve the work efficiency of on-site workers and significantly reduce inquiries to support desks. Furthermore, by utilizing speech recognition and speech synthesis technologies, the user experience can be enhanced.

[0097] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0098] Step 1:

[0099] The user inputs their questions by voice into their mobile device.

[0100] Specific action: The user speaks into their mobile device saying, "Please tell me how to install the fiber optic cable."

[0101] Input: User's voice data.

[0102] Output: The device receives the audio data.

[0103] Step 2:

[0104] The device uses its built-in microphone to capture audio data.

[0105] Specific operation: The device uses the microphone to collect the user's voice as digital audio data.

[0106] Input: User's voice data.

[0107] Output: Captured digital audio data.

[0108] Step 3:

[0109] The device uses a speech recognition API (e.g., Google Cloud Speech-to-Text API) to convert speech data into text data.

[0110] Specific operation: The device sends the captured audio data to a speech recognition API, which then converts the audio into text.

[0111] Input: Captured digital audio data.

[0112] Output: Text data saying "Please tell me how to install a fiber optic cable."

[0113] Step 4:

[0114] The terminal sends the converted text data to the server. HTTP requests via the internet are used as the means of communication.

[0115] Specific operation: The terminal includes the converted text data in the HTTP request and sends it to the server.

[0116] Input: Text data "Please tell me how to install a fiber optic cable."

[0117] Output: The server receives text data.

[0118] Step 5:

[0119] The server searches the FAQ database based on the received text data.

[0120] Specific operation: The server executes the search query "How to install a fiber optic cable" against the FAQ database and searches for relevant information.

[0121] Input: Text data "Please tell me how to install a fiber optic cable."

[0122] Output: Search results from the FAQ database.

[0123] Step 6:

[0124] The server generates an answer based on the search results.

[0125] Specific operation: The server analyzes the search results and constructs an appropriate response. For example, it might say, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0126] Input: Search results from the FAQ database.

[0127] Output: The generated response.

[0128] Step 7:

[0129] The server sends the generated response to the terminal. Communication takes place via the internet.

[0130] Specific operation: The server sends the response message it has prepared to the terminal as an HTTP response.

[0131] Input: The generated response.

[0132] Output: The terminal receives the response message.

[0133] Step 8:

[0134] The device displays the received response to the user and also provides audio output.

[0135] Specific operation: The terminal displays the received response on the screen, converts it to speech using the Google Cloud Text-to-Speech API, and plays it through the speaker. For example, the display might show "The procedure for installing a fiber optic line is: (1) Check the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," and the same content would be played aloud.

[0136] Input: Received response message.

[0137] Output: The displayed response and the information played back as audio.

[0138] (Application Example 1)

[0139] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0140] In field work, prompt and appropriate information provision is necessary to immediately address problems and questions faced by workers. However, conventional systems require workers to manually search for information, which reduces efficiency. Furthermore, the technology for receiving questions via voice is limited, and there is no provision of answers using speech synthesis. As a result, there is a lack of systems that can significantly improve work efficiency, which is a major challenge.

[0141] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0142] In this invention, the server includes voice input means, conversion means for converting speech to text, communication means for transmitting text data, search means for searching text data, generation means for generating answers based on search results, receiving means for receiving the generated answers, display and audio output means for displaying and outputting the received answers, voice recognition means for receiving questions by voice, and voice synthesis means for synthesizing the generated answers into speech. This makes it possible for workers to input questions by voice and immediately receive appropriate answers in both voice and text.

[0143] "Voice input means" refers to devices or software for receiving voice data.

[0144] "Conversion methods for converting speech to text" refers to technologies and tools for converting speech data into text data.

[0145] "Communication methods for transmitting text data" refers to internet communication and other communication protocols used to send converted text data to a server.

[0146] "A search method for retrieving text data" refers to a technique or system for retrieving appropriate information from a database based on text data received on a server.

[0147] "Generative means for generating answers based on search results" refers to algorithms or systems for generating appropriate answers based on searched information.

[0148] "Receiving means for receiving generated responses" refers to equipment or software used to receive response data sent from a server.

[0149] "Display and audio output means for displaying and audio outputting received responses" refers to devices and software, including displays and speakers, for displaying received responses to the user and outputting them as audio.

[0150] "Voice recognition means for receiving questions via voice" refers to voice recognition APIs and other voice recognition technologies that recognize user voice input and convert it into text data.

[0151] "Speech synthesis means for synthesizing generated responses into speech" refers to speech synthesis technology or tools that convert text data into speech data and provide the user with the response in voice.

[0152] The present invention is a system for improving the efficiency of workers in a factory. This system includes a voice input means, a conversion means for converting voice to text, a communication means for transmitting text data, a search means for searching text data, a generation means for generating answers based on the search results, a receiving means for receiving the generated answers, a display and audio output means for displaying and outputting the received answers, a voice recognition means for receiving questions by voice, and a voice synthesis means for synthesizing the generated answers into speech.

[0153] First, the user voice-inputs their question into their smartphone. The smartphone uses its microphone to capture the voice data. Next, the smartphone uses its built-in speech recognition API to convert the voice data into text data. For example, if the user asks, "How do I fix error code 1234?", this voice will be converted into the text data "How do I fix error code 1234?".

[0154] The converted text data is sent to the server using a communication method. The server receives this text data and searches the FAQ database using a search method. Based on the search results, the server generates an appropriate answer. For example, it might generate an answer such as, "To fix error code 1234, first turn off the power and then restart. After that, reset the sensor."

[0155] The generated response is sent from the server to the smartphone. The smartphone displays the received response and also plays it back using speech synthesis. For example, the message "To fix error code 1234, first turn off the power and then restart. After that, reset the sensor" is displayed and played back.

[0156] This system includes speech recognition functionality using the Python `speech_recognition` library, communication functionality using the `requests` library, and speech synthesis functionality using gTTS (Google Text-to-Speech). This allows users to quickly ask questions and receive appropriate answers immediately.

[0157] Example of a prompt:

[0158] "How do I fix error code 1234?"

[0159] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0160] Step 1:

[0161] The user voice-inputs their question into their smartphone. The device's microphone captures the audio data. This input data is in audio format and is used for subsequent processing.

[0162] Step 2:

[0163] The audio data is sent to the speech recognition system within the device, and the speech_recognition library is used to convert the audio data into text data. The data processing performed here involves recognizing phonemes based on spectral analysis of the audio waveform data and converting them into strings. The output is text data.

[0164] Step 3:

[0165] The converted text data is sent to the server via the internet using a communication method. An HTTP request is used for this transmission, and the character data is passed to the server as input.

[0166] Step 4:

[0167] The server queries the FAQ database using a search mechanism based on the received text data to find the relevant answer. Data processing involves searching for the appropriate entry from the database index and extracting its content. The output is the retrieved answer data.

[0168] Step 5:

[0169] The server uses a generation mechanism to generate answers based on the search results. This process formats the acquired database information according to a preset format. The generated answer data is output and sent back to the terminal via the communication mechanism.

[0170] Step 6:

[0171] The terminal receives the received response data via a receiving means and presents the response to the user using display and audio output means. Display is performed on a screen, and audio output is synthesized using the gTTS library. Specifically, text data is converted into an audio file, which is then played back through the speaker. The output consists of a visual text display and audio playback.

[0172] With all processing steps now complete, users can input questions by voice and receive instant answers in both text and voice.

[0173] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0174] This invention provides a system that allows field workers to input voice data into a mobile device, convert the voice into text, and send that text data to a server to automatically obtain an appropriate response. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide responses and adjust search priorities based on the user's emotions.

[0175] Voice input and emotion recognition

[0176] The user speaks a question into their mobile device, for example, "Please tell me how to install a fiber optic cable." Simultaneously, the user's emotions are also recognized. The device has a microphone to receive voice and uses an emotion engine to recognize the user's emotions from their voice.

[0177] Speech-to-text conversion

[0178] The device uses a speech recognition API to convert the received audio data into text data. This conversion generates text data such as, "Please tell me how to install a fiber optic cable."

[0179] Emotion-based adjustment

[0180] The device uses emotion data recognized by the emotion engine to adjust search and response generation priorities. For example, if the user is feeling anxious or angry, the server prioritizes processing to provide a response quickly.

[0181] Sending text data

[0182] The converted text data and sentiment data are sent from the terminal to the server. HTTP requests via the internet are generally used as the means of communication.

[0183] Searching for questions and generating answers

[0184] Based on the received text data, the server searches a pre-prepared FAQ database. Based on the text data and sentiment data, the server generates the most appropriate answer. For example, the database has pre-registered answers to the question "How to install a fiber optic cable?", and when generating an answer such as "The procedure for installing a fiber optic cable is (1) to check the installation location of the equipment, (2) to connect the cables, and (3) to configure the network settings," the server adjusts the priority and tone of the answer based on sentiment data.

[0185] Submit your response

[0186] The generated response is sent from the server to the terminal. This is also done via communication means.

[0187] Display of answers and audio output

[0188] The terminal displays the received response to the user and also provides audio output. The display method and voice tone are adjusted based on sentiment data. This allows on-site workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing a fiber optic cable is (1) confirm the equipment installation location, (2) connect the cables, and (3) configure the network settings," while simultaneously playing an audio message saying, "Stay calm and review the next steps. First, confirm the equipment installation location."

[0189] Specific example

[0190] Imagine a worker installing fiber optic cables at a new site. The worker speaks into a terminal and says, "Please tell me how to install fiber optic cables." The terminal receives the audio and uses an emotion engine to simultaneously recognize the worker's urgency and emotional state. It then uses a speech recognition API to convert the audio into text data and sends the converted text data and emotion data to a server. The server searches its FAQ database for relevant information, generates an appropriate answer based on the emotion data, and sends it to the terminal. The terminal displays the answer and simultaneously communicates it to the worker via audio output.

[0191] Thus, by combining emotion recognition, the present invention makes it possible to provide appropriate information tailored to the situation of on-site workers, improve work efficiency, and significantly reduce inquiries to support desks.

[0192] The following describes the processing flow.

[0193] Step 1:

[0194] The user speaks a question into their mobile device. They input a question by voice, such as "Please tell me how to install a fiber optic cable."

[0195] Step 2:

[0196] The device receives audio using its microphone. The user's voice data is acquired using a voice input method.

[0197] Step 3:

[0198] The device uses an emotion engine to recognize the user's emotions from the received audio data. For example, it can determine whether the user is anxious or anxious based on the tone and speed of their voice.

[0199] Step 4:

[0200] The device uses a speech recognition API to convert received audio data into text data. For example, it converts the audio "Please tell me how to install a fiber optic cable" into text data.

[0201] Step 5:

[0202] The terminal sends the converted text data and recognized emotion data to the server. This data is transmitted using communication methods such as HTTP requests.

[0203] Step 6:

[0204] The server searches the FAQ database based on the text data it receives. It finds the most relevant FAQ entries based on keywords and phrases.

[0205] Step 7:

[0206] The server generates appropriate answers based on the search results. For example, it provides specific steps such as, "The procedure for installing a fiber optic line is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0207] Step 8:

[0208] The server adjusts the tone and priority of the responses it provides based on sentiment data. For example, if the user is anxious, it might add reassuring phrases such as, "Please calm down and review the next steps."

[0209] Step 9:

[0210] The server sends the generated response to the terminal. The generated response data is sent to the terminal via a communication method.

[0211] Step 10:

[0212] The device displays the received response to the user. By displaying the response on the screen, it provides the user with information visually.

[0213] Step 11:

[0214] The device outputs the received response as audio. Using speech synthesis technology, the displayed response is played back as audio, providing the user with information aurally as well.

[0215] This series of steps allows field workers to quickly obtain the necessary information, improve work efficiency, and significantly reduce inquiries to support desks. Furthermore, incorporating emotion recognition enables the provision of more personalized support.

[0216] (Example 2)

[0217] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0218] There is a need for a means of providing information to enable on-site workers to respond quickly and accurately to problems they face, but conventional systems make it difficult to obtain appropriate answers that take into account the user's feelings. In particular, in emergencies or when users are feeling anxious or angry, there is a need for quick and appropriate responses that take the user's feelings into consideration.

[0219] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a voice input means, a conversion means for converting voice to text, an emotion recognition means for recognizing the user's emotions, a communication means for transmitting text data and emotion data, a search means for searching for questions based on the text data and emotion data, a generation means for generating answers based on search results and emotion data using a generation AI model, a receiving means for receiving the generated answers, and a display / audio output means for displaying and outputting the received answers. This enables field workers to provide information quickly and accurately in response to the user's emotions.

[0220] "Voice input means" refers to a device or function for acquiring voice data, such as a microphone.

[0221] "A means of converting speech to text" refers to a technology that converts speech data into text data, and speech recognition APIs are an example of this.

[0222] "Means of recognizing user emotions" refers to technologies that detect emotional states from a user's voice or text, and an emotion analysis engine is an example of this.

[0223] "Communication means for transmitting text data and sentiment data" refers to technologies that transmit converted text data and recognized sentiment data to a remote server, such as HTTP requests using the internet.

[0224] A "search method for searching for questions based on text data and sentiment data" is a technology that extracts relevant information from a database based on received data, and a search using a query database is an example of this.

[0225] "Generative means for generating answers based on search results and sentiment data using a generative AI model" refers to a technology that uses AI technology to create answers that take search results and sentiment data into consideration, and a generative AI model is an example of this.

[0226] "Means of receiving generated responses" refers to technologies for receiving generated response data from a remote server, such as receiving data via an HTTP request.

[0227] "A display and audio output means for displaying and outputting received responses" refers to a technology that displays received responses on a screen and plays them back as audio, such as a display and speakers.

[0228] This invention relates to a system in which a field worker inputs voice data into a mobile device, converts the voice into text data, sends that text data and the user's emotion data to a server, and automatically obtains an appropriate response. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it can provide responses and adjust search priorities based on the user's emotions.

[0229] The user voice-inputs a question into their mobile device. This question may include specific details such as, "Please tell me how to install a fiber optic cable." Simultaneously, the mobile device recognizes the user's emotions through their voice. The voice data is received using a microphone.

[0230] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. This conversion generates the text message, "Please tell me how to install the fiber optic cable." Simultaneously, the device uses an emotion engine (e.g., IBM Watson® Tone Analyzer) to recognize the user's emotional state. Recognized emotions include impatience, anger, excitement, and calmness.

[0231] The converted text data and sentiment data are sent from the terminal to the server. An HTTP POST request over the internet is used for transmission. The server receives this data and first searches its FAQ database (e.g., query data stored in MySQL®). In doing so, it uses a generative AI model (e.g., OpenAI®'s GPT-3®) to generate an appropriate response based on the text and sentiment data. The server generates a prompt and inputs it to the AI ​​model as follows:

[0232] Prompt: "The user is asking, 'Please tell me how to install the fiber optic cable,' and is showing signs of anxiety. As a response, explain the fiber optic cable installation procedure in a reassuring tone."

[0233] The AI ​​model generates an appropriate response based on this prompt. For example, it might generate a response that includes specific steps, such as, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings." The tone and content are adjusted to take the user's emotions into consideration.

[0234] The generated response is sent back to the terminal from the server. The terminal displays the received response on its screen and also outputs it as audio using the Google Text-to-Speech API. For example, the display might show "The procedure for installing a fiber optic line is (1) to check the location of the equipment, (2) to connect the cables, and (3) to configure the network settings," and at the same time, it might play an audio message saying, "Stay calm and check the next steps. First, check the location of the equipment."

[0235] Thus, by combining speech recognition technology, a generative AI model, and emotion recognition technology, the present invention enables the rapid and appropriate provision of information tailored to the situation of on-site workers, improving work efficiency and significantly reducing inquiries to support desks.

[0236] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0237] Step 1:

[0238] The user voice-inputs a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device receives this voice using its microphone. The received voice data is the input and is processed in the next step.

[0239] Step 2:

[0240] The device uses an emotion recognition engine to recognize the user's emotions from the voice data. The emotion recognition engine (e.g., IBM Watson Tone Analyzer) receives the voice data and outputs emotion data. Specifically, it determines whether the user is exhibiting an emotional tone (such as impatience, anger, excitement, or calmness).

[0241] Step 3:

[0242] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. In this process, audio data is input, and the text data "Please tell me how to install a fiber optic cable" is output.

[0243] Step 4:

[0244] The device collects the converted text data and recognized sentiment data and sends them to the server. HTTP POST requests over the internet are used for transmission. The input consists of text data and sentiment data, which are encoded in HTTP request format and sent to the server.

[0245] Step 5:

[0246] The server receives the HTTP request and extracts the sent text and sentiment data. This receiving operation prepares the server for the next processing. At this point, the input is the text and sentiment data, and the output is the data after parsing is complete.

[0247] Step 6:

[0248] The server searches the FAQ database. Specifically, it sends a search query to the database based on the parsed text data and retrieves the appropriate information. The input to this search operation is a text query, and the output is the search result from the FAQ database.

[0249] Step 7:

[0250] The server activates a generative AI model and generates the best possible response based on search results and sentiment data. For example, the server might input the following prompt into the generative AI model:

[0251] "The user is asking, 'Please tell me how to install the fiber optic cable,' and is showing signs of impatience. As a solution, please explain the fiber optic cable installation procedure in a reassuring tone."

[0252] The output of the generative AI model generates response text that takes the user's emotions into consideration.

[0253] Step 8:

[0254] The server sends the generated response text to the terminal. In this operation, the response text becomes the input data, which is then encoded in HTTP response format and sent to the terminal.

[0255] Step 9:

[0256] The device analyzes the received response text, displays it on the screen, and also outputs it as audio. Specifically, it uses the Google Text-to-Speech API to convert the text data into audio data and plays it back to the user. The input for this operation is the response text, and the output is display on the screen and audio playback.

[0257] This allows users to quickly and accurately obtain the information they need. For example, the display might show "The procedure for installing a fiber optic line is (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings," while simultaneously playing a voice message saying, "Stay calm and review the next steps. First, confirm the location of the equipment."

[0258] (Application Example 2)

[0259] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0260] To enable on-site workers to respond quickly and appropriately to machinery and equipment problems they encounter in industrial environments, it is necessary to obtain accurate information immediately. However, general search systems do not support voice input and have the problem of not being able to recognize the emotional state of workers, such as anxiety or stress, and prioritize responses accordingly. In addition, work sites are noisy, so there is a need for methods to obtain information without using one's hands.

[0261] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0262] In this invention, the server includes voice input means, conversion means for converting speech to text, emotion recognition means, and adjustment means for adjusting the priority and tone of responses based on emotion data. This makes it possible for field workers to input questions by voice, have their emotional state recognized, and then quickly receive the most appropriate response, thereby significantly improving work efficiency.

[0263] "Voice input means" refers to a device that receives the voice spoken by a user as digital data.

[0264] "Conversion means" refers to devices or software that convert received audio data into text data.

[0265] "Communication means" refers to network communication modules and protocols used to send text data to a server.

[0266] "Search method" refers to a device or software that searches for information based on text data received on the server side.

[0267] "Generation means" refers to devices or software that create appropriate answers based on retrieved information.

[0268] "Receiving means" refers to the device or software that receives the response sent from the server.

[0269] "Display and audio output means" refers to devices or software that present received responses to the user visually and audibly.

[0270] "Emotion recognition means" refers to devices or software that analyze voice data and other input data to identify the user's emotional state.

[0271] "Adjustment means" refers to devices or software that change the priority or tone of responses based on recognized emotional data.

[0272] This invention is a system that converts voice input from on-site workers or users into text and provides appropriate responses. Specific embodiments for carrying out this invention are described below.

[0273] System Configuration

[0274] Voice input method

[0275] The user performs voice input using the microphone built into the smart glasses. By means of this voice input, it is possible for the operator to voice-input questions regarding troubles with machines or equipment.

[0276] Conversion means

[0277] The received voice data is converted into text data using the speech_recognition library. By using the voice recognition API, high-precision voice recognition is achieved.

[0278] Communication means

[0279] The converted text data and emotion data are transmitted to the server through the network communication module (for example, Wi-Fi or 4G communication) of the smart glasses. This communication is performed using an HTTP request.

[0280] Emotion recognition means

[0281] In order to analyze the emotion from the voice data, the emotion_recognition module is used. Thereby, the emotional state can be identified from the tone and speed of the user's voice, and the stress and urgency can be evaluated.

[0282] Search means

[0283] On the server side, the FAQ database is searched based on the received text data. Thereby, the information required by the user can be quickly extracted. By means of this search means, an appropriate countermeasure against troubles with machines or equipment is provided.

[0284] Adjustment means

[0285] Based on the emotion data, the priority and tone of the generated answer are adjusted. By means of this adjustment, when the user is in a high-stress state, an answer is provided in a tone that prompts a quick and calm response.

[0286] Generation means, reception means, display / audio output means

[0287] The answer generated by the server is transmitted back to the smart glass through the communication means. The answer is displayed on the display of the smart glass and the user is guided by audio output.

[0288] Specific example

[0289] When a field worker in a certain factory determines that a machine is malfunctioning, the worker asks the smart glass, "What is the cause of this machine not working?" The voice data is immediately converted into character data, and the emotion recognition engine determines from his voice that it is an emergency. This data is transmitted to the server, and the corresponding answer is searched from the FAQ database. The server generates an answer such as "It's okay. First, check if the machine is powered on, and then check if the cables are properly connected," and adjusts the tone based on the emotion data. As a result, it is displayed on the smart glass and guided by voice.

[0290] Prompt sentence examples

[0291] Please create pseudo-code for a system that uses the question obtained through voice recognition and the recognized emotion data to provide an appropriate answer to improve work efficiency. Voice input, emotion recognition, server communication, and answer display are performed using a smart glass. Emotion recognition is performed from voice data to determine the stress level and urgency. The result is displayed on the display of the smart glass and also guided by voice. Please describe in detail in pseudo-code format.

[0292] The flow of the specific process in Application Example 2 will be described using FIG. 14.

[0293] Step 1:

[0294] The user performs voice input

[0295] The user inputs a question verbally towards the smart glasses. For example, the user says, "What is the reason why this machine doesn't move?" This voice data becomes digital voice data through the microphone.

[0296] Input: User's voice

[0297] Output: Digital voice data

[0298] Step 2:

[0299] The terminal converts the voice into text.

[0300] The terminal (smart glasses) converts the digital voice data into text data using the speech_recognition library. In this process, the voice recognition API extracts characters from the voice waveform and converts them into text format.

[0301] Input: Digital voice data

[0302] Output: Text data

[0303] Step 3:

[0304] The terminal recognizes the emotion.

[0305] The terminal uses the emotion_recognition module to identify the user's emotion from the voice data. For example, it evaluates stress or urgency from the tone of the user's voice and the speaking speed.

[0306] Input: Digital voice data

[0307] Output: Emotion data

[0308] Step 4:

[0309] The terminal sends the text data and emotion data to the server.

[0310] The device sends the converted text data and sentiment data to the server via a communication module (e.g., Wi-Fi, 4G). This transmission is typically done using HTTP requests.

[0311] Input: Text data, sentiment data

[0312] Output: HTTP request to the server

[0313] Step 5:

[0314] The server performs a search based on text data.

[0315] The server searches the FAQ database based on the received text data. In this process, it quickly extracts information related to the entered question.

[0316] Input: Text data

[0317] Output: Search results data

[0318] Step 6:

[0319] The server generates and adjusts the response.

[0320] The server adjusts the priority and tone of generated responses based on emotional data. For example, if the user is feeling anxious, it will immediately provide a response in a quick and calm tone.

[0321] Input: Search results data, sentiment data

[0322] Output: Adjusted response data

[0323] Step 7:

[0324] The server sends the adjusted response data to the terminal.

[0325] The server sends the adjusted response data to the terminal. This transmission is again performed using an HTTP request.

[0326] Input: Adjusted response data

[0327] Output: HTTP response to the terminal

[0328] Step 8:

[0329] The device displays and outputs the answer aloud.

[0330] The device displays the received response on its screen and also provides audio output. This allows users to quickly obtain the information they need, both visually and aurally. Additional voice input is also possible if the user requires further guidance or assistance.

[0331] Input: Adjusted response data

[0332] Output: Display, audio playback

[0333] Through the steps described above, the present invention significantly improves the work efficiency of on-site workers and enables quick and appropriate responses.

[0334] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0335] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0336] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0337] [Second Embodiment]

[0338] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0339] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0340] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0341] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0342] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0343] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0344] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0345] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0346] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0347] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0348] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0349] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0350] This invention provides a system that allows field workers to input voice data into a mobile device, convert the voice into text, and send that text data to a server to automatically obtain an appropriate response. The system operates as follows:

[0351] Receiving voice input

[0352] The user speaks a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device has a microphone that can receive voice commands.

[0353] Speech-to-text conversion

[0354] The device uses a speech recognition API to convert the received audio data into text data. This conversion generates text data such as, "Please tell me how to install a fiber optic cable."

[0355] Sending text data

[0356] The converted text data is sent from the terminal to the server. The communication method typically used is an HTTP request via the internet.

[0357] Searching for questions and generating answers

[0358] Based on the received text data, the server searches a pre-prepared FAQ database. For example, the database contains pre-registered answers to the question "How to install a fiber optic cable," and the server searches for and retrieves the relevant entry. Based on the search results, the server generates an answer such as, "The procedure for installing a fiber optic cable is: (1) confirm the installation location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0359] Submit your response

[0360] The generated response is sent from the server to the terminal. This is also done via communication means.

[0361] Display of answers and audio output

[0362] The terminal displays the received response to the user and also provides audio output. This allows on-site workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing the fiber optic cable is: (1) Confirm the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," while the same content is simultaneously played aloud.

[0363] Specific example

[0364] For example, suppose a worker is installing fiber optic cables at a new site. The worker says into the terminal, "Please tell me how to install fiber optic cables." The terminal receives the voice and converts it into text data using a speech recognition API. Then, it sends the converted text data to a server. The server searches its FAQ database for the relevant information, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer and simultaneously outputs it as audio, providing the worker with an immediate response and improving work efficiency.

[0365] Thus, by providing information quickly, the present invention can improve the work efficiency of on-site workers and significantly reduce inquiries to support desks.

[0366] The following describes the processing flow.

[0367] Step 1:

[0368] The user speaks a question into their mobile device. They input a question by voice, such as "Please tell me how to install a fiber optic cable."

[0369] Step 2:

[0370] The device receives audio using its microphone. The user's voice data is acquired using a voice input method.

[0371] Step 3:

[0372] The device uses a speech recognition API to convert received audio data into text data. For example, it uses the speech recognition API to convert the audio "Please tell me how to install a fiber optic cable" into text data.

[0373] Step 4:

[0374] The terminal sends the converted text data to the server. The text data is sent to the server using communication methods such as HTTP requests.

[0375] Step 5:

[0376] The server searches the FAQ database based on the text data it receives. It uses keywords and phrases contained in the text data to search the FAQ database for related questions and their answers.

[0377] Step 6:

[0378] The server generates appropriate answers based on the search results. For example, in response to the question "How to install a fiber optic cable," it might generate an answer such as, "The procedure for installing a fiber optic cable is: (1) Check the installation location of the equipment, (2) Connect the cables, and (3) Configure the network settings."

[0379] Step 7:

[0380] The server sends the generated response to the terminal. The generated response data is sent to the terminal via a communication method.

[0381] Step 8:

[0382] The device displays the received response to the user. By displaying the response on the screen, it provides the user with information visually.

[0383] Step 9:

[0384] The device simultaneously outputs the received responses as audio. Using speech synthesis technology, the displayed responses are played back as audio, providing the user with information aurally as well.

[0385] This series of steps allows on-site workers, who are the users, to quickly obtain the necessary information, improve work efficiency, and significantly reduce inquiries to support desks.

[0386] (Example 1)

[0387] Next, we will describe Example 1. 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."

[0388] Traditional methods for obtaining necessary information quickly on-site by field workers were inefficient due to the time-consuming nature of information retrieval. Furthermore, frequent inquiries to support desks increased operational costs. There was also a need for accuracy and speed in voice-to-text conversion and subsequent information retrieval and response provision.

[0389] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0390] In this invention, the server includes voice input means, conversion means for converting speech to text, communication means for transmitting text data, search means for searching text data, generation means for generating answers based on search results, receiving means for receiving the generated answers, and display / speech output means for displaying and outputting the received answers. This enables field workers to quickly obtain necessary information through voice input, improving work efficiency and significantly reducing inquiries to support desks.

[0391] "Voice input means" refers to devices or interfaces that users use to input questions or commands verbally.

[0392] "Conversion means" refers to the technology or algorithm used to convert received audio data into text data.

[0393] "Communication methods" refer to internet connections and protocols used to transmit text data and other information to remote servers.

[0394] "Search methods" refer to technologies and algorithms that retrieve relevant information or answers from a database based on received text data.

[0395] "Generation means" refers to the technology or section that constructs and generates appropriate answers based on search results.

[0396] "Receiving means" refers to the technology or interface used to receive data or responses sent from a server on a terminal.

[0397] "Display and audio output means" refers to displays and speech synthesis technologies that visually display received responses to the user and also output them as audio.

[0398] "Speech recognition API" refers to an application programming interface for converting speech data into text data.

[0399] An "FAQ database" refers to a collection of information that stores frequently asked questions (FAQs) and their answers.

[0400] A "speech synthesis API" refers to an application programming interface for converting text data into speech data.

[0401] An "HTTP request" refers to a form of communication protocol in which a web browser or other client requests a specific action from a web server.

[0402] This invention relates to a system in which a field worker inputs voice data into a mobile device, which is then converted into text and sent to a server to automatically obtain an appropriate response. The system operates as follows:

[0403] Hardware and software to be used

[0404] The device used is a mobile device with a built-in microphone (e.g., smartphone, tablet). This device uses a speech recognition API to convert speech to text. Specifically, the Google Cloud Speech-to-Text API is used. The device also uses the Google Cloud Text-to-Speech API to convert text to speech and provide information to the user in audio format.

[0405] The server uses either cloud-based or on-premises servers. This server searches an FAQ database based on the received text data and generates answers based on the search results. An optimized database search algorithm is used as the search method.

[0406] System operation

[0407] The user voice-inputs a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device uses its built-in microphone to capture this voice. Then, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text. Specifically, this voice data is converted into the text "Please tell me how to install a fiber optic cable."

[0408] The converted text data is sent from the terminal to the server. HTTP requests over the internet are the commonly used means of communication. The server searches a pre-prepared FAQ database based on the received text data. For example, the database has answers registered in advance to the question "How to install a fiber optic cable?". The server searches for the relevant entry and generates an answer such as, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0409] The generated response is sent from the server to the terminal. The terminal displays the received response to the user and also outputs it as audio using the Google Cloud Text-to-Speech API. This allows field workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing a fiber optic cable is: (1) Confirm the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," and the same content is played aloud through the speaker.

[0410] Specific example

[0411] For example, suppose a worker is installing fiber optic cables at a new site. The worker speaks into a terminal and says, "Please tell me how to install fiber optic cables." The terminal receives the audio and converts it into text data using the Google Cloud Speech-to-Text API. The converted text data is then sent to a server. The server searches its FAQ database for the relevant information, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer and also outputs it as audio using the Google Cloud Text-to-Speech API, providing the worker with an immediate response. This increases work efficiency.

[0412] This system can improve the work efficiency of on-site workers and significantly reduce inquiries to support desks. Furthermore, by utilizing speech recognition and speech synthesis technologies, the user experience can be enhanced.

[0413] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0414] Step 1:

[0415] The user inputs their questions by voice into their mobile device.

[0416] Specific action: The user speaks into their mobile device saying, "Please tell me how to install the fiber optic cable."

[0417] Input: User's voice data.

[0418] Output: The device receives the audio data.

[0419] Step 2:

[0420] The device uses its built-in microphone to capture audio data.

[0421] Specific operation: The device uses the microphone to collect the user's voice as digital audio data.

[0422] Input: User's voice data.

[0423] Output: Captured digital audio data.

[0424] Step 3:

[0425] The device uses a speech recognition API (e.g., Google Cloud Speech-to-Text API) to convert speech data into text data.

[0426] Specific operation: The device sends the captured audio data to a speech recognition API, which then converts the audio into text.

[0427] Input: Captured digital audio data.

[0428] Output: Text data saying "Please tell me how to install a fiber optic cable."

[0429] Step 4:

[0430] The terminal sends the converted text data to the server. HTTP requests via the internet are used as the means of communication.

[0431] Specific operation: The terminal includes the converted text data in the HTTP request and sends it to the server.

[0432] Input: Text data "Please tell me how to install a fiber optic cable."

[0433] Output: The server receives text data.

[0434] Step 5:

[0435] The server searches the FAQ database based on the received text data.

[0436] Specific operation: The server executes the search query "How to install a fiber optic cable" against the FAQ database and searches for relevant information.

[0437] Input: Text data "Please tell me how to install a fiber optic cable."

[0438] Output: Search results from the FAQ database.

[0439] Step 6:

[0440] The server generates an answer based on the search results.

[0441] Specific operation: The server analyzes the search results and constructs an appropriate response. For example, it might say, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0442] Input: Search results from the FAQ database.

[0443] Output: The generated response.

[0444] Step 7:

[0445] The server sends the generated response to the terminal. Communication takes place via the internet.

[0446] Specific operation: The server sends the response message it has prepared to the terminal as an HTTP response.

[0447] Input: The generated response.

[0448] Output: The terminal receives the response message.

[0449] Step 8:

[0450] The device displays the received response to the user and also provides audio output.

[0451] Specific operation: The terminal displays the received response on the screen, converts it to speech using the Google Cloud Text-to-Speech API, and plays it through the speaker. For example, the display might show "The procedure for installing a fiber optic line is: (1) Check the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," and the same content would be played aloud.

[0452] Input: Received response message.

[0453] Output: The displayed response and the information played back as audio.

[0454] (Application Example 1)

[0455] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0456] In field work, prompt and appropriate information provision is necessary to immediately address problems and questions faced by workers. However, conventional systems require workers to manually search for information, which reduces efficiency. Furthermore, the technology for receiving questions via voice is limited, and there is no provision of answers using speech synthesis. As a result, there is a lack of systems that can significantly improve work efficiency, which is a major challenge.

[0457] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0458] In this invention, the server includes voice input means, conversion means for converting speech to text, communication means for transmitting text data, search means for searching text data, generation means for generating answers based on search results, receiving means for receiving the generated answers, display and audio output means for displaying and outputting the received answers, voice recognition means for receiving questions by voice, and voice synthesis means for synthesizing the generated answers into speech. This makes it possible for workers to input questions by voice and immediately receive appropriate answers in both voice and text.

[0459] "Voice input means" refers to devices or software for receiving voice data.

[0460] "Conversion methods for converting speech to text" refers to technologies and tools for converting speech data into text data.

[0461] "Communication methods for transmitting text data" refers to internet communication and other communication protocols used to send converted text data to a server.

[0462] "A search method for retrieving text data" refers to a technique or system for retrieving appropriate information from a database based on text data received on a server.

[0463] "Generative means for generating answers based on search results" refers to algorithms or systems for generating appropriate answers based on searched information.

[0464] "Receiving means for receiving generated responses" refers to equipment or software used to receive response data sent from a server.

[0465] "Display and audio output means for displaying and audio outputting received responses" refers to devices and software, including displays and speakers, for displaying received responses to the user and outputting them as audio.

[0466] "Voice recognition means for receiving questions via voice" refers to voice recognition APIs and other voice recognition technologies that recognize user voice input and convert it into text data.

[0467] "Speech synthesis means for synthesizing generated responses into speech" refers to speech synthesis technology or tools that convert text data into speech data and provide the user with the response in voice.

[0468] The present invention is a system for improving the efficiency of workers in a factory. This system includes a voice input means, a conversion means for converting voice to text, a communication means for transmitting text data, a search means for searching text data, a generation means for generating answers based on the search results, a receiving means for receiving the generated answers, a display and audio output means for displaying and outputting the received answers, a voice recognition means for receiving questions by voice, and a voice synthesis means for synthesizing the generated answers into speech.

[0469] First, the user voice-inputs their question into their smartphone. The smartphone uses its microphone to capture the voice data. Next, the smartphone uses its built-in speech recognition API to convert the voice data into text data. For example, if the user asks, "How do I fix error code 1234?", this voice will be converted into the text data "How do I fix error code 1234?".

[0470] The converted text data is sent to the server using a communication method. The server receives this text data and searches the FAQ database using a search method. Based on the search results, the server generates an appropriate answer. For example, it might generate an answer such as, "To fix error code 1234, first turn off the power and then restart. After that, reset the sensor."

[0471] The generated response is sent from the server to the smartphone. The smartphone displays the received response and also plays it back using speech synthesis. For example, the message "To fix error code 1234, first turn off the power and then restart. After that, reset the sensor" is displayed and played back.

[0472] This system includes speech recognition functionality using the Python `speech_recognition` library, communication functionality using the `requests` library, and speech synthesis functionality using gTTS (Google Text-to-Speech). This allows users to quickly ask questions and receive appropriate answers immediately.

[0473] Example of a prompt:

[0474] "How do I fix error code 1234?"

[0475] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0476] Step 1:

[0477] The user inputs their question by voice into their smartphone. At this time, the device's microphone captures the voice data. This input data is in audio format and is used for subsequent processing.

[0478] Step 2:

[0479] The audio data is sent to the speech recognition system within the device, and the speech_recognition library is used to convert the audio data into text data. The data processing performed here involves recognizing phonemes based on spectral analysis of the audio waveform data and converting them into strings. The output is text data.

[0480] Step 3:

[0481] The converted text data is sent to the server via the internet using a communication method. An HTTP request is used for this transmission, and the character data is passed to the server as input.

[0482] Step 4:

[0483] The server queries the FAQ database using a search mechanism based on the received text data to find the relevant answer. Data processing involves searching for the appropriate entry from the database index and extracting its content. The output is the retrieved answer data.

[0484] Step 5:

[0485] The server uses a generation mechanism to generate answers based on the search results. This process formats the acquired database information according to a preset format. The generated answer data is output and sent back to the terminal via the communication mechanism.

[0486] Step 6:

[0487] The terminal receives the received response data via a receiving means and presents the response to the user using display and audio output means. Display is performed on a screen, and audio output is synthesized using the gTTS library. Specifically, text data is converted into an audio file, which is then played back through the speaker. The output consists of a visual text display and audio playback.

[0488] With all processing steps now complete, users can input questions by voice and receive instant answers in both text and voice.

[0489] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0490] This invention provides a system that allows field workers to input voice data into a mobile device, convert the voice into text, and send that text data to a server to automatically obtain an appropriate response. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide responses and adjust search priorities based on the user's emotions.

[0491] Voice input and emotion recognition

[0492] The user speaks a question into their mobile device, for example, "Please tell me how to install a fiber optic cable." Simultaneously, the user's emotions are also recognized. The device has a microphone to receive voice and uses an emotion engine to recognize the user's emotions from their voice.

[0493] Speech-to-text conversion

[0494] The device uses a speech recognition API to convert the received audio data into text data. This conversion generates text data such as, "Please tell me how to install a fiber optic cable."

[0495] Emotion-based adjustment

[0496] The device uses emotion data recognized by the emotion engine to adjust the priority of searches and response generation. For example, if the user is feeling anxious or angry, the server will prioritize processing to provide a response quickly.

[0497] Sending text data

[0498] The converted text data and sentiment data are sent from the terminal to the server. HTTP requests via the internet are generally used as the means of communication.

[0499] Searching for questions and generating answers

[0500] Based on the received text data, the server searches a pre-prepared FAQ database. Based on the text data and sentiment data, the server generates the most appropriate answer. For example, the database has pre-registered answers to the question "How to install a fiber optic cable?", and when generating an answer such as "The procedure for installing a fiber optic cable is (1) to check the installation location of the equipment, (2) to connect the cables, and (3) to configure the network settings," the server adjusts the priority and tone of the answer based on sentiment data.

[0501] Submit your response

[0502] The generated response is sent from the server to the terminal. This is also done via communication means.

[0503] Display of answers and audio output

[0504] The terminal displays the received response to the user and also provides audio output. The display method and voice tone are adjusted based on sentiment data. This allows on-site workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing a fiber optic cable is (1) confirm the equipment installation location, (2) connect the cables, and (3) configure the network settings," while simultaneously playing an audio message saying, "Stay calm and review the next steps. First, confirm the equipment installation location."

[0505] Specific example

[0506] Imagine a worker installing fiber optic cables at a new site. The worker speaks into a terminal and says, "Please tell me how to install fiber optic cables." The terminal receives the audio and uses an emotion engine to simultaneously recognize the worker's urgency and emotional state. It then uses a speech recognition API to convert the audio into text data and sends the converted text data and emotion data to a server. The server searches its FAQ database for relevant information, generates an appropriate answer based on the emotion data, and sends it to the terminal. The terminal displays the answer and simultaneously communicates it to the worker via audio output.

[0507] Thus, by combining emotion recognition, the present invention makes it possible to provide appropriate information tailored to the situation of on-site workers, improve work efficiency, and significantly reduce inquiries to support desks.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The user speaks a question into their mobile device. They input a question by voice, such as "Please tell me how to install a fiber optic cable."

[0511] Step 2:

[0512] The device receives audio using its microphone. The user's voice data is acquired using a voice input method.

[0513] Step 3:

[0514] The device uses an emotion engine to recognize the user's emotions from the received audio data. For example, it can determine whether the user is anxious or anxious based on the tone and speed of their voice.

[0515] Step 4:

[0516] The device uses a speech recognition API to convert received audio data into text data. For example, it converts the audio "Please tell me how to install a fiber optic cable" into text data.

[0517] Step 5:

[0518] The terminal sends the converted text data and recognized emotion data to the server. This data is transmitted using communication methods such as HTTP requests.

[0519] Step 6:

[0520] The server searches the FAQ database based on the text data it receives. It finds the most relevant FAQ entries based on keywords and phrases.

[0521] Step 7:

[0522] The server generates appropriate answers based on the search results. For example, it provides specific steps such as, "The procedure for installing a fiber optic line is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0523] Step 8:

[0524] The server adjusts the tone and priority of the responses it provides based on sentiment data. For example, if the user is anxious, it might add reassuring phrases such as, "Please calm down and review the next steps."

[0525] Step 9:

[0526] The server sends the generated response to the terminal. The generated response data is sent to the terminal via a communication method.

[0527] Step 10:

[0528] The device displays the received response to the user. By displaying the response on the screen, it provides the user with information visually.

[0529] Step 11:

[0530] The device outputs the received response as audio. Using speech synthesis technology, the displayed response is played back as audio, providing the user with information aurally as well.

[0531] This series of steps allows field workers to quickly obtain the necessary information, improve work efficiency, and significantly reduce inquiries to support desks. Furthermore, incorporating emotion recognition enables the provision of more personalized support.

[0532] (Example 2)

[0533] Next, we will describe Example 2. 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".

[0534] There is a need for a means of providing information to enable on-site workers to respond quickly and accurately to problems they face, but conventional systems make it difficult to obtain appropriate answers that take into account the user's feelings. In particular, in emergencies or when users are feeling anxious or angry, there is a need for quick and appropriate responses that take the user's feelings into consideration.

[0535] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a voice input means, a conversion means for converting voice to text, an emotion recognition means for recognizing the user's emotions, a communication means for transmitting text data and emotion data, a search means for searching for questions based on the text data and emotion data, a generation means for generating answers based on search results and emotion data using a generation AI model, a receiving means for receiving the generated answers, and a display / audio output means for displaying and outputting the received answers. This enables field workers to provide information quickly and accurately in response to the user's emotions.

[0536] "Voice input means" refers to a device or function for acquiring voice data, such as a microphone.

[0537] "A means of converting speech to text" refers to a technology that converts speech data into text data, and speech recognition APIs are an example of this.

[0538] "Means of recognizing user emotions" refers to technologies that detect emotional states from a user's voice or text, and an emotion analysis engine is an example of this.

[0539] "Communication means for transmitting text data and sentiment data" refers to technologies that transmit converted text data and recognized sentiment data to a remote server, such as HTTP requests using the internet.

[0540] A "search method for searching for questions based on text data and sentiment data" is a technology that extracts relevant information from a database based on received data, and a search using a query database is an example of this.

[0541] "Generative means for generating answers based on search results and sentiment data using a generative AI model" refers to a technology that uses AI technology to create answers that take search results and sentiment data into consideration, and a generative AI model is an example of this.

[0542] "Means of receiving generated responses" refers to technologies for receiving generated response data from a remote server, such as receiving data via an HTTP request.

[0543] "A display and audio output means for displaying and outputting received responses" refers to a technology that displays received responses on a screen and plays them back as audio, such as a display and speakers.

[0544] This invention relates to a system in which a field worker inputs voice data into a mobile device, converts the voice into text data, sends that text data and the user's emotion data to a server, and automatically obtains an appropriate response. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it can provide responses and adjust search priorities based on the user's emotions.

[0545] The user voice-inputs a question into their mobile device. This question may include specific details such as, "Please tell me how to install a fiber optic cable." Simultaneously, the mobile device recognizes the user's emotions through their voice. The voice data is received using a microphone.

[0546] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. This conversion generates the text message, "Please tell me how to install the fiber optic cable." Simultaneously, the device uses an emotion engine (e.g., IBM Watson Tone Analyzer) to recognize the user's emotional state. Recognized emotions include impatience, anger, excitement, and calmness.

[0547] The converted text data and sentiment data are sent from the terminal to the server. An HTTP POST request over the internet is used for transmission. The server receives this data and first searches its FAQ database (e.g., query data stored in MySQL). In doing so, it uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response based on the text and sentiment data. The server generates a prompt and inputs it to the AI ​​model as follows:

[0548] Prompt: "The user is asking, 'Please tell me how to install the fiber optic cable,' and is showing signs of anxiety. As a response, explain the fiber optic cable installation procedure in a reassuring tone."

[0549] The AI ​​model generates an appropriate response based on this prompt. For example, it might generate a response that includes specific steps, such as, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings." The tone and content are adjusted to take the user's emotions into consideration.

[0550] The generated response is sent back to the terminal from the server. The terminal displays the received response on its screen and also outputs it as audio using the Google Text-to-Speech API. For example, the display might show "The procedure for installing a fiber optic line is (1) to check the location of the equipment, (2) to connect the cables, and (3) to configure the network settings," and at the same time, it might play an audio message saying, "Stay calm and check the next steps. First, check the location of the equipment."

[0551] Thus, by combining speech recognition technology, a generative AI model, and emotion recognition technology, the present invention enables the rapid and appropriate provision of information tailored to the situation of on-site workers, improving work efficiency and significantly reducing inquiries to support desks.

[0552] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0553] Step 1:

[0554] The user voice-inputs a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device receives this voice using its microphone. The received voice data is the input and is processed in the next step.

[0555] Step 2:

[0556] The device uses an emotion recognition engine to recognize the user's emotions from the voice data. The emotion recognition engine (e.g., IBM Watson Tone Analyzer) receives the voice data and outputs emotion data. Specifically, it determines whether the user is exhibiting an emotional tone (such as impatience, anger, excitement, or calmness).

[0557] Step 3:

[0558] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. In this process, audio data is input, and the text data "Please tell me how to install a fiber optic cable" is output.

[0559] Step 4:

[0560] The device collects the converted text data and recognized sentiment data and sends them to the server. HTTP POST requests over the internet are used for transmission. The input consists of text data and sentiment data, which are encoded in HTTP request format and sent to the server.

[0561] Step 5:

[0562] The server receives the HTTP request and extracts the sent text and sentiment data. This receiving operation prepares the server for the next processing. At this point, the input is text and sentiment data, and the output is the data after parsing is complete.

[0563] Step 6:

[0564] The server searches the FAQ database. Specifically, it sends a search query to the database based on the parsed text data and retrieves the appropriate information. The input to this search operation is a text query, and the output is the search result from the FAQ database.

[0565] Step 7:

[0566] The server activates a generative AI model and generates the best possible response based on search results and sentiment data. For example, the server might input the following prompt into the generative AI model:

[0567] "The user is asking, 'Please tell me how to install the fiber optic cable,' and is showing signs of impatience. As a countermeasure, please explain the fiber optic cable installation procedure in a reassuring tone."

[0568] The output of the generative AI model generates response text that takes the user's emotions into consideration.

[0569] Step 8:

[0570] The server sends the generated response text to the terminal. In this operation, the response text becomes the input data, which is then encoded in HTTP response format and sent to the terminal.

[0571] Step 9:

[0572] The device analyzes the received response text, displays it on the screen, and also outputs it as audio. Specifically, it uses the Google Text-to-Speech API to convert the text data into audio data and plays it back to the user. The input for this operation is the response text, and the output is display on the screen and audio playback.

[0573] This allows users to quickly and accurately obtain the information they need. For example, the display might show "The procedure for installing a fiber optic line is (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings," while simultaneously playing a voice message saying, "Stay calm and review the next steps. First, confirm the location of the equipment."

[0574] (Application Example 2)

[0575] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0576] To enable on-site workers to respond quickly and appropriately to machinery and equipment problems they encounter in industrial environments, it is necessary to obtain accurate information immediately. However, general search systems do not support voice input and have the problem of not being able to recognize the emotional state of workers, such as anxiety or stress, and prioritize responses accordingly. In addition, work sites are noisy, so there is a need for methods to obtain information without using one's hands.

[0577] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0578] In this invention, the server includes voice input means, conversion means for converting speech to text, emotion recognition means, and adjustment means for adjusting the priority and tone of responses based on emotion data. This makes it possible for field workers to input questions by voice, have their emotional state recognized, and then quickly receive the most appropriate response, thereby significantly improving work efficiency.

[0579] "Voice input means" refers to a device that receives the voice spoken by a user as digital data.

[0580] "Conversion means" refers to devices or software that convert received audio data into text data.

[0581] "Communication means" refers to network communication modules and protocols used to send text data to a server.

[0582] "Search method" refers to a device or software that searches for information based on text data received on the server side.

[0583] "Generation means" refers to devices or software that create appropriate answers based on retrieved information.

[0584] "Receiving means" refers to the device or software that receives the response sent from the server.

[0585] "Display and audio output means" refers to devices or software that present received responses to the user visually and audibly.

[0586] "Emotion recognition means" refers to devices or software that analyze voice data and other input data to identify the user's emotional state.

[0587] "Adjustment means" refers to devices or software that change the priority or tone of responses based on recognized emotional data.

[0588] This invention is a system that converts voice input from on-site workers or users into text and provides appropriate responses. Specific embodiments for carrying out this invention are described below.

[0589] System Configuration

[0590] Voice input method

[0591] Users use the microphone built into the smart glasses to input voice information. This voice input method allows workers to input questions about machine and equipment problems using voice.

[0592] Conversion means

[0593] The received audio data is converted into text data using the speech_recognition library. High-precision speech recognition is achieved by using the speech recognition API.

[0594] means of communication

[0595] The converted text and sentiment data are sent to a server via the smart glasses' network communication module (e.g., Wi-Fi or 4G communication). This communication is performed using HTTP requests.

[0596] emotion recognition means

[0597] The `emotion_recognition` module is used to analyze emotions from voice data. This allows us to identify emotional states from the tone and speed of the user's voice and assess stress levels and urgency.

[0598] Search methods

[0599] On the server side, the system searches the FAQ database based on the received text data. This allows for the rapid extraction of the information the user is looking for. This search method provides appropriate solutions to problems with machinery and equipment.

[0600] Adjustment means

[0601] Based on emotional data, the system adjusts the priority and tone of the generated responses. This adjustment ensures that responses are delivered in a tone that encourages a quick and calm response when the user is experiencing high levels of stress.

[0602] Generation means, receiving means, display / sound output means

[0603] The response generated on the server is sent back to the smart glasses via a communication method. The response is displayed on the smart glasses' screen and the user is guided by voice output.

[0604] Specific example

[0605] If a factory worker determines that a machine is malfunctioning, they might ask their smart glasses, "What could be causing this machine to not work?" The voice data is instantly converted into text, and an emotion recognition engine determines from their voice that it is an emergency. This data is sent to a server, which searches its FAQ database for the appropriate answer. The server generates a response such as, "It's okay. First, check if the machine is powered on, and then check if the cables are properly connected," adjusting the tone based on the emotion data. The result is then displayed on the smart glasses and guided by voice.

[0606] Prompt example

[0607] Create pseudocode for a system that uses speech recognition to acquire questions and recognize emotion data, providing appropriate answers to improve work efficiency. The system uses smart glasses for voice input, emotion recognition, server communication, and answer display. Emotion recognition is performed from the voice data to determine stress levels and urgency. The results are displayed on the smart glasses' screen and also provided via voice guidance. Please describe the system in detail using pseudocode.

[0608] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0609] Step 1:

[0610] The user performs voice input.

[0611] The user inputs a question by voice into the smart glasses. For example, they might say, "What is causing this machine not to work?" This voice data is then transmitted through the microphone and converted into digital audio data.

[0612] Input: User's voice

[0613] Output: Digital audio data

[0614] Step 2:

[0615] The device converts speech to text.

[0616] The device (smart glasses) converts digital audio data into text data using the speech_recognition library. In this process, the speech recognition API extracts characters from the audio waveform and converts them into text format.

[0617] Input: Digital audio data

[0618] Output: Text data

[0619] Step 3:

[0620] The device recognizes emotions

[0621] The device uses the emotion_recognition module to identify the user's emotions from voice data. For example, it assesses stress levels and urgency based on the user's tone of voice and speaking speed.

[0622] Input: Digital audio data

[0623] Output: Sentiment data

[0624] Step 4:

[0625] The device sends text data and sentiment data to the server.

[0626] The device sends the converted text data and sentiment data to the server via a communication module (e.g., Wi-Fi, 4G). This transmission is typically done using HTTP requests.

[0627] Input: Text data, sentiment data

[0628] Output: HTTP request to the server

[0629] Step 5:

[0630] The server performs a search based on text data.

[0631] The server searches the FAQ database based on the received text data. In this process, it quickly extracts information related to the entered question.

[0632] Input: Text data

[0633] Output: Search results data

[0634] Step 6:

[0635] The server generates and adjusts the response.

[0636] The server adjusts the priority and tone of generated responses based on emotional data. For example, if the user is feeling anxious, it will immediately provide a response in a quick and calm tone.

[0637] Input: Search results data, sentiment data

[0638] Output: Adjusted response data

[0639] Step 7:

[0640] The server sends the adjusted response data to the terminal.

[0641] The server sends the adjusted response data to the terminal. This transmission is again performed using an HTTP request.

[0642] Input: Adjusted response data

[0643] Output: HTTP response to the terminal

[0644] Step 8:

[0645] The device displays and outputs the answer aloud.

[0646] The device displays the received response on its screen and also provides audio output. This allows users to quickly obtain the information they need, both visually and aurally. Additional voice input is also possible if the user requires further guidance or assistance.

[0647] Input: Adjusted response data

[0648] Output: Display, audio playback

[0649] Through the steps described above, the present invention significantly improves the work efficiency of on-site workers and enables quick and appropriate responses.

[0650] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0651] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0652] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0653] [Third Embodiment]

[0654] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0655] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0656] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0657] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0658] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0659] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0660] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0661] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0662] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0663] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0664] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0665] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0666] This invention provides a system that allows field workers to input voice data into a mobile device, convert the voice into text, and send that text data to a server to automatically obtain an appropriate response. The system operates as follows:

[0667] Receiving voice input

[0668] The user speaks a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device has a microphone that can receive voice commands.

[0669] Speech-to-text conversion

[0670] The device uses a speech recognition API to convert the received audio data into text data. This conversion generates text data such as, "Please tell me how to install a fiber optic cable."

[0671] Sending text data

[0672] The converted text data is sent from the terminal to the server. The communication method typically used is an HTTP request via the internet.

[0673] Searching for questions and generating answers

[0674] Based on the received text data, the server searches a pre-prepared FAQ database. For example, the database contains pre-registered answers to the question "How to install a fiber optic cable," and the server searches for and retrieves the relevant entry. Based on the search results, the server generates an answer such as, "The procedure for installing a fiber optic cable is: (1) confirm the installation location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0675] Submit your response

[0676] The generated response is sent from the server to the terminal. This is also done via communication means.

[0677] Display of answers and audio output

[0678] The terminal displays the received response to the user and also provides audio output. This allows on-site workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing the fiber optic cable is: (1) Confirm the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," while the same content is simultaneously played aloud.

[0679] Specific example

[0680] For example, suppose a worker is installing fiber optic cables at a new site. The worker says into the terminal, "Please tell me how to install fiber optic cables." The terminal receives the voice and converts it into text data using a speech recognition API. Then, it sends the converted text data to a server. The server searches its FAQ database for the relevant information, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer and simultaneously outputs it as audio, providing the worker with an immediate response and improving work efficiency.

[0681] Thus, by providing information quickly, the present invention can improve the work efficiency of on-site workers and significantly reduce inquiries to support desks.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] The user speaks a question into their mobile device. They input a question by voice, such as "Please tell me how to install a fiber optic cable."

[0685] Step 2:

[0686] The device receives audio using its microphone. The user's voice data is acquired using a voice input method.

[0687] Step 3:

[0688] The device uses a speech recognition API to convert received audio data into text data. For example, it uses the speech recognition API to convert the audio "Please tell me how to install a fiber optic cable" into text data.

[0689] Step 4:

[0690] The terminal sends the converted text data to the server. The text data is sent to the server using communication methods such as HTTP requests.

[0691] Step 5:

[0692] The server searches the FAQ database based on the text data it receives. It uses keywords and phrases contained in the text data to search the FAQ database for related questions and their answers.

[0693] Step 6:

[0694] The server generates appropriate answers based on the search results. For example, in response to the question "How to install a fiber optic cable," it might generate an answer such as, "The procedure for installing a fiber optic cable is: (1) Check the installation location of the equipment, (2) Connect the cables, and (3) Configure the network settings."

[0695] Step 7:

[0696] The server sends the generated response to the terminal. The generated response data is sent to the terminal via a communication method.

[0697] Step 8:

[0698] The device displays the received response to the user. By displaying the response on the screen, it provides the user with information visually.

[0699] Step 9:

[0700] The device simultaneously outputs the received responses as audio. Using speech synthesis technology, the displayed responses are played back as audio, providing the user with information aurally as well.

[0701] This series of steps allows on-site workers, who are the users, to quickly obtain the necessary information, improve work efficiency, and significantly reduce inquiries to support desks.

[0702] (Example 1)

[0703] Next, we will describe Example 1. 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."

[0704] Traditional methods for obtaining necessary information quickly on-site by field workers were inefficient due to the time-consuming nature of information retrieval. Furthermore, frequent inquiries to support desks increased operational costs. There was also a need for accuracy and speed in voice-to-text conversion and subsequent information retrieval and response provision.

[0705] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0706] In this invention, the server includes voice input means, conversion means for converting speech to text, communication means for transmitting text data, search means for searching text data, generation means for generating answers based on search results, receiving means for receiving the generated answers, and display / speech output means for displaying and outputting the received answers. This enables field workers to quickly obtain necessary information through voice input, improving work efficiency and significantly reducing inquiries to support desks.

[0707] "Voice input means" refers to devices or interfaces that users use to input questions or commands verbally.

[0708] "Conversion means" refers to the technology or algorithm used to convert received audio data into text data.

[0709] "Communication methods" refer to internet connections and protocols used to transmit text data and other information to remote servers.

[0710] "Search methods" refer to technologies and algorithms that retrieve relevant information or answers from a database based on received text data.

[0711] "Generation means" refers to the technology or section that constructs and generates appropriate answers based on search results.

[0712] "Receiving means" refers to the technology or interface used to receive data or responses sent from a server on a terminal.

[0713] "Display and audio output means" refers to displays and speech synthesis technologies that visually display received responses to the user and also output them as audio.

[0714] "Speech recognition API" refers to an application programming interface for converting speech data into text data.

[0715] An "FAQ database" refers to a collection of information that stores frequently asked questions (FAQs) and their answers.

[0716] A "speech synthesis API" refers to an application programming interface for converting text data into speech data.

[0717] An "HTTP request" refers to a form of communication protocol in which a web browser or other client requests a specific action from a web server.

[0718] This invention relates to a system in which a field worker inputs voice data into a mobile device, which is then converted into text and sent to a server to automatically obtain an appropriate response. The system operates as follows:

[0719] Hardware and software to be used

[0720] The device used is a mobile device with a built-in microphone (e.g., smartphone, tablet). This device uses a speech recognition API to convert speech to text. Specifically, the Google Cloud Speech-to-Text API is used. The device also uses the Google Cloud Text-to-Speech API to convert text to speech and provide information to the user in audio format.

[0721] The server uses either cloud-based or on-premises servers. This server searches an FAQ database based on the received text data and generates answers based on the search results. An optimized database search algorithm is used as the search method.

[0722] System operation

[0723] The user voice-inputs a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device uses its built-in microphone to capture this voice. Then, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text. Specifically, this voice data is converted into the text "Please tell me how to install a fiber optic cable."

[0724] The converted text data is sent from the terminal to the server. HTTP requests over the internet are the commonly used means of communication. The server searches a pre-prepared FAQ database based on the received text data. For example, the database has answers registered in advance to the question "How to install a fiber optic cable?". The server searches for the relevant entry and generates an answer such as, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0725] The generated response is sent from the server to the terminal. The terminal displays the received response to the user and also outputs it as audio using the Google Cloud Text-to-Speech API. This allows field workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing a fiber optic cable is: (1) Confirm the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," and the same content is played aloud through the speaker.

[0726] Specific example

[0727] For example, suppose a worker is installing fiber optic cables at a new site. The worker speaks into a terminal and says, "Please tell me how to install fiber optic cables." The terminal receives the audio and converts it into text data using the Google Cloud Speech-to-Text API. The converted text data is then sent to a server. The server searches its FAQ database for the relevant information, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer and also outputs it as audio using the Google Cloud Text-to-Speech API, providing the worker with an immediate response. This increases work efficiency.

[0728] This system can improve the work efficiency of on-site workers and significantly reduce inquiries to support desks. Furthermore, by utilizing speech recognition and speech synthesis technologies, the user experience can be enhanced.

[0729] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0730] Step 1:

[0731] The user inputs their questions by voice into their mobile device.

[0732] Specific action: The user speaks into their mobile device saying, "Please tell me how to install the fiber optic cable."

[0733] Input: User's voice data.

[0734] Output: The device receives the audio data.

[0735] Step 2:

[0736] The device uses its built-in microphone to capture audio data.

[0737] Specific operation: The device uses the microphone to collect the user's voice as digital audio data.

[0738] Input: User's voice data.

[0739] Output: Captured digital audio data.

[0740] Step 3:

[0741] The device uses a speech recognition API (e.g., Google Cloud Speech-to-Text API) to convert speech data into text data.

[0742] Specific operation: The device sends the captured audio data to a speech recognition API, which then converts the audio into text.

[0743] Input: Captured digital audio data.

[0744] Output: Text data saying "Please tell me how to install a fiber optic cable."

[0745] Step 4:

[0746] The terminal sends the converted text data to the server. HTTP requests via the internet are used as the means of communication.

[0747] Specific operation: The terminal includes the converted text data in the HTTP request and sends it to the server.

[0748] Input: Text data "Please tell me how to install a fiber optic cable."

[0749] Output: The server receives text data.

[0750] Step 5:

[0751] The server searches the FAQ database based on the received text data.

[0752] Specific operation: The server executes the search query "How to install a fiber optic cable" against the FAQ database and searches for relevant information.

[0753] Input: Text data "Please tell me how to install a fiber optic cable."

[0754] Output: Search results from the FAQ database.

[0755] Step 6:

[0756] The server generates an answer based on the search results.

[0757] Specific operation: The server analyzes the search results and constructs an appropriate response. For example, it might say, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0758] Input: Search results from the FAQ database.

[0759] Output: The generated response.

[0760] Step 7:

[0761] The server sends the generated response to the terminal. Communication takes place via the internet.

[0762] Specific operation: The server sends the response message it has prepared to the terminal as an HTTP response.

[0763] Input: The generated response.

[0764] Output: The terminal receives the response message.

[0765] Step 8:

[0766] The device displays the received response to the user and also provides audio output.

[0767] Specific operation: The terminal displays the received response on the screen, converts it to speech using the Google Cloud Text-to-Speech API, and plays it through the speaker. For example, the display might show "The procedure for installing a fiber optic line is: (1) Check the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," and the same content would be played aloud.

[0768] Input: Received response message.

[0769] Output: The displayed response and the information played back as audio.

[0770] (Application Example 1)

[0771] Next, we will explain Application Example 1. In the following explanation, 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."

[0772] In field work, prompt and appropriate information provision is necessary to immediately address problems and questions faced by workers. However, conventional systems require workers to manually search for information, which reduces efficiency. Furthermore, the technology for receiving questions via voice is limited, and there is no provision of answers using speech synthesis. As a result, there is a lack of systems that can significantly improve work efficiency, which is a major challenge.

[0773] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0774] In this invention, the server includes voice input means, conversion means for converting speech to text, communication means for transmitting text data, search means for searching text data, generation means for generating answers based on search results, receiving means for receiving the generated answers, display and audio output means for displaying and outputting the received answers, voice recognition means for receiving questions by voice, and voice synthesis means for synthesizing the generated answers into speech. This makes it possible for workers to input questions by voice and immediately receive appropriate answers in both voice and text.

[0775] "Voice input means" refers to devices or software for receiving voice data.

[0776] "Conversion methods for converting speech to text" refers to technologies and tools for converting speech data into text data.

[0777] "Communication methods for transmitting text data" refers to internet communication and other communication protocols used to send converted text data to a server.

[0778] "A search method for retrieving text data" refers to a technique or system for retrieving appropriate information from a database based on text data received on a server.

[0779] "Generative means for generating answers based on search results" refers to algorithms or systems for generating appropriate answers based on searched information.

[0780] "Receiving means for receiving generated responses" refers to equipment or software used to receive response data sent from a server.

[0781] "Display and audio output means for displaying and audio outputting received responses" refers to devices and software, including displays and speakers, for displaying received responses to the user and outputting them as audio.

[0782] "Voice recognition means for receiving questions via voice" refers to voice recognition APIs and other voice recognition technologies that recognize user voice input and convert it into text data.

[0783] "Speech synthesis means for synthesizing generated responses into speech" refers to speech synthesis technology or tools that convert text data into speech data and provide the user with the response in voice.

[0784] The present invention is a system for improving the efficiency of workers in a factory. This system includes a voice input means, a conversion means for converting voice to text, a communication means for transmitting text data, a search means for searching text data, a generation means for generating answers based on the search results, a receiving means for receiving the generated answers, a display and audio output means for displaying and outputting the received answers, a voice recognition means for receiving questions by voice, and a voice synthesis means for synthesizing the generated answers into speech.

[0785] First, the user voice-inputs their question into their smartphone. The smartphone uses its microphone to capture the voice data. Next, the smartphone uses its built-in speech recognition API to convert the voice data into text data. For example, if the user asks, "How do I fix error code 1234?", this voice will be converted into the text data "How do I fix error code 1234?".

[0786] The converted text data is sent to the server using a communication method. The server receives this text data and searches the FAQ database using a search method. Based on the search results, the server generates an appropriate answer. For example, it might generate an answer such as, "To fix error code 1234, first turn off the power and then restart. After that, reset the sensor."

[0787] The generated response is sent from the server to the smartphone. The smartphone displays the received response and also plays it back using speech synthesis. For example, the message "To fix error code 1234, first turn off the power and then restart. After that, reset the sensor" is displayed and played back.

[0788] This system includes speech recognition functionality using the Python `speech_recognition` library, communication functionality using the `requests` library, and speech synthesis functionality using gTTS (Google Text-to-Speech). This allows users to quickly ask questions and receive appropriate answers immediately.

[0789] Example of a prompt:

[0790] "How do I fix error code 1234?"

[0791] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0792] Step 1:

[0793] The user voice-inputs their question into their smartphone. The device's microphone captures the audio data. This input data is in audio format and is used for subsequent processing.

[0794] Step 2:

[0795] The audio data is sent to the speech recognition system within the device, and the speech_recognition library is used to convert the audio data into text data. The data processing performed here involves recognizing phonemes based on spectral analysis of the audio waveform data and converting them into strings. The output is text data.

[0796] Step 3:

[0797] The converted text data is sent to the server via the internet using a communication method. An HTTP request is used for this transmission, and the character data is passed to the server as input.

[0798] Step 4:

[0799] The server queries the FAQ database using a search mechanism based on the received text data to find the relevant answer. Data processing involves searching for the appropriate entry from the database index and extracting its content. The output is the retrieved answer data.

[0800] Step 5:

[0801] The server uses a generation mechanism to generate answers based on the search results. This process formats the acquired database information according to a preset format. The generated answer data is output and sent back to the terminal via the communication mechanism.

[0802] Step 6:

[0803] The terminal receives the received response data via a receiving means and presents the response to the user using display and audio output means. Display is performed on a screen, and audio output is synthesized using the gTTS library. Specifically, text data is converted into an audio file, which is then played back through the speaker. The output consists of a visual text display and audio playback.

[0804] With all processing steps now complete, users can input questions by voice and receive instant answers in both text and voice.

[0805] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0806] This invention provides a system that allows field workers to input voice data into a mobile device, convert the voice into text, and send that text data to a server to automatically obtain an appropriate response. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide responses and adjust search priorities based on the user's emotions.

[0807] Voice input and emotion recognition

[0808] The user speaks a question into their mobile device, for example, "Please tell me how to install a fiber optic cable." Simultaneously, the user's emotions are also recognized. The device has a microphone to receive voice and uses an emotion engine to recognize the user's emotions from their voice.

[0809] Speech-to-text conversion

[0810] The device uses a speech recognition API to convert the received audio data into text data. This conversion generates text data such as, "Please tell me how to install a fiber optic cable."

[0811] Emotion-based adjustment

[0812] The device uses emotion data recognized by the emotion engine to adjust search and response generation priorities. For example, if the user is feeling anxious or angry, the server prioritizes processing to provide a response quickly.

[0813] Sending text data

[0814] The converted text data and sentiment data are sent from the terminal to the server. HTTP requests via the internet are generally used as the means of communication.

[0815] Searching for questions and generating answers

[0816] Based on the received text data, the server searches a pre-prepared FAQ database. Based on the text data and sentiment data, the server generates the most appropriate answer. For example, the database has pre-registered answers to the question "How to install a fiber optic cable?", and when generating an answer such as "The procedure for installing a fiber optic cable is (1) to check the installation location of the equipment, (2) to connect the cables, and (3) to configure the network settings," the server adjusts the priority and tone of the answer based on sentiment data.

[0817] Submit your response

[0818] The generated response is sent from the server to the terminal. This is also done via communication means.

[0819] Display of answers and audio output

[0820] The terminal displays the received response to the user and also provides audio output. The display method and voice tone are adjusted based on sentiment data. This allows on-site workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing a fiber optic cable is (1) confirm the equipment installation location, (2) connect the cables, and (3) configure the network settings," while simultaneously playing an audio message saying, "Stay calm and review the next steps. First, confirm the equipment installation location."

[0821] Specific example

[0822] Imagine a worker installing fiber optic cables at a new site. The worker speaks into a terminal and says, "Please tell me how to install fiber optic cables." The terminal receives the audio and uses an emotion engine to simultaneously recognize the worker's urgency and emotional state. It then uses a speech recognition API to convert the audio into text data and sends the converted text data and emotion data to a server. The server searches its FAQ database for relevant information, generates an appropriate answer based on the emotion data, and sends it to the terminal. The terminal displays the answer and simultaneously communicates it to the worker via audio output.

[0823] Thus, by combining emotion recognition, the present invention makes it possible to provide appropriate information tailored to the situation of on-site workers, improve work efficiency, and significantly reduce inquiries to support desks.

[0824] The following describes the processing flow.

[0825] Step 1:

[0826] The user speaks a question into their mobile device. They input a question by voice, such as "Please tell me how to install a fiber optic cable."

[0827] Step 2:

[0828] The device receives audio using its microphone. The user's voice data is acquired using a voice input method.

[0829] Step 3:

[0830] The device uses an emotion engine to recognize the user's emotions from the received audio data. For example, it can determine whether the user is anxious or anxious based on the tone and speed of their voice.

[0831] Step 4:

[0832] The device uses a speech recognition API to convert received audio data into text data. For example, it converts the audio "Please tell me how to install a fiber optic cable" into text data.

[0833] Step 5:

[0834] The terminal sends the converted text data and recognized emotion data to the server. This data is transmitted using communication methods such as HTTP requests.

[0835] Step 6:

[0836] The server searches the FAQ database based on the text data it receives. It finds the most relevant FAQ entries based on keywords and phrases.

[0837] Step 7:

[0838] The server generates appropriate answers based on the search results. For example, it provides specific steps such as, "The procedure for installing a fiber optic line is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0839] Step 8:

[0840] The server adjusts the tone and priority of the responses it provides based on sentiment data. For example, if the user is anxious, it might add reassuring phrases such as, "Please calm down and review the next steps."

[0841] Step 9:

[0842] The server sends the generated response to the terminal. The generated response data is sent to the terminal via a communication method.

[0843] Step 10:

[0844] The device displays the received response to the user. By displaying the response on the screen, it provides the user with information visually.

[0845] Step 11:

[0846] The device outputs the received response as audio. Using speech synthesis technology, the displayed response is played back as audio, providing the user with information aurally as well.

[0847] This series of steps allows field workers to quickly obtain the necessary information, improve work efficiency, and significantly reduce inquiries to support desks. Furthermore, incorporating emotion recognition enables the provision of more personalized support.

[0848] (Example 2)

[0849] Next, we will describe Example 2. 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."

[0850] There is a need for a means of providing information to enable on-site workers to respond quickly and accurately to problems they face, but conventional systems make it difficult to obtain appropriate answers that take into account the user's feelings. In particular, in emergencies or when users are feeling anxious or angry, there is a need for quick and appropriate responses that take the user's feelings into consideration.

[0851] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a voice input means, a conversion means for converting voice to text, an emotion recognition means for recognizing the user's emotions, a communication means for transmitting text data and emotion data, a search means for searching for questions based on the text data and emotion data, a generation means for generating answers based on search results and emotion data using a generation AI model, a receiving means for receiving the generated answers, and a display / audio output means for displaying and outputting the received answers. This enables field workers to provide information quickly and accurately in response to the user's emotions.

[0852] "Voice input means" refers to a device or function for acquiring voice data, such as a microphone.

[0853] "A means of converting speech to text" refers to a technology that converts speech data into text data, and speech recognition APIs are an example of this.

[0854] "Means of recognizing user emotions" refers to technologies that detect emotional states from a user's voice or text, and an emotion analysis engine is an example of this.

[0855] "Communication means for transmitting text data and sentiment data" refers to technologies that transmit converted text data and recognized sentiment data to a remote server, such as HTTP requests using the internet.

[0856] A "search method for searching for questions based on text data and sentiment data" is a technology that extracts relevant information from a database based on received data, and a search using a query database is an example of this.

[0857] "Generative means for generating answers based on search results and sentiment data using a generative AI model" refers to a technology that uses AI technology to create answers that take search results and sentiment data into consideration, and a generative AI model is an example of this.

[0858] "Means of receiving generated responses" refers to technologies for receiving generated response data from a remote server, such as receiving data via an HTTP request.

[0859] "A display and audio output means for displaying and outputting received responses" refers to a technology that displays received responses on a screen and plays them back as audio, such as a display and speakers.

[0860] This invention relates to a system in which a field worker inputs voice data into a mobile device, converts the voice into text data, sends that text data and the user's emotion data to a server, and automatically obtains an appropriate response. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it can provide responses and adjust search priorities based on the user's emotions.

[0861] The user voice-inputs a question into their mobile device. This question may include specific details such as, "Please tell me how to install a fiber optic cable." Simultaneously, the mobile device recognizes the user's emotions through their voice. The voice data is received using a microphone.

[0862] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. This conversion generates the text message, "Please tell me how to install the fiber optic cable." Simultaneously, the device uses an emotion engine (e.g., IBM Watson Tone Analyzer) to recognize the user's emotional state. Recognized emotions include impatience, anger, excitement, and calmness.

[0863] The converted text data and sentiment data are sent from the terminal to the server. An HTTP POST request over the internet is used for transmission. The server receives this data and first searches its FAQ database (e.g., query data stored in MySQL). In doing so, it uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response based on the text and sentiment data. The server generates a prompt and inputs it to the AI ​​model as follows:

[0864] Prompt: "The user is asking, 'Please tell me how to install the fiber optic cable,' and is showing signs of anxiety. As a response, explain the fiber optic cable installation procedure in a reassuring tone."

[0865] The AI ​​model generates an appropriate response based on this prompt. For example, it might generate a response that includes specific steps, such as, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings." The tone and content are adjusted to take the user's emotions into consideration.

[0866] The generated response is sent back to the terminal from the server. The terminal displays the received response on its screen and also outputs it as audio using the Google Text-to-Speech API. For example, the display might show "The procedure for installing a fiber optic line is (1) to check the location of the equipment, (2) to connect the cables, and (3) to configure the network settings," and at the same time, it might play an audio message saying, "Stay calm and check the next steps. First, check the location of the equipment."

[0867] Thus, by combining speech recognition technology, a generative AI model, and emotion recognition technology, the present invention enables the rapid and appropriate provision of information tailored to the situation of on-site workers, improving work efficiency and significantly reducing inquiries to support desks.

[0868] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0869] Step 1:

[0870] The user voice-inputs a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device receives this voice using its microphone. The received voice data is the input and is processed in the next step.

[0871] Step 2:

[0872] The device uses an emotion recognition engine to recognize the user's emotions from the voice data. The emotion recognition engine (e.g., IBM Watson Tone Analyzer) receives the voice data and outputs emotion data. Specifically, it determines whether the user is exhibiting an emotional tone (such as impatience, anger, excitement, or calmness).

[0873] Step 3:

[0874] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. In this process, audio data is input, and the text data "Please tell me how to install a fiber optic cable" is output.

[0875] Step 4:

[0876] The device collects the converted text data and recognized sentiment data and sends them to the server. HTTP POST requests over the internet are used for transmission. The input consists of text data and sentiment data, which are encoded in HTTP request format and sent to the server.

[0877] Step 5:

[0878] The server receives the HTTP request and extracts the sent text and sentiment data. This receiving operation prepares the server for the next processing. At this point, the input is text and sentiment data, and the output is the data after parsing is complete.

[0879] Step 6:

[0880] The server searches the FAQ database. Specifically, it sends a search query to the database based on the parsed text data and retrieves the appropriate information. The input to this search operation is a text query, and the output is the search result from the FAQ database.

[0881] Step 7:

[0882] The server activates a generative AI model and generates the best possible response based on search results and sentiment data. For example, the server might input the following prompt into the generative AI model:

[0883] "The user is asking, 'Please tell me how to install the fiber optic cable,' and is showing signs of impatience. As a countermeasure, please explain the fiber optic cable installation procedure in a reassuring tone."

[0884] The output of the generative AI model generates response text that takes the user's emotions into consideration.

[0885] Step 8:

[0886] The server sends the generated response text to the terminal. In this operation, the response text becomes the input data, which is then encoded in HTTP response format and sent to the terminal.

[0887] Step 9:

[0888] The device analyzes the received response text, displays it on the screen, and also outputs it as audio. Specifically, it uses the Google Text-to-Speech API to convert the text data into audio data and plays it back to the user. The input for this operation is the response text, and the output is display on the screen and audio playback.

[0889] This allows users to quickly and accurately obtain the information they need. For example, the display might show "The procedure for installing a fiber optic line is (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings," while simultaneously playing a voice message saying, "Stay calm and review the next steps. First, confirm the location of the equipment."

[0890] (Application Example 2)

[0891] Next, we will explain application example 2. In the following explanation, 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."

[0892] To enable on-site workers to respond quickly and appropriately to machinery and equipment problems they encounter in industrial environments, it is necessary to obtain accurate information immediately. However, general search systems do not support voice input and have the problem of not being able to recognize the emotional state of workers, such as anxiety or stress, and prioritize responses accordingly. In addition, work sites are noisy, so there is a need for methods to obtain information without using one's hands.

[0893] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0894] In this invention, the server includes voice input means, conversion means for converting speech to text, emotion recognition means, and adjustment means for adjusting the priority and tone of responses based on emotion data. This makes it possible for field workers to input questions by voice, have their emotional state recognized, and then quickly receive the most appropriate response, thereby significantly improving work efficiency.

[0895] "Voice input means" refers to a device that receives the voice spoken by a user as digital data.

[0896] "Conversion means" refers to devices or software that convert received audio data into text data.

[0897] "Communication means" refers to network communication modules and protocols used to send text data to a server.

[0898] "Search method" refers to a device or software that searches for information based on text data received on the server side.

[0899] "Generation means" refers to devices or software that create appropriate answers based on retrieved information.

[0900] "Receiving means" refers to the device or software that receives the response sent from the server.

[0901] "Display and audio output means" refers to devices or software that present received responses to the user visually and audibly.

[0902] "Emotion recognition means" refers to devices or software that analyze voice data and other input data to identify the user's emotional state.

[0903] "Adjustment means" refers to devices or software that change the priority or tone of responses based on recognized emotional data.

[0904] This invention is a system that converts voice input from on-site workers or users into text and provides appropriate responses. Specific embodiments for carrying out this invention are described below.

[0905] System Configuration

[0906] Voice input method

[0907] Users use the microphone built into the smart glasses to input voice information. This voice input method allows workers to input questions about machine and equipment problems using voice.

[0908] Conversion means

[0909] The received audio data is converted into text data using the speech_recognition library. High-precision speech recognition is achieved by using the speech recognition API.

[0910] means of communication

[0911] The converted text and sentiment data are sent to a server via the smart glasses' network communication module (e.g., Wi-Fi or 4G communication). This communication is performed using HTTP requests.

[0912] emotion recognition means

[0913] The `emotion_recognition` module is used to analyze emotions from voice data. This allows us to identify emotional states from the tone and speed of the user's voice and assess stress levels and urgency.

[0914] Search methods

[0915] On the server side, the system searches the FAQ database based on the received text data. This allows for the rapid extraction of the information the user is looking for. This search method provides appropriate solutions to problems with machinery and equipment.

[0916] Adjustment means

[0917] Based on emotional data, the system adjusts the priority and tone of the generated responses. This adjustment ensures that responses are delivered in a tone that encourages a quick and calm response when the user is experiencing high levels of stress.

[0918] Generation means, receiving means, display / sound output means

[0919] The response generated on the server is sent back to the smart glasses via a communication method. The response is displayed on the smart glasses' screen and the user is guided by voice output.

[0920] Specific example

[0921] If a factory worker determines that a machine is malfunctioning, they might ask their smart glasses, "What could be causing this machine to not work?" The voice data is instantly converted into text, and an emotion recognition engine determines from their voice that it is an emergency. This data is sent to a server, which searches its FAQ database for the appropriate answer. The server generates a response such as, "It's okay. First, check if the machine is powered on, and then check if the cables are properly connected," adjusting the tone based on the emotion data. The result is then displayed on the smart glasses and guided by voice.

[0922] Prompt example

[0923] Create pseudocode for a system that uses speech recognition to acquire questions and recognize emotion data, providing appropriate answers to improve work efficiency. The system uses smart glasses for voice input, emotion recognition, server communication, and answer display. Emotion recognition is performed from the voice data to determine stress levels and urgency. The results are displayed on the smart glasses' screen and also provided via voice guidance. Please describe the system in detail using pseudocode.

[0924] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0925] Step 1:

[0926] The user performs voice input.

[0927] The user inputs a question by voice into the smart glasses. For example, they might say, "What is causing this machine not to work?" This voice data is then transmitted through the microphone and converted into digital audio data.

[0928] Input: User's voice

[0929] Output: Digital audio data

[0930] Step 2:

[0931] The device converts speech to text.

[0932] The device (smart glasses) converts digital audio data into text data using the speech_recognition library. In this process, the speech recognition API extracts characters from the audio waveform and converts them into text format.

[0933] Input: Digital audio data

[0934] Output: Text data

[0935] Step 3:

[0936] The device recognizes emotions

[0937] The device uses the emotion_recognition module to identify the user's emotions from voice data. For example, it assesses stress levels and urgency based on the user's tone of voice and speaking speed.

[0938] Input: Digital audio data

[0939] Output: Sentiment data

[0940] Step 4:

[0941] The device sends text data and sentiment data to the server.

[0942] The device sends the converted text data and sentiment data to the server via a communication module (e.g., Wi-Fi, 4G). This transmission is typically done using HTTP requests.

[0943] Input: Text data, sentiment data

[0944] Output: HTTP request to the server

[0945] Step 5:

[0946] The server performs a search based on text data.

[0947] The server searches the FAQ database based on the received text data. In this process, it quickly extracts information related to the entered question.

[0948] Input: Text data

[0949] Output: Search results data

[0950] Step 6:

[0951] The server generates and adjusts the response.

[0952] The server adjusts the priority and tone of generated responses based on emotional data. For example, if the user is feeling anxious, it will immediately provide a response in a quick and calm tone.

[0953] Input: Search results data, sentiment data

[0954] Output: Adjusted response data

[0955] Step 7:

[0956] The server sends the adjusted response data to the terminal.

[0957] The server sends the adjusted response data to the terminal. This transmission is again performed using an HTTP request.

[0958] Input: Adjusted response data

[0959] Output: HTTP response to the terminal

[0960] Step 8:

[0961] The device displays and outputs the answer aloud.

[0962] The device displays the received response on its screen and also provides audio output. This allows users to quickly obtain the information they need, both visually and aurally. Additional voice input is also possible if the user requires further guidance or assistance.

[0963] Input: Adjusted response data

[0964] Output: Display, audio playback

[0965] Through the steps described above, the present invention significantly improves the work efficiency of on-site workers and enables quick and appropriate responses.

[0966] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0967] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0968] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0969] [Fourth Embodiment]

[0970] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0971] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0972] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0973] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0974] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0975] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0976] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0977] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0978] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0979] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0980] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0981] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0982] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0983] This invention provides a system that allows field workers to input voice data into a mobile device, convert the voice into text, and send that text data to a server to automatically obtain an appropriate response. The system operates as follows:

[0984] Receiving voice input

[0985] The user speaks a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device has a microphone that can receive voice commands.

[0986] Speech-to-text conversion

[0987] The device uses a speech recognition API to convert the received audio data into text data. This conversion generates text data such as, "Please tell me how to install a fiber optic cable."

[0988] Sending text data

[0989] The converted text data is sent from the terminal to the server. The communication method typically used is an HTTP request via the internet.

[0990] Searching for questions and generating answers

[0991] Based on the received text data, the server searches a pre-prepared FAQ database. For example, the database contains pre-registered answers to the question "How to install a fiber optic cable," and the server searches for and retrieves the relevant entry. Based on the search results, the server generates an answer such as, "The procedure for installing a fiber optic cable is: (1) confirm the installation location of the equipment, (2) connect the cables, and (3) configure the network settings."

[0992] Submit your response

[0993] The generated response is sent from the server to the terminal. This is also done via communication means.

[0994] Display of answers and audio output

[0995] The terminal displays the received response to the user and also provides audio output. This allows on-site workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing the fiber optic cable is: (1) Confirm the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," while the same content is simultaneously played aloud.

[0996] Specific example

[0997] For example, suppose a worker is installing fiber optic cables at a new site. The worker says into the terminal, "Please tell me how to install fiber optic cables." The terminal receives the voice and converts it into text data using a speech recognition API. Then, it sends the converted text data to a server. The server searches its FAQ database for the relevant information, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer and simultaneously outputs it as audio, providing the worker with an immediate response and improving work efficiency.

[0998] Thus, by providing information quickly, the present invention can improve the work efficiency of on-site workers and significantly reduce inquiries to support desks.

[0999] The following describes the processing flow.

[1000] Step 1:

[1001] The user speaks a question into their mobile device. They input a question by voice, such as "Please tell me how to install a fiber optic cable."

[1002] Step 2:

[1003] The device receives audio using its microphone. The user's voice data is acquired using a voice input method.

[1004] Step 3:

[1005] The device uses a speech recognition API to convert received audio data into text data. For example, it uses the speech recognition API to convert the audio "Please tell me how to install a fiber optic cable" into text data.

[1006] Step 4:

[1007] The terminal sends the converted text data to the server. The text data is sent to the server using communication methods such as HTTP requests.

[1008] Step 5:

[1009] The server searches the FAQ database based on the text data it receives. It uses keywords and phrases contained in the text data to search the FAQ database for related questions and their answers.

[1010] Step 6:

[1011] The server generates appropriate answers based on the search results. For example, in response to the question "How to install a fiber optic cable," it might generate an answer such as, "The procedure for installing a fiber optic cable is: (1) Check the installation location of the equipment, (2) Connect the cables, and (3) Configure the network settings."

[1012] Step 7:

[1013] The server sends the generated response to the terminal. The generated response data is sent to the terminal via a communication method.

[1014] Step 8:

[1015] The device displays the received response to the user. By displaying the response on the screen, it provides the user with information visually.

[1016] Step 9:

[1017] The device simultaneously outputs the received responses as audio. Using speech synthesis technology, the displayed responses are played back as audio, providing the user with information aurally as well.

[1018] This series of steps allows on-site workers, who are the users, to quickly obtain the necessary information, improve work efficiency, and significantly reduce inquiries to support desks.

[1019] (Example 1)

[1020] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1021] Traditional methods for obtaining necessary information quickly on-site by field workers were inefficient due to the time-consuming nature of information retrieval. Furthermore, frequent inquiries to support desks increased operational costs. There was also a need for accuracy and speed in voice-to-text conversion and subsequent information retrieval and response provision.

[1022] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1023] In this invention, the server includes voice input means, conversion means for converting speech to text, communication means for transmitting text data, search means for searching text data, generation means for generating answers based on search results, receiving means for receiving the generated answers, and display / speech output means for displaying and outputting the received answers. This enables field workers to quickly obtain necessary information through voice input, improving work efficiency and significantly reducing inquiries to support desks.

[1024] "Voice input means" refers to devices or interfaces that users use to input questions or commands verbally.

[1025] "Conversion means" refers to the technology or algorithm used to convert received audio data into text data.

[1026] "Communication methods" refer to internet connections and protocols used to transmit text data and other information to remote servers.

[1027] "Search methods" refer to technologies and algorithms that retrieve relevant information or answers from a database based on received text data.

[1028] "Generation means" refers to the technology or section that constructs and generates appropriate answers based on search results.

[1029] "Receiving means" refers to the technology or interface used to receive data or responses sent from a server on a terminal.

[1030] "Display and audio output means" refers to displays and speech synthesis technologies that visually display received responses to the user and also output them as audio.

[1031] "Speech recognition API" refers to an application programming interface for converting speech data into text data.

[1032] An "FAQ database" refers to a collection of information that stores frequently asked questions (FAQs) and their answers.

[1033] A "speech synthesis API" refers to an application programming interface for converting text data into speech data.

[1034] An "HTTP request" refers to a form of communication protocol in which a web browser or other client requests a specific action from a web server.

[1035] This invention relates to a system in which a field worker inputs voice data into a mobile device, which is then converted into text and sent to a server to automatically obtain an appropriate response. The system operates as follows:

[1036] Hardware and software to be used

[1037] The device used is a mobile device with a built-in microphone (e.g., smartphone, tablet). This device uses a speech recognition API to convert speech to text. Specifically, the Google Cloud Speech-to-Text API is used. The device also uses the Google Cloud Text-to-Speech API to convert text to speech and provide information to the user in audio format.

[1038] The server uses either cloud-based or on-premises servers. This server searches an FAQ database based on the received text data and generates answers based on the search results. An optimized database search algorithm is used as the search method.

[1039] System operation

[1040] The user voice-inputs a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device uses its built-in microphone to capture this voice. Then, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text. Specifically, this voice data is converted into the text "Please tell me how to install a fiber optic cable."

[1041] The converted text data is sent from the terminal to the server. HTTP requests over the internet are the commonly used means of communication. The server searches a pre-prepared FAQ database based on the received text data. For example, the database has answers registered in advance to the question "How to install a fiber optic cable?". The server searches for the relevant entry and generates an answer such as, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[1042] The generated response is sent from the server to the terminal. The terminal displays the received response to the user and also outputs it as audio using the Google Cloud Text-to-Speech API. This allows field workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing a fiber optic cable is: (1) Confirm the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," and the same content is played aloud through the speaker.

[1043] Specific example

[1044] For example, suppose a worker is installing fiber optic cables at a new site. The worker speaks into a terminal and says, "Please tell me how to install fiber optic cables." The terminal receives the audio and converts it into text data using the Google Cloud Speech-to-Text API. The converted text data is then sent to a server. The server searches its FAQ database for the relevant information, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer and also outputs it as audio using the Google Cloud Text-to-Speech API, providing the worker with an immediate response. This increases work efficiency.

[1045] This system can improve the work efficiency of on-site workers and significantly reduce inquiries to support desks. Furthermore, by utilizing speech recognition and speech synthesis technologies, the user experience can be enhanced.

[1046] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1047] Step 1:

[1048] The user inputs their questions by voice into their mobile device.

[1049] Specific action: The user speaks into their mobile device saying, "Please tell me how to install the fiber optic cable."

[1050] Input: User's voice data.

[1051] Output: The device receives the audio data.

[1052] Step 2:

[1053] The device uses its built-in microphone to capture audio data.

[1054] Specific operation: The device uses the microphone to collect the user's voice as digital audio data.

[1055] Input: User's voice data.

[1056] Output: Captured digital audio data.

[1057] Step 3:

[1058] The device uses a speech recognition API (e.g., Google Cloud Speech-to-Text API) to convert speech data into text data.

[1059] Specific operation: The device sends the captured audio data to a speech recognition API, which then converts the audio into text.

[1060] Input: Captured digital audio data.

[1061] Output: Text data saying "Please tell me how to install a fiber optic cable."

[1062] Step 4:

[1063] The terminal sends the converted text data to the server. HTTP requests via the internet are used as the means of communication.

[1064] Specific operation: The terminal includes the converted text data in the HTTP request and sends it to the server.

[1065] Input: Text data "Please tell me how to install a fiber optic cable."

[1066] Output: The server receives text data.

[1067] Step 5:

[1068] The server searches the FAQ database based on the received text data.

[1069] Specific operation: The server executes the search query "How to install a fiber optic cable" against the FAQ database and searches for relevant information.

[1070] Input: Text data "Please tell me how to install a fiber optic cable."

[1071] Output: Search results from the FAQ database.

[1072] Step 6:

[1073] The server generates an answer based on the search results.

[1074] Specific operation: The server analyzes the search results and constructs an appropriate response. For example, it might say, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[1075] Input: Search results from the FAQ database.

[1076] Output: The generated response.

[1077] Step 7:

[1078] The server sends the generated response to the terminal. Communication takes place via the internet.

[1079] Specific operation: The server sends the response message it has prepared to the terminal as an HTTP response.

[1080] Input: The generated response.

[1081] Output: The terminal receives the response message.

[1082] Step 8:

[1083] The device displays the received response to the user and also provides audio output.

[1084] Specific operation: The terminal displays the received response on the screen, converts it to speech using the Google Cloud Text-to-Speech API, and plays it through the speaker. For example, the display might show "The procedure for installing a fiber optic line is: (1) Check the installation location of the equipment, (2) Connect the cables, (3) Configure the network settings," and the same content would be played aloud.

[1085] Input: Received response message.

[1086] Output: The displayed response and the information played back as audio.

[1087] (Application Example 1)

[1088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1089] In field work, prompt and appropriate information provision is necessary to immediately address problems and questions faced by workers. However, conventional systems require workers to manually search for information, which reduces efficiency. Furthermore, the technology for receiving questions via voice is limited, and there is no provision of answers using speech synthesis. As a result, there is a lack of systems that can significantly improve work efficiency, which is a major challenge.

[1090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1091] In this invention, the server includes voice input means, conversion means for converting speech to text, communication means for transmitting text data, search means for searching text data, generation means for generating answers based on search results, receiving means for receiving the generated answers, display and audio output means for displaying and outputting the received answers, voice recognition means for receiving questions by voice, and voice synthesis means for synthesizing the generated answers into speech. This makes it possible for workers to input questions by voice and immediately receive appropriate answers in both voice and text.

[1092] "Voice input means" refers to devices or software for receiving voice data.

[1093] "Conversion methods for converting speech to text" refers to technologies and tools for converting speech data into text data.

[1094] "Communication methods for transmitting text data" refers to internet communication and other communication protocols used to send converted text data to a server.

[1095] "A search method for retrieving text data" refers to a technique or system for retrieving appropriate information from a database based on text data received on a server.

[1096] "Generative means for generating answers based on search results" refers to algorithms or systems for generating appropriate answers based on searched information.

[1097] "Receiving means for receiving generated responses" refers to equipment or software used to receive response data sent from a server.

[1098] "Display and audio output means for displaying and audio outputting received responses" refers to devices and software, including displays and speakers, for displaying received responses to the user and outputting them as audio.

[1099] "Voice recognition means for receiving questions via voice" refers to voice recognition APIs and other voice recognition technologies that recognize user voice input and convert it into text data.

[1100] "Speech synthesis means for synthesizing generated responses into speech" refers to speech synthesis technology or tools that convert text data into speech data and provide the user with the response in voice.

[1101] The present invention is a system for improving the efficiency of workers in a factory. This system includes a voice input means, a conversion means for converting voice to text, a communication means for transmitting text data, a search means for searching text data, a generation means for generating answers based on the search results, a receiving means for receiving the generated answers, a display and audio output means for displaying and outputting the received answers, a voice recognition means for receiving questions by voice, and a voice synthesis means for synthesizing the generated answers into speech.

[1102] First, the user voice-inputs their question into their smartphone. The smartphone uses its microphone to capture the voice data. Next, the smartphone uses its built-in speech recognition API to convert the voice data into text data. For example, if the user asks, "How do I fix error code 1234?", this voice will be converted into the text data "How do I fix error code 1234?".

[1103] The converted text data is sent to the server using a communication method. The server receives this text data and searches the FAQ database using a search method. Based on the search results, the server generates an appropriate answer. For example, it might generate an answer such as, "To fix error code 1234, first turn off the power and then restart. After that, reset the sensor."

[1104] The generated response is sent from the server to the smartphone. The smartphone displays the received response and also plays it back using speech synthesis. For example, the message "To fix error code 1234, first turn off the power and then restart. After that, reset the sensor" is displayed and played back.

[1105] This system includes speech recognition functionality using the Python `speech_recognition` library, communication functionality using the `requests` library, and speech synthesis functionality using gTTS (Google Text-to-Speech). This allows users to quickly ask questions and receive appropriate answers immediately.

[1106] Example of a prompt:

[1107] "How do I fix error code 1234?"

[1108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1109] Step 1:

[1110] The user inputs their question by voice into their smartphone. At this time, the device's microphone captures the voice data. This input data is in audio format and is used for subsequent processing.

[1111] Step 2:

[1112] The audio data is sent to the speech recognition system within the device, and the speech_recognition library is used to convert the audio data into text data. The data processing performed here involves recognizing phonemes based on spectral analysis of the audio waveform data and converting them into strings. The output is text data.

[1113] Step 3:

[1114] The converted text data is sent to the server via the internet using a communication method. An HTTP request is used for this transmission, and the character data is passed to the server as input.

[1115] Step 4:

[1116] The server queries the FAQ database using a search mechanism based on the received text data to find the relevant answer. Data processing involves searching for the appropriate entry from the database index and extracting its content. The output is the retrieved answer data.

[1117] Step 5:

[1118] The server uses a generation mechanism to generate answers based on the search results. This process formats the acquired database information according to a preset format. The generated answer data is output and sent back to the terminal via the communication mechanism.

[1119] Step 6:

[1120] The terminal receives the received response data via a receiving means and presents the response to the user using display and audio output means. Display is performed on a screen, and audio output is synthesized using the gTTS library. Specifically, text data is converted into an audio file, which is then played back through the speaker. The output consists of a visual text display and audio playback.

[1121] With all processing steps now complete, users can input questions by voice and receive instant answers in both text and voice.

[1122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1123] This invention provides a system that allows field workers to input voice data into a mobile device, convert the voice into text, and send that text data to a server to automatically obtain an appropriate response. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide responses and adjust search priorities based on the user's emotions.

[1124] Voice input and emotion recognition

[1125] The user speaks a question into their mobile device, for example, "Please tell me how to install a fiber optic cable." Simultaneously, the user's emotions are also recognized. The device has a microphone to receive voice and uses an emotion engine to recognize the user's emotions from their voice.

[1126] Speech-to-text conversion

[1127] The device uses a speech recognition API to convert the received audio data into text data. This conversion generates text data such as, "Please tell me how to install a fiber optic cable."

[1128] Emotion-based adjustment

[1129] The device uses emotion data recognized by the emotion engine to adjust search and response generation priorities. For example, if the user is feeling anxious or angry, the server prioritizes processing to provide a response quickly.

[1130] Sending text data

[1131] The converted text data and sentiment data are sent from the terminal to the server. HTTP requests via the internet are generally used as the means of communication.

[1132] Searching for questions and generating answers

[1133] Based on the received text data, the server searches a pre-prepared FAQ database. Based on the text data and sentiment data, the server generates the most appropriate answer. For example, the database has pre-registered answers to the question "How to install a fiber optic cable?", and when generating an answer such as "The procedure for installing a fiber optic cable is (1) to check the installation location of the equipment, (2) to connect the cables, and (3) to configure the network settings," the server adjusts the priority and tone of the answer based on sentiment data.

[1134] Submit your response

[1135] The generated response is sent from the server to the terminal. This is also done via communication means.

[1136] Display of answers and audio output

[1137] The terminal displays the received response to the user and also provides audio output. The display method and voice tone are adjusted based on sentiment data. This allows on-site workers to quickly obtain the necessary information. For example, the display might show "The procedure for installing a fiber optic cable is (1) confirm the equipment installation location, (2) connect the cables, and (3) configure the network settings," while simultaneously playing an audio message saying, "Stay calm and review the next steps. First, confirm the equipment installation location."

[1138] Specific example

[1139] Imagine a worker installing fiber optic cables at a new site. The worker speaks into a terminal and says, "Please tell me how to install fiber optic cables." The terminal receives the audio and uses an emotion engine to simultaneously recognize the worker's urgency and emotional state. It then uses a speech recognition API to convert the audio into text data and sends the converted text data and emotion data to a server. The server searches its FAQ database for relevant information, generates an appropriate answer based on the emotion data, and sends it to the terminal. The terminal displays the answer and simultaneously communicates it to the worker via audio output.

[1140] Thus, by combining emotion recognition, the present invention makes it possible to provide appropriate information tailored to the situation of on-site workers, improve work efficiency, and significantly reduce inquiries to support desks.

[1141] The following describes the processing flow.

[1142] Step 1:

[1143] The user speaks a question into their mobile device. They input a question by voice, such as "Please tell me how to install a fiber optic cable."

[1144] Step 2:

[1145] The device receives audio using its microphone. The user's voice data is acquired using a voice input method.

[1146] Step 3:

[1147] The device uses an emotion engine to recognize the user's emotions from the received audio data. For example, it can determine whether the user is anxious or anxious based on the tone and speed of their voice.

[1148] Step 4:

[1149] The device uses a speech recognition API to convert received audio data into text data. For example, it converts the audio "Please tell me how to install a fiber optic cable" into text data.

[1150] Step 5:

[1151] The terminal sends the converted text data and recognized emotion data to the server. This data is transmitted using communication methods such as HTTP requests.

[1152] Step 6:

[1153] The server searches the FAQ database based on the text data it receives. It finds the most relevant FAQ entries based on keywords and phrases.

[1154] Step 7:

[1155] The server generates appropriate answers based on the search results. For example, it provides specific steps such as, "The procedure for installing a fiber optic line is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings."

[1156] Step 8:

[1157] The server adjusts the tone and priority of the responses it provides based on sentiment data. For example, if the user is anxious, it might add reassuring phrases such as, "Please calm down and review the next steps."

[1158] Step 9:

[1159] The server sends the generated response to the terminal. The generated response data is sent to the terminal via a communication method.

[1160] Step 10:

[1161] The device displays the received response to the user. By displaying the response on the screen, it provides the user with information visually.

[1162] Step 11:

[1163] The device outputs the received response as audio. Using speech synthesis technology, the displayed response is played back as audio, providing the user with information aurally as well.

[1164] This series of steps allows field workers to quickly obtain the necessary information, improve work efficiency, and significantly reduce inquiries to support desks. Furthermore, incorporating emotion recognition enables the provision of more personalized support.

[1165] (Example 2)

[1166] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1167] There is a need for a means of providing information to enable on-site workers to respond quickly and accurately to problems they face, but conventional systems make it difficult to obtain appropriate answers that take into account the user's feelings. In particular, in emergencies or when users are feeling anxious or angry, there is a need for quick and appropriate responses that take the user's feelings into consideration.

[1168] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a voice input means, a conversion means for converting voice to text, an emotion recognition means for recognizing the user's emotions, a communication means for transmitting text data and emotion data, a search means for searching for questions based on the text data and emotion data, a generation means for generating answers based on search results and emotion data using a generation AI model, a receiving means for receiving the generated answers, and a display / audio output means for displaying and outputting the received answers. This enables field workers to provide information quickly and accurately in response to the user's emotions.

[1169] "Voice input means" refers to a device or function for acquiring voice data, such as a microphone.

[1170] "A means of converting speech to text" refers to a technology that converts speech data into text data, and speech recognition APIs are an example of this.

[1171] "Means of recognizing user emotions" refers to technologies that detect emotional states from a user's voice or text, and an emotion analysis engine is an example of this.

[1172] "Communication means for transmitting text data and sentiment data" refers to technologies that transmit converted text data and recognized sentiment data to a remote server, such as HTTP requests using the internet.

[1173] A "search method for searching for questions based on text data and sentiment data" is a technology that extracts relevant information from a database based on received data, and a search using a query database is an example of this.

[1174] "Generative means for generating answers based on search results and sentiment data using a generative AI model" refers to a technology that uses AI technology to create answers that take search results and sentiment data into consideration, and a generative AI model is an example of this.

[1175] "Means of receiving generated responses" refers to technologies for receiving generated response data from a remote server, such as receiving data via an HTTP request.

[1176] "A display and audio output means for displaying and outputting received responses" refers to a technology that displays received responses on a screen and plays them back as audio, such as a display and speakers.

[1177] This invention relates to a system in which a field worker inputs voice data into a mobile device, converts the voice into text data, sends that text data and the user's emotion data to a server, and automatically obtains an appropriate response. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it can provide responses and adjust search priorities based on the user's emotions.

[1178] The user voice-inputs a question into their mobile device. This question may include specific details such as, "Please tell me how to install a fiber optic cable." Simultaneously, the mobile device recognizes the user's emotions through their voice. The voice data is received using a microphone.

[1179] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. This conversion generates the text message, "Please tell me how to install the fiber optic cable." Simultaneously, the device uses an emotion engine (e.g., IBM Watson Tone Analyzer) to recognize the user's emotional state. Recognized emotions include impatience, anger, excitement, and calmness.

[1180] The converted text data and sentiment data are sent from the terminal to the server. An HTTP POST request over the internet is used for transmission. The server receives this data and first searches its FAQ database (e.g., query data stored in MySQL). In doing so, it uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response based on the text and sentiment data. The server generates a prompt and inputs it to the AI ​​model as follows:

[1181] Prompt: "The user is asking, 'Please tell me how to install the fiber optic cable,' and is showing signs of anxiety. As a response, explain the fiber optic cable installation procedure in a reassuring tone."

[1182] The AI ​​model generates an appropriate response based on this prompt. For example, it might generate a response that includes specific steps, such as, "The procedure for installing a fiber optic cable is: (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings." The tone and content are adjusted to take the user's emotions into consideration.

[1183] The generated response is sent back to the terminal from the server. The terminal displays the received response on its screen and also outputs it as audio using the Google Text-to-Speech API. For example, the display might show "The procedure for installing a fiber optic line is (1) to check the location of the equipment, (2) to connect the cables, and (3) to configure the network settings," and at the same time, it might play an audio message saying, "Stay calm and check the next steps. First, check the location of the equipment."

[1184] Thus, by combining speech recognition technology, a generative AI model, and emotion recognition technology, the present invention enables the rapid and appropriate provision of information tailored to the situation of on-site workers, improving work efficiency and significantly reducing inquiries to support desks.

[1185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1186] Step 1:

[1187] The user voice-inputs a question into their mobile device. For example, they might say, "Please tell me how to install a fiber optic cable." The device receives this voice using its microphone. The received voice data is the input and is processed in the next step.

[1188] Step 2:

[1189] The device uses an emotion recognition engine to recognize the user's emotions from the voice data. The emotion recognition engine (e.g., IBM Watson Tone Analyzer) receives the voice data and outputs emotion data. Specifically, it determines whether the user is exhibiting an emotional tone (such as impatience, anger, excitement, or calmness).

[1190] Step 3:

[1191] The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. In this process, audio data is input, and the text data "Please tell me how to install a fiber optic cable" is output.

[1192] Step 4:

[1193] The device collects the converted text data and recognized sentiment data and sends them to the server. HTTP POST requests over the internet are used for transmission. The input consists of text data and sentiment data, which are encoded in HTTP request format and sent to the server.

[1194] Step 5:

[1195] The server receives the HTTP request and extracts the sent text and sentiment data. This receiving operation prepares the server for the next processing. At this point, the input is text and sentiment data, and the output is the data after parsing is complete.

[1196] Step 6:

[1197] The server searches the FAQ database. Specifically, it sends a search query to the database based on the parsed text data and retrieves the appropriate information. The input to this search operation is a text query, and the output is the search result from the FAQ database.

[1198] Step 7:

[1199] The server activates a generative AI model and generates the best possible response based on search results and sentiment data. For example, the server might input the following prompt into the generative AI model:

[1200] "The user is asking, 'Please tell me how to install the fiber optic cable,' and is showing signs of impatience. As a countermeasure, please explain the fiber optic cable installation procedure in a reassuring tone."

[1201] The output of the generative AI model generates response text that takes the user's emotions into consideration.

[1202] Step 8:

[1203] The server sends the generated response text to the terminal. In this operation, the response text becomes the input data, which is then encoded in HTTP response format and sent to the terminal.

[1204] Step 9:

[1205] The device analyzes the received response text, displays it on the screen, and also outputs it as audio. Specifically, it uses the Google Text-to-Speech API to convert the text data into audio data and plays it back to the user. The input for this operation is the response text, and the output is display on the screen and audio playback.

[1206] This allows users to quickly and accurately obtain the information they need. For example, the display might show "The procedure for installing a fiber optic line is (1) confirm the location of the equipment, (2) connect the cables, and (3) configure the network settings," while simultaneously playing a voice message saying, "Stay calm and review the next steps. First, confirm the location of the equipment."

[1207] (Application Example 2)

[1208] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1209] To enable on-site workers to respond quickly and appropriately to machinery and equipment problems they encounter in industrial environments, it is necessary to obtain accurate information immediately. However, general search systems do not support voice input and have the problem of not being able to recognize the emotional state of workers, such as anxiety or stress, and prioritize responses accordingly. In addition, work sites are noisy, so there is a need for methods to obtain information without using one's hands.

[1210] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1211] In this invention, the server includes voice input means, conversion means for converting speech to text, emotion recognition means, and adjustment means for adjusting the priority and tone of responses based on emotion data. This makes it possible for field workers to input questions by voice, have their emotional state recognized, and then quickly receive the most appropriate response, thereby significantly improving work efficiency.

[1212] "Voice input means" refers to a device that receives the voice spoken by a user as digital data.

[1213] "Conversion means" refers to devices or software that convert received audio data into text data.

[1214] "Communication means" refers to network communication modules and protocols used to send text data to a server.

[1215] "Search method" refers to a device or software that searches for information based on text data received on the server side.

[1216] "Generation means" refers to devices or software that create appropriate answers based on retrieved information.

[1217] "Receiving means" refers to the device or software that receives the response sent from the server.

[1218] "Display and audio output means" refers to devices or software that present received responses to the user visually and audibly.

[1219] "Emotion recognition means" refers to devices or software that analyze voice data and other input data to identify the user's emotional state.

[1220] "Adjustment means" refers to devices or software that change the priority or tone of responses based on recognized emotional data.

[1221] This invention is a system that converts voice input from on-site workers or users into text and provides appropriate responses. Specific embodiments for carrying out this invention are described below.

[1222] System Configuration

[1223] Voice input method

[1224] Users use the microphone built into the smart glasses to input voice information. This voice input method allows workers to input questions about machine and equipment problems using voice.

[1225] Conversion means

[1226] The received audio data is converted into text data using the speech_recognition library. High-precision speech recognition is achieved by using the speech recognition API.

[1227] means of communication

[1228] The converted text and sentiment data are sent to a server via the smart glasses' network communication module (e.g., Wi-Fi or 4G communication). This communication is performed using HTTP requests.

[1229] emotion recognition means

[1230] The `emotion_recognition` module is used to analyze emotions from voice data. This allows us to identify emotional states from the tone and speed of the user's voice and assess stress levels and urgency.

[1231] Search methods

[1232] On the server side, the system searches the FAQ database based on the received text data. This allows for the rapid extraction of the information the user is looking for. This search method provides appropriate solutions to problems with machinery and equipment.

[1233] Adjustment means

[1234] Based on emotional data, the system adjusts the priority and tone of the generated responses. This adjustment ensures that responses are delivered in a tone that encourages a quick and calm response when the user is experiencing high levels of stress.

[1235] Generation means, receiving means, display / sound output means

[1236] The response generated on the server is sent back to the smart glasses via a communication method. The response is displayed on the smart glasses' screen and the user is guided by voice output.

[1237] Specific example

[1238] If a factory worker determines that a machine is malfunctioning, they might ask their smart glasses, "What could be causing this machine to not work?" The voice data is instantly converted into text, and an emotion recognition engine determines from their voice that it is an emergency. This data is sent to a server, which searches its FAQ database for the appropriate answer. The server generates a response such as, "It's okay. First, check if the machine is powered on, and then check if the cables are properly connected," adjusting the tone based on the emotion data. The result is then displayed on the smart glasses and guided by voice.

[1239] Prompt example

[1240] Create pseudocode for a system that uses speech recognition to acquire questions and recognize emotion data, providing appropriate answers to improve work efficiency. The system uses smart glasses for voice input, emotion recognition, server communication, and answer display. Emotion recognition is performed from the voice data to determine stress levels and urgency. The results are displayed on the smart glasses' screen and also provided via voice guidance. Please describe the system in detail using pseudocode.

[1241] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1242] Step 1:

[1243] The user performs voice input.

[1244] The user inputs a question by voice into the smart glasses. For example, they might say, "What is causing this machine not to work?" This voice data is then transmitted through the microphone and converted into digital audio data.

[1245] Input: User's voice

[1246] Output: Digital audio data

[1247] Step 2:

[1248] The device converts speech to text.

[1249] The device (smart glasses) converts digital audio data into text data using the speech_recognition library. In this process, the speech recognition API extracts characters from the audio waveform and converts them into text format.

[1250] Input: Digital audio data

[1251] Output: Text data

[1252] Step 3:

[1253] The device recognizes emotions

[1254] The device uses the emotion_recognition module to identify the user's emotions from voice data. For example, it assesses stress levels and urgency based on the user's tone of voice and speaking speed.

[1255] Input: Digital audio data

[1256] Output: Sentiment data

[1257] Step 4:

[1258] The device sends text data and sentiment data to the server.

[1259] The device sends the converted text data and sentiment data to the server via a communication module (e.g., Wi-Fi, 4G). This transmission is typically done using HTTP requests.

[1260] Input: Text data, sentiment data

[1261] Output: HTTP request to the server

[1262] Step 5:

[1263] The server performs a search based on text data.

[1264] The server searches the FAQ database based on the received text data. In this process, it quickly extracts information related to the entered question.

[1265] Input: Text data

[1266] Output: Search results data

[1267] Step 6:

[1268] The server generates and adjusts the response.

[1269] The server adjusts the priority and tone of generated responses based on emotional data. For example, if the user is feeling anxious, it will immediately provide a response in a quick and calm tone.

[1270] Input: Search results data, sentiment data

[1271] Output: Adjusted response data

[1272] Step 7:

[1273] The server sends the adjusted response data to the terminal.

[1274] The server sends the adjusted response data to the terminal. This transmission is again performed using an HTTP request.

[1275] Input: Adjusted response data

[1276] Output: HTTP response to the terminal

[1277] Step 8:

[1278] The device displays and outputs the answer aloud.

[1279] The device displays the received response on its screen and also provides audio output. This allows users to quickly obtain the information they need, both visually and aurally. Additional voice input is also possible if the user requires further guidance or assistance.

[1280] Input: Adjusted response data

[1281] Output: Display, audio playback

[1282] Through the steps described above, the present invention significantly improves the work efficiency of on-site workers and enables quick and appropriate responses.

[1283] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1284] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1285] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1286] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1287] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1288] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1289] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1290] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1291] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1292] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1293] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1294] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1295] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1296] 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.

[1297] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1298] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1299] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1300] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1301] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1302] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1303] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1304] The following is further disclosed regarding the embodiments described above.

[1305] (Claim 1)

[1306] Voice input method,

[1307] A means of converting speech into text,

[1308] A means of communication for transmitting character data,

[1309] Search methods for searching text data,

[1310] A generation means for generating answers based on search results,

[1311] A receiving means for receiving the generated response,

[1312] A display and audio output means for displaying and outputting the received response,

[1313] A system that includes this.

[1314] (Claim 2)

[1315] The system according to claim 1, wherein the conversion means for converting speech to text is a means for using a speech recognition API.

[1316] (Claim 3)

[1317] The system according to claim 1, wherein the search means is a means of searching for answers based on questions using an FAQ database.

[1318] "Example 1"

[1319] (Claim 1)

[1320] Voice input method,

[1321] A means of converting speech into text,

[1322] A means of communication for transmitting character data,

[1323] Search methods for searching text data,

[1324] A generation means for generating answers based on search results,

[1325] A receiving means for receiving the generated response,

[1326] A display and audio output means for displaying and outputting the received response,

[1327] A system that includes this.

[1328] (Claim 2)

[1329] The system according to claim 1, wherein the conversion means for converting speech to text is a means for using a speech recognition API.

[1330] (Claim 3)

[1331] The system according to claim 1, wherein the search means is a means of searching for answers based on questions using an FAQ database.

[1332] (Claim 4)

[1333] The system according to claim 1, which uses a speech synthesis API to output the received response as audio.

[1334] (Claim 5)

[1335] The system according to claim 1, wherein the means of communication is a means of using HTTP requests via the Internet.

[1336] "Application Example 1"

[1337] (Claim 1)

[1338] Voice input method,

[1339] A means of converting speech into text,

[1340] A means of communication for transmitting character data,

[1341] Search methods for searching text data,

[1342] A generation means for generating answers based on search results,

[1343] A receiving means for receiving the generated response,

[1344] A display and audio output means for displaying and outputting the received response,

[1345] A voice recognition means for receiving questions via voice,

[1346] A speech synthesis means for synthesizing the generated response into speech,

[1347] A system that includes this.

[1348] (Claim 2)

[1349] The system according to claim 1, wherein the conversion means for converting speech to text is a means for using a speech recognition API.

[1350] (Claim 3)

[1351] The system according to claim 1, wherein the search means is a means of searching for answers based on questions using an FAQ database.

[1352] (Claim 4)

[1353] The system according to claim 1, further comprising a speech synthesis means for synthesizing the generated response into speech and outputting it as speech.

[1354] That's all.

[1355] "Example 2 of combining an emotion engine"

[1356] (Claim 1)

[1357] Voice input method,

[1358] A means of converting speech into text,

[1359] A means of recognizing the user's emotions,

[1360] A means of transmitting text data and emotional data,

[1361] A search method for retrieving questions based on text data and sentiment data,

[1362] A generation method that generates answers based on search results and sentiment data using a generative AI model,

[1363] A receiving means for receiving the generated response,

[1364] A display and audio output means for displaying and outputting the received response,

[1365] A system that includes this.

[1366] (Claim 2)

[1367] The system according to claim 1, wherein the conversion means for converting speech to text is a means for using a speech recognition API.

[1368] (Claim 3)

[1369] The system according to claim 1, wherein the search means is a means of searching for answers based on a question using a query database.

[1370] "Application example 2 of combining emotional engines"

[1371] (Claim 1)

[1372] Voice input method,

[1373] A means of converting speech into text,

[1374] A means of communication for transmitting character data,

[1375] Search methods for searching text data,

[1376] A generation means for generating answers based on search results,

[1377] A receiving means for receiving the generated response,

[1378] A display and audio output means for displaying and outputting the received response,

[1379] A means of recognizing emotions,

[1380] A means of adjusting the priority and tone of responses based on emotional data,

[1381] A system that includes this.

[1382] (Claim 2)

[1383] The system according to claim 1, wherein the conversion means for converting speech to text is a means for using a speech recognition API.

[1384] (Claim 3)

[1385] The system according to claim 1, wherein the search means is a means of searching for answers based on questions using an FAQ database. [Explanation of Symbols]

[1386] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Voice input method, A means of converting speech into text, A means of communication for transmitting character data, Search methods for searching text data, A generation means for generating answers based on search results, A receiving means for receiving the generated response, A display and audio output means for displaying and outputting the received response, A system that includes this.

2. The system according to claim 1, wherein the conversion means for converting speech to text is a means for using a speech recognition API.

3. The system according to claim 1, wherein the search means is a means of searching for answers based on questions using an FAQ database.

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