System

Abstract

A system is provided.SOLUTION: A system, comprising: means for obtaining speech input from a user; means for converting the speech input to text; means for parsing the text and generating an appropriate solution; and means for presenting the solution to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] As mobile phones have become more widespread, their functions and settings have become more complex, leaving many users confused about how to set up and operate them. It is particularly difficult for users with limited technical knowledge or those using a particular function for the first time to receive appropriate support. Therefore, there is a demand for a support system that makes it easy to set up and operate mobile phones. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for acquiring voice input from a user; means for converting the voice input into text; means for analyzing the text and generating appropriate solutions; and means for presenting the solutions to the user. Specifically, the system acquires voice using a microphone, converts the voice input into text using speech recognition technology, analyzes the text using natural language processing technology, generates solutions to the user's problems, and displays the solutions to the user, thereby supporting the setup and operation of a mobile phone.

[0006] "User" refers to an individual who uses a mobile phone and operates or changes its functions and settings.

[0007] "Voice input" refers to information or commands that a user communicates to a system using their voice.

[0008] "Text" refers to documents or information that is a digital representation of voice input converted into written data.

[0009] "Microphone" refers to a device that collects a user's voice and converts it into digital data.

[0010] "Voice recognition technology" refers to technology that analyzes voice data and converts it into a corresponding string of characters.

[0011] "Natural language processing technology" refers to technology that enables computers to understand and process human language, and is used to analyze and generate text.

[0012] "Solution" refers to specific methods or instructions for dealing with a user's problem or request.

[0013] "Display" refers to the way in which solutions or information are visually presented to the user. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] Implementing this invention involves four main steps: a user inputting voice using a mobile phone, a terminal converting the voice into text, a server analyzing the text and generating a solution, and finally a server presenting the solution to the user.

[0036] First, the user speaks into the microphone on their mobile phone to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi."

[0037] The device then converts this voice input into text using speech recognition technology, converting the user's voice into text data such as "Please tell me how to set up Wi-Fi."

[0038] The converted text is sent to a server, which uses natural language processing techniques to analyze it and generate the specific solution the user is looking for. For example, the server might generate instructions like, "Open Settings and select the Wi-Fi option."

[0039] Finally, the terminal receives the solution from the server and presents it to the user, who can then configure the mobile phone according to the displayed solution.

[0040] For example, if a user speaks, "How do I set up Bluetooth?", the device converts this speech into text. The text is sent to a server, which then generates instructions such as, "Open the settings screen, select the Bluetooth option, and turn it on." The user can then follow the instructions to set up Bluetooth.

[0041] This system allows even users with little technical knowledge to easily and efficiently configure their mobile phones. By linking the server and the device, the system quickly provides users with an appropriate solution, reducing their anxiety about operating their mobile phones.

[0042] The processing flow will be explained below.

[0043] Step 1:

[0044] The user speaks into the microphone of the mobile phone. For example, the user says, "Please tell me how to set up Wi-Fi."

[0045] Step 2:

[0046] The device captures voice data through a microphone. This voice data is a direct recording of the user's speech.

[0047] Step 3:

[0048] The device uses voice recognition technology to convert voice data into text data. At this time, the voice input "Please tell me how to set up Wi-Fi" is converted into text format.

[0049] Step 4:

[0050] The terminal transmits the converted text data to a server via the Internet, where the text data is further analyzed.

[0051] Step 5:

[0052] The server uses natural language processing technology to understand the content of the text data and identify the user's requests and problems. For example, from the text "Please tell me how to set up Wi-Fi," it can analyze that the user is looking for information about Wi-Fi settings.

[0053] Step 6:

[0054] Based on the analysis, the server generates the optimal solution for the user's request, such as "Open the settings screen and select the Wi-Fi option."

[0055] Step 7:

[0056] The server sends the generated solution to the terminal as text data, and the terminal receives the solution from the server.

[0057] Step 8:

[0058] The device visually displays the received text data to the user. Specifically, the device displays the message "Open Settings and select the Wi-Fi option."

[0059] Step 9:

[0060] The user follows the instructions displayed on the device screen to set up and operate the mobile phone. For example, the user can set up Wi-Fi by opening the settings screen and selecting the Wi-Fi option.

[0061] These steps allow the user to receive appropriate support for setting up and operating the mobile phone through voice input.

[0062] Example 1

[0063] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0064] Existing systems have a problem in that it is difficult to provide a quick and accurate solution when a user asks about a technical problem or setting method. In particular, it is difficult to properly understand the user's request entered by voice and provide a specific solution based on that. As a result, users who lack technical knowledge often find it time-consuming to set up or solve a problem, which can be frustrating.

[0065] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0066] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for converting the text into prompt sentences for solution generation, means for generating solutions from the prompt sentences using a generative AI model, and means for presenting the solutions to the user. This makes it possible to generate prompt sentences from text and generate accurate solutions using the generative AI model, and quickly present them to the user.

[0067] The "means for obtaining voice input from a user" refers to a device or method for obtaining information input by voice from a user.

[0068] "Means for converting voice input into text" refers to technology or devices for converting acquired voice data into character data.

[0069] The "means for analyzing text and generating appropriate solutions" is a device or method that analyzes the converted text and creates appropriate solutions for the user based on the results of the analysis.

[0070] The "means for presenting a solution to a user" refers to a device or method for presenting the generated solution to a user in an easy-to-understand manner, and includes screen display, audio output, and the like.

[0071] "Means for converting text into prompts for solution generation" refers to technology or devices for converting analyzed text data into prompts in a format that is easy for the generative AI model to understand.

[0072] "Means for generating solutions from prompt sentences using a generative AI model" refers to technology or devices that utilize a generative AI model based on prompt sentences to generate specific solutions.

[0073] To put this invention into practice, a system is constructed in which a user inputs voice using a mobile information terminal, and appropriate solutions are presented based on that input. The specific processing flow of the entire system and details of the hardware and software used are described below.

[0074] First, the user speaks into the microphone on the mobile information terminal to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi." This voice input is captured by the terminal via the microphone, which serves as a voice acquisition means.

[0075] The device then converts the user's voice into text using speech recognition technology, using voice recognition software such as the Google Cloud Speech-to-Text API, which converts the speech into text data such as "How do I set up Wi-Fi?"

[0076] The converted text data is sent from the device to a server via the Internet. The server converts the received text into a prompt for solution generation, and generates a solution using a generative AI model (e.g., OpenAI's GPT-3) based on the prompt. The prompt is converted to the format "The user is looking for information on how to set up Wi-Fi."

[0077] The solution generated using the generative AI model is sent back from the server to the device, which receives the solution and presents it to the user visually or audibly. For example, a specific solution such as "Open the Settings screen and select the Wi-Fi option" may be displayed on the device screen.

[0078] (Example)

[0079] For example, consider the case where a user speaks, "How do I set up Bluetooth?" The user's voice input is captured through the microphone of the mobile information device. The device converts this speech into text data, "How do I set up Bluetooth?", using the Google Cloud Speech-to-Text API. This text data is sent to the server, which analyzes the text and generates a specific solution, "Open the settings screen, select the Bluetooth option, and turn it on," based on a generative AI model. This solution is sent to the device and displayed to the user.

[0080] This system allows even users with little technical knowledge to easily and efficiently configure their mobile devices. The collaboration between the server and the device quickly provides users with an appropriate solution, reducing anxiety about operating the mobile device.

[0081] Example prompts to be input to the generative AI model:

[0082] Input: How do I set up Bluetooth?

[0083] Model output: Open the settings screen, select the Bluetooth option and turn it on

[0084] Thus, by implementing the present invention, a system can be realized that quickly generates and provides appropriate solutions based on voice input from a user.

[0085] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0086] Step 1:

[0087] The user speaks into the microphone of the mobile information device to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi." This voice input is captured as data to be processed in the next step.

[0088] Step 2:

[0089] The device receives voice data from the user and converts it into text data using speech recognition technology such as the Google Cloud Speech-to-Text API. The voice input "Please tell me how to set up Wi-Fi" is converted into text data "Please tell me how to set up Wi-Fi." This converted text data becomes the input for the next step.

[0090] Step 3:

[0091] The terminal transmits the converted text data to the server, where the terminal uses a secure communication protocol to ensure the security of the data while sending the text data to the server.

[0092] Step 4:

[0093] The server analyzes the received text data and converts it into a prompt for generating a solution. For example, the received text "Please tell me how to set up Wi-Fi" is converted into a prompt "The user is looking for instructions on how to set up Wi-Fi." This prompt becomes the input data for the next step.

[0094] Step 5:

[0095] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate solution from the prompt. The generative AI model inputs the prompt and generates a specific solution: "Open the Settings screen and select the Wi-Fi option." This solution becomes the output data for the next step.

[0096] Step 6:

[0097] The server sends the generated solution to the device, again using a secure communication protocol to ensure data safety. The solution arrives at the device.

[0098] Step 7:

[0099] The device will then present the received solution to the user visually or audibly, for example, by displaying a message on the device screen saying, "Open the settings screen and select the Wi-Fi option." The user can then follow the instructions to proceed with the setup.

[0100] The above is the specific processing flow of the program of this system.

[0101] (Application example 1)

[0102] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0103] Inventory management in logistics centers is a time-consuming and labor-intensive task, and efficient inventory management is required, especially when handling large quantities of items. However, it is difficult for many employees to manage inventory accurately and quickly. Furthermore, using a system that requires complex operations and knowledge can easily lead to mistakes and waste of time. This invention aims to solve these problems by providing a simple and efficient inventory management system that uses voice input.

[0104] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0105] In this invention, the server includes means for acquiring voice input, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions by voice, and means for being installed in the logistics robot to support inventory management, thereby enabling employees to easily manage inventory by voice and achieving efficient and accurate work.

[0106] "User" refers to a person who uses the system to provide voice input and obtain a solution.

[0107] "Voice input" refers to information that a user provides to the system by speaking through a microphone.

[0108] "Text" refers to character data converted from voice input using voice recognition technology.

[0109] "Analysis" is the process performed to understand text data and derive appropriate solutions.

[0110] "Solutions" refer to instructions for solving problems or operating procedures that are generated as a result of analyzing the user's voice input.

[0111] "Presenting" is the act of presenting the generated solution to the user in a visible or audible form.

[0112] "Audio presentation" refers to verbally communicating the generated solution to the user using a synthesized voice or the like.

[0113] A "logistics robot" is an automated mechanical device used to assist with inventory management, product movement, and other tasks within a logistics center.

[0114] "Installation" is the act of placing a system or software on specific hardware and making it operational.

[0115] "Inventory management" refers to the general task of monitoring and appropriately managing inventory quantities and conditions.

[0116] "Server" refers to the computer system that processes voice input from the user and performs text analysis and solution generation.

[0117] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0118] A "microphone" is a device that converts sound into an electrical signal and inputs it into a system.

[0119] This invention relates to a system that supports efficient inventory management in logistics centers. Its main components include a microphone for capturing voice input, speech recognition software for converting speech into text, natural language processing technology for analyzing text and generating solutions, speech synthesis technology for presenting solutions to users, and a logistics robot. Each component is described in detail below.

[0120] 1. Voice input

[0121] A user speaks into a microphone to describe the inventory problem or request they want to solve. This speech data is captured by speech recognition software. For example, a user might say, "Check inventory and confirm the quantity of new product."

[0122] 2. Voice Recognition

[0123] Using speech recognition software (e.g., the "speech_recognition" library) installed on the device, the user's speech is converted into text data, which generates the text information "Check inventory to see the quantity of new products."

[0124] 3. Natural Language Processing

[0125] This text data is sent to a server and analyzed by a server-side natural language processing engine (for example, Google Natural Language API). This analysis allows the system to accurately understand the user's intentions and requests and generate appropriate solutions.

[0126] 4. Solution Generation

[0127] The server generates a specific solution based on the analysis results, for example, the server generates an instruction such as "Check the inventory list and display the quantity of the new product."

[0128] 5. Providing a solution

[0129] The generated solution is converted into voice using speech synthesis technology (e.g., the "pyttsx3" library) and presented to the user via the logistics robot, allowing the user to receive the solution by voice.

[0130] 6. Logistics robots

[0131] The logistics robot assists with inventory management by performing stock checks and quantity confirmations according to generated instructions and reporting the results to the user.

[0132] Specific examples

[0133] For example, if a logistics center staff member voice-inputs, "Check inventory and confirm new product quantities," the voice is captured by a microphone and converted into text via speech recognition software. The text data is sent to a server and analyzed by a natural language processing engine. The server generates a solution, "Check inventory list and display new product quantities," and presents it to the user using speech synthesis technology. This series of steps enables fast and efficient inventory management.

[0134] Prompt Sentence Examples

[0135] Example user input: "Check inventory to see new product quantities."

[0136] Example server response: "Check inventory list and display new product quantities."

[0137] In this way, the present invention can improve the efficiency of inventory management in a logistics center through voice input, thereby improving work accuracy and reducing labor.

[0138] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0139] Step 1:

[0140] The user inputs instructions by voice.

[0141] Input: User's spoken input (e.g., "Check inventory to see new product quantities")

[0142] Output: Audio data

[0143] Specific operation: The user verbally inputs instructions regarding inventory management to the logistics robot through a microphone.

[0144] Step 2:

[0145] The device converts the voice input into text.

[0146] Input: Audio data

[0147] Output: Text data (e.g., "Check inventory to see new product quantities")

[0148] Specific operation: Using speech recognition software (the "speech_recognition" library), the voice data is analyzed and converted into appropriate text data.

[0149] Step 3:

[0150] The terminal transmits the text data to the server.

[0151] Input: Text data

[0152] Output: Send text data to the server

[0153] Specific operation: The terminal sends the converted text data to the server using a protocol such as an HTTP request.

[0154] Step 4:

[0155] The server analyzes the text data and generates solutions.

[0156] Input: Text data (e.g., "Check inventory to see new product quantities")

[0157] Output: Solution (e.g. "Check the inventory list and display the quantity of the new product")

[0158] Specific operation: The server uses a natural language processing engine (such as the Google Natural Language API) to analyze the text data, understand the user's intent, and generate an appropriate solution.

[0159] Step 5:

[0160] The server sends the solution to the device.

[0161] Input: Solution

[0162] Output: Solution sent to terminal

[0163] Specific operation: The server returns the generated solution to the terminal as text data.

[0164] Step 6:

[0165] The device will vocalize the solution and present it to the user.

[0166] Input: Solution (e.g. "Check the inventory list and display the new product quantity")

[0167] Output: A spoken solution

[0168] Specific operation: Using the speech synthesis technology ("pyttsx3" library) installed on the device, the solution is output aloud and communicated to the user.

[0169] Step 7:

[0170] Logistics robots perform inventory management tasks.

[0171] Input: Instructions for implementing the solution

[0172] Output: Stock check result

[0173] Specific operation: The logistics robot performs actual inventory checks according to instructions and reports the results to the user.

[0174] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0175] The present invention is a system including means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions to the user, and an emotion engine for acquiring the user's emotions.

[0176] The user speaks into the microphone of their mobile phone, saying something like, "Please tell me how to set up Wi-Fi." This voice input is picked up by the device's microphone. The device then converts the acquired voice data into text data using voice recognition technology. During this conversion process, voice recognition technology is used to convert the voice into character data, making it possible to identify the user's request as text.

[0177] The converted text data is sent to a server via the Internet. The server then analyzes the text data using natural language processing technology. Specifically, for example, from the text "Please tell me how to set up Wi-Fi," the server identifies that the user is looking for information about Wi-Fi settings.

[0178] The server also includes an emotion engine that identifies the user's emotions. The emotion engine analyzes elements of the voice data, such as tone, speed, and pauses, to recognize the user's emotional state (e.g., irritation, confusion, excitement, etc.). This emotional information is used to devise solutions.

[0179] The server reflects the user's emotional information acquired by the emotion engine and generates a more appropriate solution. For example, if the user is confused, it generates detailed, step-by-step instructions. Specifically, if the emotion engine identifies the user's confusion, the server can create more friendly and detailed instructions, such as "First, open the settings screen, and then select the Wi-Fi option."

[0180] The generated solution is sent to the device as text data again. The device receives this text data and displays the solution to the user. The user configures the Wi-Fi by following the solution displayed on the device screen.

[0181] For example, if a user says, "How do I set up Bluetooth?", the device converts this speech into text. The text is sent to the server, which generates instructions such as, "Open the Settings screen, select the Bluetooth option, and turn it on." If the emotion engine detects the user's confusion, it generates additional detailed instructions, such as, "You can find the Settings screen by swiping right from the home screen," and sends these to the device for display.

[0182] This system allows users to receive appropriate support for setting up and operating their mobile phones through voice input, and also provides detailed support according to their emotional state. As a result, users will be able to reduce their anxiety about operating their mobile phones and set them up more smoothly.

[0183] The processing flow will be explained below.

[0184] Step 1:

[0185] The user speaks into the microphone of the mobile phone. For example, the user says, "Please tell me how to set up Wi-Fi."

[0186] Step 2:

[0187] The device uses a microphone to capture the user's voice data, which is a recording of the user's speech.

[0188] Step 3:

[0189] The device uses voice recognition technology to convert the acquired voice data into text data, resulting in the voice being converted into text saying, "Please tell me how to set up Wi-Fi."

[0190] Step 4:

[0191] The terminal transmits the converted text data to a server via the Internet, where the text data is further analyzed.

[0192] Step 5:

[0193] The server uses natural language processing technology to analyze the content of the text data. For example, from the text "Please tell me how to set up Wi-Fi," it understands that the user is looking for information about Wi-Fi settings.

[0194] Step 6:

[0195] At the same time, the emotion engine installed on the server analyzes the voice data and recognizes the user's emotions, such as tone of voice, speed, and pauses, to determine whether the user is confused or annoyed.

[0196] Step 7:

[0197] The server generates the optimal solution based on the analysis results and emotional information according to the user's request. For example, if the user is confused, it generates detailed instructions such as "Open the settings screen and select the Wi-Fi option."

[0198] Step 8:

[0199] The server sends the generated solution to the terminal as text data, and the terminal receives the solution from the server.

[0200] Step 9:

[0201] The device visually displays the received text data to the user, specifically displaying the message "Open Settings and select the Wi-Fi option" on the device screen.

[0202] Step 10:

[0203] The user follows the instructions displayed on the device screen to set up and operate the mobile phone. For example, the user can set up Wi-Fi by opening the settings screen and selecting the Wi-Fi option.

[0204] Through this series of steps, users can receive appropriate support for setting up and operating their mobile phone through voice input. Furthermore, the introduction of an emotion engine provides detailed support according to the user's emotional state, reducing stress and anxiety when operating the device.

[0205] Example 2

[0206] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0207] Conventional technologies have had difficulty providing appropriate solutions, particularly in response to the user's emotional state, when users obtain information using voice input. This often leaves users feeling confused and anxious. Furthermore, when users need detailed guidance, they are unable to obtain appropriate and prompt assistance.

[0208] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0209] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating an appropriate solution, means for presenting the solution to the user, means for identifying the user's emotion, and means for adjusting the solution based on the result of identifying the emotion. This makes it possible to provide an appropriate solution according to the user's emotional state based on the information acquired through the voice input, reduce the user's confusion and anxiety, and provide quick and detailed guidance.

[0210] "Voice input" is data that electronically captures the words and sounds spoken by a user.

[0211] "Text conversion means" refers to technology or devices for converting acquired voice data into text data.

[0212] "Means for generating appropriate solutions" refers to technologies and devices that provide the information and instructions desired by the user based on the analyzed text data.

[0213] "Presentation means" refers to the techniques or devices used to visually or audibly communicate the generated solution to the user.

[0214] An "emotion identification means" is a technique or device that identifies a user's emotional state (e.g., confusion, excitement, irritation, etc.) from the user's voice data or other input.

[0215] The "means for adjusting a solution" refers to a technique or device for changing the content and presentation method of the solution provided according to the identified emotional state of the user.

[0216] The present invention is a system that receives voice input from a user, converts the voice into text, analyzes the text, and presents appropriate solutions. It also has the ability to identify the user's emotions and adjust solutions based on the emotional information. Detailed embodiments are described below.

[0217] Acquiring voice input

[0218] The user speaks into the device's microphone, for example, "Please tell me how to set up Wi-Fi." The device uses a high-precision microphone to capture this voice, which is then captured as digital data.

[0219] Speech recognition and text conversion

[0220] The device uses voice recognition technology to convert the captured voice data into text data. Specifically, it uses voice recognition software such as the Google Speech-to-Text API to convert the user's voice into a string of characters. For example, the voice saying "Please tell me how to set up Wi-Fi" is converted into text "Please tell me how to set up Wi-Fi."

[0221] Sending text data to the server

[0222] The converted text data is then sent to a server via the Internet. The device uses the HTTPS protocol to transfer the text data to the server securely and quickly.

[0223] Text data analysis

[0224] The server uses natural language processing technology to analyze the text data it receives. For example, a generative AI model such as OpenAI GPT-4 is used. The server analyzes the text data, understands the user's request, and determines what the user specifically wants from the text "Please tell me how to set up Wi-Fi."

[0225] Identifying emotions

[0226] The server is equipped with an emotion engine that analyzes parameters such as tone, speed, and pauses in the voice data to identify the user's emotional state. For example, it analyzes whether the user is confused or irritated and recognizes that emotional state.

[0227] Solution generation and coordination

[0228] The server takes into account the emotional information obtained by the emotion engine and generates an appropriate solution. The generated solution is provided to the user in an easy-to-understand manner using natural language generation technology. If the user is confused, a detailed guide is generated. For example, step-by-step instructions are provided, such as "First, open the settings screen, then select the Wi-Fi option."

[0229] Submitting and Viewing Solutions

[0230] The generated solution is sent to the device as text data, and the device displays the solution to the user visually or audibly. The user can then configure the Wi-Fi by following the instructions displayed on the device screen.

[0231] Examples of concrete examples and prompts

[0232] As a concrete example, consider the case where a user speaks, "How do I set up Bluetooth?" The device converts this speech into text and sends it to the server. The server generates instructions such as, "Open the Settings screen, select the Bluetooth option, and turn it on." If the emotion engine detects the user's confusion, it will generate additional detailed instructions, such as, "You can find the Settings screen by swiping right from the home screen," and send these to the device for display.

[0233] Prompt Sentence Examples

[0234] User Input: "How do I set up Bluetooth?"

[0235] User's emotional state: "Confused"

[0236] Goal: Provide users with clear, detailed instructions on how to set up Bluetooth.

[0237] output:

[0238] Open the Settings screen, select the Bluetooth option, and switch it on. You can find the Settings screen by swiping right from the home screen.

[0239] This system allows users to receive appropriate support for setting up and operating their mobile devices through voice input. Furthermore, by providing detailed support according to the user's emotional state, it is possible to reduce the user's anxiety and achieve smoother operation.

[0240] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0241] Step 1:

[0242] The user speaks into the device's microphone, saying something like, "Please tell me how to set up Wi-Fi."

[0243] Input: User voice input

[0244] Output: Audio data

[0245] What it does: The device's microphone picks up the audio and stores it as digital audio data.

[0246] Step 2:

[0247] The device uses voice recognition technology to convert voice data into text data, specifically using the Google Speech-to-Text API.

[0248] Input: Audio data

[0249] Output: Text data

[0250] Specific operation: Call the speech recognition API and send the speech data to the API. Retrieve the text data returned from the API. The speech "Please tell me how to set up Wi-Fi" is converted into text "Please tell me how to set up Wi-Fi."

[0251] Step 3:

[0252] The terminal transmits the converted text data to a server via the Internet.

[0253] Input: Text data

[0254] Output: HTTP request to the server

[0255] Specific operation: Generates an HTTPS request and securely sends the converted text data to the server.

[0256] Step 4:

[0257] The server analyzes the received text data using natural language processing technology (e.g., OpenAI GPT-4).

[0258] Input: Text data

[0259] Output: Data that recognizes the user's request

[0260] Specific operation: The natural language processing engine is started and the text is analyzed. As a result, the specific request "Set up Wi-Fi" is recognized.

[0261] Step 5:

[0262] The server analyzes the tone, speed, and timing of the voice data using an emotion engine to identify the user's emotional state.

[0263] Input: Audio parameters

[0264] Output: Emotional state (e.g., confused, annoyed, etc.)

[0265] What it does: It uses an emotion engine to analyze multiple parameters of the voice data to identify the user's emotional state.

[0266] Step 6:

[0267] The server uses a generative AI model to generate appropriate solutions based on the text and emotional state.

[0268] Input: User's request, emotional state

[0269] Output: Solution text

[0270] What it does: It uses a natural language generation engine to generate solutions based on the user's request and emotional state. It provides detailed, step-by-step instructions for confused users. For example, "First, open Settings, then select the Wi-Fi option."

[0271] Step 7:

[0272] The server transmits the generated solution to the terminal again as text data.

[0273] Input: Solution text

[0274] Output: HTTP response to the device

[0275] Specific behavior: Generate an HTTP response containing the generated solution text and send it to the terminal.

[0276] Step 8:

[0277] The terminal displays the received solution text to the user.

[0278] Input: Solution text

[0279] Output: Displayed on the user interface

[0280] Specific operation: The received solution text is displayed on the device screen, visually communicating the solution to the user.

[0281] (Application example 2)

[0282] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0283] Conventional customer service in brick-and-mortar stores has difficulty instantly understanding customers' voice-generated questions and requests and providing appropriate solutions. Furthermore, it has been impossible to respond while taking into account the customer's emotional state, limiting the improvement of customer satisfaction. There is a need for a system that can solve these problems, provide appropriate solutions in response to customer requests in real time, and provide support that takes into account the customer's emotional state.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0285] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions to the user, means for analyzing the user's emotions, and means for adjusting the solutions based on the results of the emotion analysis. This makes it possible to understand customer questions and requests voiced in real time even in a physical store, and to provide appropriate services that take into account the customer's emotional state.

[0286] "Voice input" refers to voice data uttered by a user and acquired through a microphone.

[0287] "Text conversion" is the process of converting captured audio data into text data.

[0288] "Solution generation" is the process of generating appropriate solutions based on the analyzed text data in response to the user's requests.

[0289] "Emotion analysis" is the process of analyzing the tone, rate, and pauses of audio data to identify the user's emotional state.

[0290] "Smart glasses" are eyeglass-type devices that have the ability to display information and receive voice input.

[0291] A "smartphone" is a portable information terminal that has the ability to display information and receive voice input.

[0292] "Natural language processing technology" is a technology for analyzing text data and understanding and generating human language.

[0293] "Means for presenting to the user" refers to the method by which the generated solution is presented to the user visually or audibly.

[0294] "Adjustment based on emotion analysis results" is a process of adjusting the content of the solution and the method of providing it, reflecting the user's emotional information obtained through emotion analysis.

[0295] A "microphone" is an acoustic device for acquiring audio data.

[0296] This invention is a system for improving the efficiency of customer service in brick-and-mortar stores and responding to customer voice inquiries and requests in real time. The system includes means for acquiring voice input from a user, means for converting the acquired voice data into text data, means for analyzing the converted text data to generate appropriate solutions, means for presenting the solutions to the user, means for analyzing the user's emotions, and means for adjusting the solutions based on the results of the emotion analysis.

[0297] Hardware / Software used

[0298] The hardware used includes smart glasses and a smartphone, which are used to capture voice input and present information.

[0299] The software uses the Google Cloud Speech-to-Text API to convert voice data into text, and a natural language processing engine (e.g., Google Natural Language API, OpenAI GPT-4) to analyze text data. It also integrates an emotion engine to analyze user emotions.

[0300] System operation explanation

[0301] 1. Voice input acquisition:

[0302] The system first receives voice input from the user using the microphone in the smart glasses or smartphone. When the customer speaks a question or request, the voice data is sent to the device.

[0303] 2. Audio to text conversion:

[0304] The acquired voice data is converted into text data in real time using the Google Cloud Speech-to-Text API, etc. At this stage, voice recognition technology is utilized to convert the user's speech into text.

[0305] 3. Text data analysis:

[0306] The converted text data is sent to a cloud server and analyzed by a natural language processing engine (Google Natural Language API, OpenAI GPT-4). The server then understands the user's question or request and generates an appropriate solution.

[0307] 4. Emotion analysis:

[0308] The server is integrated with an emotion engine that analyzes the customer's emotional state from the tone, speed, and pauses of the voice data, thereby extracting information such as whether the customer is confused or excited.

[0309] 5. Coordinating and presenting solutions:

[0310] Solutions are generated as text data and adjusted based on sentiment analysis. For example, if the customer is confused, more detailed instructions will be included. The generated solutions are displayed on the screen of the smart glasses or smartphone, and staff can assist the customer based on the solution.

[0311] Specific examples

[0312] For example, if a customer asks, "Where is this item?", the system captures voice input and converts it to text in real time. The text data is then analyzed on a cloud server to generate product location instructions. If sentiment analysis determines that the customer is confused, additional details such as "Section A is on the right, straight ahead from the entrance" are provided. This allows customers to receive prompt and accurate guidance.

[0313] Prompt Sentence Examples

[0314] "Please provide an appropriate response to the following question: Where is this item?"

[0315] In this way, this system will improve the efficiency of customer service in physical stores and increase customer satisfaction.

[0316] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0317] Step 1:

[0318] The device receives voice input from the user. The input voice data is collected through the microphone of smart glasses or a smartphone. Specifically, the voice data is generated when the user speaks a question or request.

[0319] Step 2:

[0320] The device converts the collected voice data into text data using the Google Cloud Speech-to-Text API. The input for this process is voice data, and the output is text data. Specifically, the speech recognition engine combines acoustic and language models to analyze the voice signal and generate corresponding text.

[0321] Step 3:

[0322] The device sends the converted text data to a cloud server. The server uses the received text data as input and analyzes it using a natural language processing engine (e.g., Google Natural Language API or OpenAI GPT-4). The output is an initial solution proposal based on the user's question or request. Specifically, the natural language processing engine understands the meaning of the text and extracts relevant information.

[0323] Step 4:

[0324] The server inputs voice data and analyzes it using an emotion engine to determine the user's emotional state. The output is emotional information such as whether the user is distressed, angry, or happy. Specifically, the emotion analysis engine analyzes parameters such as tone, speed, and pauses in the voice and identifies the user's emotional state from the results.

[0325] Step 5:

[0326] The server adjusts the initial solution based on the sentiment analysis results. The input is the initial solution and sentiment information, and the output is the adjusted solution. Specifically, if the user is confused, for example, the server adds more detailed instructions and explanations to the solution. Also, if the user is in a hurry, the server prioritizes a quick solution.

[0327] Step 6:

[0328] The server sends the final solution to the device, which receives it and presents it to the user. Specifically, it displays it as text on the screen of smart glasses or a smartphone, or plays it as audio guidance. This allows the user to receive the appropriate solution in real time.

[0329] In this way, the system can respond to user requests quickly and accurately through the connection between the terminal and the server. In addition, by combining this with emotion analysis, it can provide a more friendly and considerate response to the user.

[0330] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0331] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0332] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0333] [Second embodiment]

[0334] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0335] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0336] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0337] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0338] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0339] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0340] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0341] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0342] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0343] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0344] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0345] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0346] Implementing this invention involves four main steps: a user inputting voice using a mobile phone, a terminal converting the voice into text, a server analyzing the text and generating a solution, and finally a server presenting the solution to the user.

[0347] First, the user speaks into the microphone on their mobile phone to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi."

[0348] The device then converts this voice input into text using speech recognition technology, converting the user's voice into text data such as "Please tell me how to set up Wi-Fi."

[0349] The converted text is sent to a server, which uses natural language processing techniques to analyze it and generate the specific solution the user is looking for. For example, the server might generate instructions like, "Open Settings and select the Wi-Fi option."

[0350] Finally, the terminal receives the solution from the server and presents it to the user, who can then configure the mobile phone according to the displayed solution.

[0351] For example, if a user speaks, "How do I set up Bluetooth?", the device converts this speech into text. The text is sent to a server, which then generates instructions such as, "Open the settings screen, select the Bluetooth option, and turn it on." The user can then follow the instructions to set up Bluetooth.

[0352] This system allows even users with little technical knowledge to easily and efficiently configure their mobile phones. By linking the server and the device, the system quickly provides users with an appropriate solution, reducing their anxiety about operating their mobile phones.

[0353] The processing flow will be explained below.

[0354] Step 1:

[0355] The user speaks into the microphone of the mobile phone. For example, the user says, "Please tell me how to set up Wi-Fi."

[0356] Step 2:

[0357] The device captures voice data through a microphone. This voice data is a direct recording of the user's speech.

[0358] Step 3:

[0359] The device uses voice recognition technology to convert voice data into text data. At this time, the voice input "Please tell me how to set up Wi-Fi" is converted into text format.

[0360] Step 4:

[0361] The terminal transmits the converted text data to a server via the Internet, where the text data is further analyzed.

[0362] Step 5:

[0363] The server uses natural language processing technology to understand the content of the text data and identify the user's requests and problems. For example, from the text "Please tell me how to set up Wi-Fi," it can analyze that the user is looking for information about Wi-Fi settings.

[0364] Step 6:

[0365] Based on the analysis, the server generates the optimal solution for the user's request, such as "Open the settings screen and select the Wi-Fi option."

[0366] Step 7:

[0367] The server sends the generated solution to the terminal as text data, and the terminal receives the solution from the server.

[0368] Step 8:

[0369] The device visually displays the received text data to the user. Specifically, the device displays the message "Open Settings and select the Wi-Fi option."

[0370] Step 9:

[0371] The user follows the instructions displayed on the device screen to set up and operate the mobile phone. For example, the user can set up Wi-Fi by opening the settings screen and selecting the Wi-Fi option.

[0372] These steps allow the user to receive appropriate support for setting up and operating the mobile phone through voice input.

[0373] Example 1

[0374] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0375] Existing systems have a problem in that it is difficult to provide a quick and accurate solution when a user asks about a technical problem or setting method. In particular, it is difficult to properly understand the user's request entered by voice and provide a specific solution based on that. As a result, users who lack technical knowledge often find it time-consuming to set up or solve a problem, which can be frustrating.

[0376] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0377] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for converting the text into prompt sentences for solution generation, means for generating solutions from the prompt sentences using a generative AI model, and means for presenting the solutions to the user. This makes it possible to generate prompt sentences from text and generate accurate solutions using the generative AI model, and quickly present them to the user.

[0378] The "means for obtaining voice input from a user" refers to a device or method for obtaining information input by voice from a user.

[0379] "Means for converting voice input into text" refers to technology or devices for converting acquired voice data into character data.

[0380] The "means for analyzing text and generating appropriate solutions" is a device or method that analyzes the converted text and creates appropriate solutions for the user based on the results of the analysis.

[0381] The "means for presenting a solution to a user" refers to a device or method for presenting the generated solution to a user in an easy-to-understand manner, and includes screen display, audio output, and the like.

[0382] "Means for converting text into prompts for solution generation" refers to technology or devices for converting analyzed text data into prompts in a format that is easy for the generative AI model to understand.

[0383] "Means for generating solutions from prompt sentences using a generative AI model" refers to technology or devices that utilize a generative AI model based on prompt sentences to generate specific solutions.

[0384] To put this invention into practice, a system is constructed in which a user inputs voice using a mobile information terminal, and appropriate solutions are presented based on that input. The specific processing flow of the entire system and details of the hardware and software used are described below.

[0385] First, the user speaks into the microphone on the mobile information terminal to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi." This voice input is captured by the terminal via the microphone, which serves as a voice acquisition means.

[0386] The device then converts the user's voice into text using speech recognition technology, using voice recognition software such as the Google Cloud Speech-to-Text API, which converts the speech into text data such as "How do I set up Wi-Fi?"

[0387] The converted text data is sent from the device to a server via the Internet. The server converts the received text into a prompt for solution generation, and generates a solution using a generative AI model (e.g., OpenAI's GPT-3) based on the prompt. The prompt is converted to the format "The user is looking for information on how to set up Wi-Fi."

[0388] The solution generated using the generative AI model is sent back from the server to the device, which receives the solution and presents it to the user visually or audibly. For example, a specific solution such as "Open the Settings screen and select the Wi-Fi option" may be displayed on the device screen.

[0389] (Example)

[0390] For example, consider the case where a user speaks, "How do I set up Bluetooth?" The user's voice input is captured through the microphone of the mobile information device. The device converts this speech into text data, "How do I set up Bluetooth?", using the Google Cloud Speech-to-Text API. This text data is sent to the server, which analyzes the text and generates a specific solution, "Open the settings screen, select the Bluetooth option, and turn it on," based on a generative AI model. This solution is sent to the device and displayed to the user.

[0391] This system allows even users with little technical knowledge to easily and efficiently configure their mobile devices. The collaboration between the server and the device quickly provides users with an appropriate solution, reducing anxiety about operating the mobile device.

[0392] Example prompts to be input to the generative AI model:

[0393] Input: How do I set up Bluetooth?

[0394] Model output: Open the settings screen, select the Bluetooth option and turn it on

[0395] Thus, by implementing the present invention, a system can be realized that quickly generates and provides appropriate solutions based on voice input from a user.

[0396] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0397] Step 1:

[0398] The user speaks into the microphone of the mobile information device to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi." This voice input is captured as data to be processed in the next step.

[0399] Step 2:

[0400] The device receives voice data from the user and converts it into text data using speech recognition technology such as the Google Cloud Speech-to-Text API. The voice input "Please tell me how to set up Wi-Fi" is converted into text data "Please tell me how to set up Wi-Fi." This converted text data becomes the input for the next step.

[0401] Step 3:

[0402] The terminal transmits the converted text data to the server, where the terminal uses a secure communication protocol to ensure the security of the data while sending the text data to the server.

[0403] Step 4:

[0404] The server analyzes the received text data and converts it into a prompt for generating a solution. For example, the received text "Please tell me how to set up Wi-Fi" is converted into a prompt "The user is looking for instructions on how to set up Wi-Fi." This prompt becomes the input data for the next step.

[0405] Step 5:

[0406] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate solution from the prompt. The generative AI model inputs the prompt and generates a specific solution: "Open the Settings screen and select the Wi-Fi option." This solution becomes the output data for the next step.

[0407] Step 6:

[0408] The server sends the generated solution to the device, again using a secure communication protocol to ensure data safety. The solution arrives at the device.

[0409] Step 7:

[0410] The device will then present the received solution to the user visually or audibly, for example, by displaying a message on the device screen saying, "Open the settings screen and select the Wi-Fi option." The user can then follow the instructions to proceed with the setup.

[0411] The above is the specific processing flow of the program of this system.

[0412] (Application example 1)

[0413] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0414] Inventory management in logistics centers is a time-consuming and labor-intensive task, and efficient inventory management is required, especially when handling large quantities of items. However, it is difficult for many employees to manage inventory accurately and quickly. Furthermore, using a system that requires complex operations and knowledge can easily lead to mistakes and waste of time. This invention aims to solve these problems by providing a simple and efficient inventory management system that uses voice input.

[0415] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0416] In this invention, the server includes means for acquiring voice input, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions by voice, and means for being installed in the logistics robot to support inventory management, thereby enabling employees to easily manage inventory by voice and achieving efficient and accurate work.

[0417] "User" refers to a person who uses the system to provide voice input and obtain a solution.

[0418] "Voice input" refers to information that a user provides to the system by speaking through a microphone.

[0419] "Text" refers to character data converted from voice input using voice recognition technology.

[0420] "Analysis" is the process performed to understand text data and derive appropriate solutions.

[0421] "Solutions" refer to instructions for solving problems or operating procedures that are generated as a result of analyzing the user's voice input.

[0422] "Presenting" is the act of presenting the generated solution to the user in a visible or audible form.

[0423] "Audio presentation" refers to verbally communicating the generated solution to the user using a synthesized voice or the like.

[0424] A "logistics robot" is an automated mechanical device used to assist with inventory management, product movement, and other tasks within a logistics center.

[0425] "Installation" is the act of placing a system or software on specific hardware and making it operational.

[0426] "Inventory management" refers to the general task of monitoring and appropriately managing inventory quantities and conditions.

[0427] "Server" refers to the computer system that processes voice input from the user and performs text analysis and solution generation.

[0428] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0429] A "microphone" is a device that converts sound into an electrical signal and inputs it into a system.

[0430] This invention relates to a system that supports efficient inventory management in logistics centers. Its main components include a microphone for capturing voice input, speech recognition software for converting speech into text, natural language processing technology for analyzing text and generating solutions, speech synthesis technology for presenting solutions to users, and a logistics robot. Each component is described in detail below.

[0431] 1. Voice input

[0432] A user speaks into a microphone to describe the inventory problem or request they want to solve. This speech data is captured by speech recognition software. For example, a user might say, "Check inventory and confirm the quantity of new product."

[0433] 2. Voice Recognition

[0434] Using speech recognition software (e.g., the "speech_recognition" library) installed on the device, the user's speech is converted into text data, which generates the text information "Check inventory to see the quantity of new products."

[0435] 3. Natural Language Processing

[0436] This text data is sent to a server and analyzed by a server-side natural language processing engine (for example, Google Natural Language API). This analysis allows the system to accurately understand the user's intentions and requests and generate appropriate solutions.

[0437] 4. Solution Generation

[0438] The server generates a specific solution based on the analysis results, for example, the server generates an instruction such as "Check the inventory list and display the quantity of the new product."

[0439] 5. Providing a solution

[0440] The generated solution is converted into voice using speech synthesis technology (e.g., the "pyttsx3" library) and presented to the user via the logistics robot, allowing the user to receive the solution by voice.

[0441] 6. Logistics robots

[0442] The logistics robot assists with inventory management by performing stock checks and quantity confirmations according to generated instructions and reporting the results to the user.

[0443] Specific examples

[0444] For example, if a logistics center staff member voice-inputs, "Check inventory and confirm new product quantities," the voice is captured by a microphone and converted into text via speech recognition software. The text data is sent to a server and analyzed by a natural language processing engine. The server generates a solution, "Check inventory list and display new product quantities," and presents it to the user using speech synthesis technology. This series of steps enables fast and efficient inventory management.

[0445] Prompt Sentence Examples

[0446] Example user input: "Check inventory to see new product quantities."

[0447] Example server response: "Check inventory list and display new product quantities."

[0448] In this way, the present invention can improve the efficiency of inventory management in a logistics center through voice input, thereby improving work accuracy and reducing labor.

[0449] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0450] Step 1:

[0451] The user inputs instructions by voice.

[0452] Input: User's spoken input (e.g., "Check inventory to see new product quantities")

[0453] Output: Audio data

[0454] Specific operation: The user verbally inputs instructions regarding inventory management to the logistics robot through a microphone.

[0455] Step 2:

[0456] The device converts the voice input into text.

[0457] Input: Audio data

[0458] Output: Text data (e.g., "Check inventory to see new product quantities")

[0459] Specific operation: Using speech recognition software (the "speech_recognition" library), the voice data is analyzed and converted into appropriate text data.

[0460] Step 3:

[0461] The terminal transmits the text data to the server.

[0462] Input: Text data

[0463] Output: Send text data to the server

[0464] Specific operation: The terminal sends the converted text data to the server using a protocol such as an HTTP request.

[0465] Step 4:

[0466] The server analyzes the text data and generates solutions.

[0467] Input: Text data (e.g., "Check inventory to see new product quantities")

[0468] Output: Solution (e.g. "Check the inventory list and display the quantity of the new product")

[0469] Specific operation: The server uses a natural language processing engine (such as the Google Natural Language API) to analyze the text data, understand the user's intent, and generate an appropriate solution.

[0470] Step 5:

[0471] The server sends the solution to the device.

[0472] Input: Solution

[0473] Output: Solution sent to terminal

[0474] Specific operation: The server returns the generated solution to the terminal as text data.

[0475] Step 6:

[0476] The device will vocalize the solution and present it to the user.

[0477] Input: Solution (e.g. "Check the inventory list and display the new product quantity")

[0478] Output: A spoken solution

[0479] Specific operation: Using the speech synthesis technology ("pyttsx3" library) installed on the device, the solution is output aloud and communicated to the user.

[0480] Step 7:

[0481] Logistics robots perform inventory management tasks.

[0482] Input: Instructions for implementing the solution

[0483] Output: Stock check result

[0484] Specific operation: The logistics robot performs actual inventory checks according to instructions and reports the results to the user.

[0485] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0486] The present invention is a system including means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions to the user, and an emotion engine for acquiring the user's emotions.

[0487] The user speaks into the microphone of their mobile phone, saying something like, "Please tell me how to set up Wi-Fi." This voice input is picked up by the device's microphone. The device then converts the acquired voice data into text data using voice recognition technology. During this conversion process, voice recognition technology is used to convert the voice into character data, making it possible to identify the user's request as text.

[0488] The converted text data is sent to a server via the Internet. The server then analyzes the text data using natural language processing technology. Specifically, for example, from the text "Please tell me how to set up Wi-Fi," the server identifies that the user is looking for information about Wi-Fi settings.

[0489] The server also includes an emotion engine that identifies the user's emotions. The emotion engine analyzes elements of the voice data, such as tone, speed, and pauses, to recognize the user's emotional state (e.g., irritation, confusion, excitement, etc.). This emotional information is used to devise solutions.

[0490] The server reflects the user's emotional information acquired by the emotion engine and generates a more appropriate solution. For example, if the user is confused, it generates detailed, step-by-step instructions. Specifically, if the emotion engine identifies the user's confusion, the server can create more friendly and detailed instructions, such as "First, open the settings screen, and then select the Wi-Fi option."

[0491] The generated solution is sent to the device as text data again. The device receives this text data and displays the solution to the user. The user configures the Wi-Fi by following the solution displayed on the device screen.

[0492] For example, if a user says, "How do I set up Bluetooth?", the device converts this speech into text. The text is sent to the server, which generates instructions such as, "Open the Settings screen, select the Bluetooth option, and turn it on." If the emotion engine detects the user's confusion, it generates additional detailed instructions, such as, "You can find the Settings screen by swiping right from the home screen," and sends these to the device for display.

[0493] This system allows users to receive appropriate support for setting up and operating their mobile phones through voice input, and also provides detailed support according to their emotional state. As a result, users will be able to reduce their anxiety about operating their mobile phones and set them up more smoothly.

[0494] The processing flow will be explained below.

[0495] Step 1:

[0496] The user speaks into the microphone of the mobile phone. For example, the user says, "Please tell me how to set up Wi-Fi."

[0497] Step 2:

[0498] The device uses a microphone to capture the user's voice data, which is a recording of the user's speech.

[0499] Step 3:

[0500] The device uses voice recognition technology to convert the acquired voice data into text data, resulting in the voice being converted into text saying, "Please tell me how to set up Wi-Fi."

[0501] Step 4:

[0502] The terminal transmits the converted text data to a server via the Internet, where the text data is further analyzed.

[0503] Step 5:

[0504] The server uses natural language processing technology to analyze the content of the text data. For example, from the text "Please tell me how to set up Wi-Fi," it understands that the user is looking for information about Wi-Fi settings.

[0505] Step 6:

[0506] At the same time, the emotion engine installed on the server analyzes the voice data and recognizes the user's emotions, such as tone of voice, speed, and pauses, to determine whether the user is confused or annoyed.

[0507] Step 7:

[0508] The server generates the optimal solution based on the analysis results and emotional information according to the user's request. For example, if the user is confused, it generates detailed instructions such as "Open the settings screen and select the Wi-Fi option."

[0509] Step 8:

[0510] The server sends the generated solution to the terminal as text data, and the terminal receives the solution from the server.

[0511] Step 9:

[0512] The device visually displays the received text data to the user, specifically displaying the message "Open Settings and select the Wi-Fi option" on the device screen.

[0513] Step 10:

[0514] The user follows the instructions displayed on the device screen to set up and operate the mobile phone. For example, the user can set up Wi-Fi by opening the settings screen and selecting the Wi-Fi option.

[0515] Through this series of steps, users can receive appropriate support for setting up and operating their mobile phone through voice input. Furthermore, the introduction of an emotion engine provides detailed support according to the user's emotional state, reducing stress and anxiety when operating the device.

[0516] Example 2

[0517] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0518] Conventional technologies have had difficulty providing appropriate solutions, particularly in response to the user's emotional state, when users obtain information using voice input. This often leaves users feeling confused and anxious. Furthermore, when users need detailed guidance, they are unable to obtain appropriate and prompt assistance.

[0519] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0520] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating an appropriate solution, means for presenting the solution to the user, means for identifying the user's emotion, and means for adjusting the solution based on the result of identifying the emotion. This makes it possible to provide an appropriate solution according to the user's emotional state based on the information acquired through the voice input, reduce the user's confusion and anxiety, and provide quick and detailed guidance.

[0521] "Voice input" is data that electronically captures the words and sounds spoken by a user.

[0522] "Text conversion means" refers to technology or devices for converting acquired voice data into text data.

[0523] "Means for generating appropriate solutions" refers to technologies and devices that provide the information and instructions desired by the user based on the analyzed text data.

[0524] "Presentation means" refers to the techniques or devices used to visually or audibly communicate the generated solution to the user.

[0525] An "emotion identification means" is a technique or device that identifies a user's emotional state (e.g., confusion, excitement, irritation, etc.) from the user's voice data or other input.

[0526] The "means for adjusting a solution" refers to a technique or device for changing the content and presentation method of the solution provided according to the identified emotional state of the user.

[0527] The present invention is a system that receives voice input from a user, converts the voice into text, analyzes the text, and presents appropriate solutions. It also has the ability to identify the user's emotions and adjust solutions based on the emotional information. Detailed embodiments are described below.

[0528] Acquiring voice input

[0529] The user speaks into the device's microphone, for example, "Please tell me how to set up Wi-Fi." The device uses a high-precision microphone to capture this voice, which is then captured as digital data.

[0530] Speech recognition and text conversion

[0531] The device uses voice recognition technology to convert the captured voice data into text data. Specifically, it uses voice recognition software such as the Google Speech-to-Text API to convert the user's voice into a string of characters. For example, the voice saying "Please tell me how to set up Wi-Fi" is converted into text "Please tell me how to set up Wi-Fi."

[0532] Sending text data to the server

[0533] The converted text data is then sent to a server via the Internet. The device uses the HTTPS protocol to transfer the text data to the server securely and quickly.

[0534] Text data analysis

[0535] The server uses natural language processing technology to analyze the text data it receives. For example, a generative AI model such as OpenAI GPT-4 is used. The server analyzes the text data, understands the user's request, and determines what the user specifically wants from the text "Please tell me how to set up Wi-Fi."

[0536] Identifying emotions

[0537] The server is equipped with an emotion engine that analyzes parameters such as tone, speed, and pauses in the voice data to identify the user's emotional state. For example, it analyzes whether the user is confused or irritated and recognizes that emotional state.

[0538] Solution generation and coordination

[0539] The server takes into account the emotional information obtained by the emotion engine and generates an appropriate solution. The generated solution is provided to the user in an easy-to-understand manner using natural language generation technology. If the user is confused, a detailed guide is generated. For example, step-by-step instructions are provided, such as "First, open the settings screen, then select the Wi-Fi option."

[0540] Submitting and Viewing Solutions

[0541] The generated solution is sent to the device as text data, and the device displays the solution to the user visually or audibly. The user can then configure the Wi-Fi by following the instructions displayed on the device screen.

[0542] Examples of concrete examples and prompts

[0543] As a concrete example, consider the case where a user speaks, "How do I set up Bluetooth?" The device converts this speech into text and sends it to the server. The server generates instructions such as, "Open the Settings screen, select the Bluetooth option, and turn it on." If the emotion engine detects the user's confusion, it will generate additional detailed instructions, such as, "You can find the Settings screen by swiping right from the home screen," and send these to the device for display.

[0544] Prompt Sentence Examples

[0545] User Input: "How do I set up Bluetooth?"

[0546] User's emotional state: "Confused"

[0547] Goal: Provide users with clear, detailed instructions on how to set up Bluetooth.

[0548] output:

[0549] Open the Settings screen, select the Bluetooth option, and switch it on. You can find the Settings screen by swiping right from the home screen.

[0550] This system allows users to receive appropriate support for setting up and operating their mobile devices through voice input. Furthermore, by providing detailed support according to the user's emotional state, it is possible to reduce the user's anxiety and achieve smoother operation.

[0551] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0552] Step 1:

[0553] The user speaks into the device's microphone, saying something like, "Please tell me how to set up Wi-Fi."

[0554] Input: User voice input

[0555] Output: Audio data

[0556] What it does: The device's microphone picks up the audio and stores it as digital audio data.

[0557] Step 2:

[0558] The device uses voice recognition technology to convert voice data into text data, specifically using the Google Speech-to-Text API.

[0559] Input: Audio data

[0560] Output: Text data

[0561] Specific operation: Call the speech recognition API and send the speech data to the API. Retrieve the text data returned from the API. The speech "Please tell me how to set up Wi-Fi" is converted into text "Please tell me how to set up Wi-Fi."

[0562] Step 3:

[0563] The terminal transmits the converted text data to a server via the Internet.

[0564] Input: Text data

[0565] Output: HTTP request to the server

[0566] Specific operation: Generates an HTTPS request and securely sends the converted text data to the server.

[0567] Step 4:

[0568] The server analyzes the received text data using natural language processing technology (e.g., OpenAI GPT-4).

[0569] Input: Text data

[0570] Output: Data that recognizes the user's request

[0571] Specific operation: The natural language processing engine is started and the text is analyzed. As a result, the specific request "Set up Wi-Fi" is recognized.

[0572] Step 5:

[0573] The server analyzes the tone, speed, and timing of the voice data using an emotion engine to identify the user's emotional state.

[0574] Input: Audio parameters

[0575] Output: Emotional state (e.g., confused, annoyed, etc.)

[0576] What it does: It uses an emotion engine to analyze multiple parameters of the voice data to identify the user's emotional state.

[0577] Step 6:

[0578] The server uses a generative AI model to generate appropriate solutions based on the text and emotional state.

[0579] Input: User's request, emotional state

[0580] Output: Solution text

[0581] What it does: It uses a natural language generation engine to generate solutions based on the user's request and emotional state. It provides detailed, step-by-step instructions for confused users. For example, "First, open Settings, then select the Wi-Fi option."

[0582] Step 7:

[0583] The server transmits the generated solution to the terminal again as text data.

[0584] Input: Solution text

[0585] Output: HTTP response to the device

[0586] Specific behavior: Generate an HTTP response containing the generated solution text and send it to the terminal.

[0587] Step 8:

[0588] The terminal displays the received solution text to the user.

[0589] Input: Solution text

[0590] Output: Displayed on the user interface

[0591] Specific operation: The received solution text is displayed on the device screen, visually communicating the solution to the user.

[0592] (Application example 2)

[0593] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0594] Conventional customer service in brick-and-mortar stores has difficulty instantly understanding customers' voice-generated questions and requests and providing appropriate solutions. Furthermore, it has been impossible to respond while taking into account the customer's emotional state, limiting the improvement of customer satisfaction. There is a need for a system that can solve these problems, provide appropriate solutions in response to customer requests in real time, and provide support that takes into account the customer's emotional state.

[0595] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0596] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions to the user, means for analyzing the user's emotions, and means for adjusting the solutions based on the results of the emotion analysis. This makes it possible to understand customer questions and requests voiced in real time even in a physical store, and to provide appropriate services that take into account the customer's emotional state.

[0597] "Voice input" refers to voice data uttered by a user and acquired through a microphone.

[0598] "Text conversion" is the process of converting captured audio data into text data.

[0599] "Solution generation" is the process of generating appropriate solutions based on the analyzed text data in response to the user's requests.

[0600] "Emotion analysis" is the process of analyzing the tone, rate, and pauses of audio data to identify the user's emotional state.

[0601] "Smart glasses" are eyeglass-type devices that have the ability to display information and receive voice input.

[0602] A "smartphone" is a portable information terminal that has the ability to display information and receive voice input.

[0603] "Natural language processing technology" is a technology for analyzing text data and understanding and generating human language.

[0604] "Means for presenting to the user" refers to the method by which the generated solution is presented to the user visually or audibly.

[0605] "Adjustment based on emotion analysis results" is a process of adjusting the content of the solution and the method of providing it, reflecting the user's emotional information obtained through emotion analysis.

[0606] A "microphone" is an acoustic device for acquiring audio data.

[0607] This invention is a system for improving the efficiency of customer service in brick-and-mortar stores and responding to customer voice inquiries and requests in real time. The system includes means for acquiring voice input from a user, means for converting the acquired voice data into text data, means for analyzing the converted text data to generate appropriate solutions, means for presenting the solutions to the user, means for analyzing the user's emotions, and means for adjusting the solutions based on the results of the emotion analysis.

[0608] Hardware / Software used

[0609] The hardware used includes smart glasses and a smartphone, which are used to capture voice input and present information.

[0610] The software uses the Google Cloud Speech-to-Text API to convert voice data into text, and a natural language processing engine (e.g., Google Natural Language API, OpenAI GPT-4) to analyze text data. It also integrates an emotion engine to analyze user emotions.

[0611] System operation explanation

[0612] 1. Voice input acquisition:

[0613] The system first receives voice input from the user using the microphone in the smart glasses or smartphone. When the customer speaks a question or request, the voice data is sent to the device.

[0614] 2. Audio to text conversion:

[0615] The acquired voice data is converted into text data in real time using the Google Cloud Speech-to-Text API, etc. At this stage, voice recognition technology is utilized to convert the user's speech into text.

[0616] 3. Text data analysis:

[0617] The converted text data is sent to a cloud server and analyzed by a natural language processing engine (Google Natural Language API, OpenAI GPT-4). The server then understands the user's question or request and generates an appropriate solution.

[0618] 4. Emotion analysis:

[0619] The server is integrated with an emotion engine that analyzes the customer's emotional state from the tone, speed, and pauses of the voice data, thereby extracting information such as whether the customer is confused or excited.

[0620] 5. Coordinating and presenting solutions:

[0621] Solutions are generated as text data and adjusted based on sentiment analysis. For example, if the customer is confused, more detailed instructions will be included. The generated solutions are displayed on the screen of the smart glasses or smartphone, and staff can assist the customer based on the solution.

[0622] Specific examples

[0623] For example, if a customer asks, "Where is this item?", the system captures voice input and converts it to text in real time. The text data is then analyzed on a cloud server to generate product location instructions. If sentiment analysis determines that the customer is confused, additional details such as "Section A is on the right, straight ahead from the entrance" are provided. This allows customers to receive prompt and accurate guidance.

[0624] Prompt Sentence Examples

[0625] "Please provide an appropriate response to the following question: Where is this item?"

[0626] In this way, this system will improve the efficiency of customer service in physical stores and increase customer satisfaction.

[0627] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0628] Step 1:

[0629] The device receives voice input from the user. The input voice data is collected through the microphone of smart glasses or a smartphone. Specifically, the voice data is generated when the user speaks a question or request.

[0630] Step 2:

[0631] The device converts the collected voice data into text data using the Google Cloud Speech-to-Text API. The input for this process is voice data, and the output is text data. Specifically, the speech recognition engine combines acoustic and language models to analyze the voice signal and generate corresponding text.

[0632] Step 3:

[0633] The device sends the converted text data to a cloud server. The server uses the received text data as input and analyzes it using a natural language processing engine (e.g., Google Natural Language API or OpenAI GPT-4). The output is an initial solution proposal based on the user's question or request. Specifically, the natural language processing engine understands the meaning of the text and extracts relevant information.

[0634] Step 4:

[0635] The server inputs voice data and analyzes it using an emotion engine to determine the user's emotional state. The output is emotional information such as whether the user is distressed, angry, or happy. Specifically, the emotion analysis engine analyzes parameters such as tone, speed, and pauses in the voice and identifies the user's emotional state from the results.

[0636] Step 5:

[0637] The server adjusts the initial solution based on the sentiment analysis results. The input is the initial solution and sentiment information, and the output is the adjusted solution. Specifically, if the user is confused, for example, the server adds more detailed instructions and explanations to the solution. Also, if the user is in a hurry, the server prioritizes a quick solution.

[0638] Step 6:

[0639] The server sends the final solution to the device, which receives it and presents it to the user. Specifically, it displays it as text on the screen of smart glasses or a smartphone, or plays it as audio guidance. This allows the user to receive the appropriate solution in real time.

[0640] In this way, the system can respond to user requests quickly and accurately through the connection between the terminal and the server. In addition, by combining this with emotion analysis, it can provide a more friendly and considerate response to the user.

[0641] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0642] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0643] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0644] [Third embodiment]

[0645] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0646] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0647] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0648] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0649] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0650] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0651] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0652] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0653] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0654] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0655] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0656] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0657] Implementing this invention involves four main steps: a user inputting voice using a mobile phone, a terminal converting the voice into text, a server analyzing the text and generating a solution, and finally a server presenting the solution to the user.

[0658] First, the user speaks into the microphone on their mobile phone to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi."

[0659] The device then converts this voice input into text using speech recognition technology, converting the user's voice into text data such as "Please tell me how to set up Wi-Fi."

[0660] The converted text is sent to a server, which uses natural language processing techniques to analyze it and generate the specific solution the user is looking for. For example, the server might generate instructions like, "Open Settings and select the Wi-Fi option."

[0661] Finally, the terminal receives the solution from the server and presents it to the user, who can then configure the mobile phone according to the displayed solution.

[0662] For example, if a user speaks, "How do I set up Bluetooth?", the device converts this speech into text. The text is sent to a server, which then generates instructions such as, "Open the settings screen, select the Bluetooth option, and turn it on." The user can then follow the instructions to set up Bluetooth.

[0663] This system allows even users with little technical knowledge to easily and efficiently configure their mobile phones. By linking the server and the device, the system quickly provides users with an appropriate solution, reducing their anxiety about operating their mobile phones.

[0664] The processing flow will be explained below.

[0665] Step 1:

[0666] The user speaks into the microphone of the mobile phone. For example, the user says, "Please tell me how to set up Wi-Fi."

[0667] Step 2:

[0668] The device captures voice data through a microphone. This voice data is a direct recording of the user's speech.

[0669] Step 3:

[0670] The device uses voice recognition technology to convert voice data into text data. At this time, the voice input "Please tell me how to set up Wi-Fi" is converted into text format.

[0671] Step 4:

[0672] The terminal transmits the converted text data to a server via the Internet, where the text data is further analyzed.

[0673] Step 5:

[0674] The server uses natural language processing technology to understand the content of the text data and identify the user's requests and problems. For example, from the text "Please tell me how to set up Wi-Fi," it can analyze that the user is looking for information about Wi-Fi settings.

[0675] Step 6:

[0676] Based on the analysis, the server generates the optimal solution for the user's request, such as "Open the settings screen and select the Wi-Fi option."

[0677] Step 7:

[0678] The server sends the generated solution to the terminal as text data, and the terminal receives the solution from the server.

[0679] Step 8:

[0680] The device visually displays the received text data to the user. Specifically, the device displays the message "Open Settings and select the Wi-Fi option."

[0681] Step 9:

[0682] The user follows the instructions displayed on the device screen to set up and operate the mobile phone. For example, the user can set up Wi-Fi by opening the settings screen and selecting the Wi-Fi option.

[0683] These steps allow the user to receive appropriate support for setting up and operating the mobile phone through voice input.

[0684] Example 1

[0685] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0686] Existing systems have a problem in that it is difficult to provide a quick and accurate solution when a user asks about a technical problem or setting method. In particular, it is difficult to properly understand the user's request entered by voice and provide a specific solution based on that. As a result, users who lack technical knowledge often find it time-consuming to set up or solve a problem, which can be frustrating.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0688] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for converting the text into prompt sentences for solution generation, means for generating solutions from the prompt sentences using a generative AI model, and means for presenting the solutions to the user. This makes it possible to generate prompt sentences from text and generate accurate solutions using the generative AI model, and quickly present them to the user.

[0689] The "means for obtaining voice input from a user" refers to a device or method for obtaining information input by voice from a user.

[0690] "Means for converting voice input into text" refers to technology or devices for converting acquired voice data into character data.

[0691] The "means for analyzing text and generating appropriate solutions" is a device or method that analyzes the converted text and creates appropriate solutions for the user based on the results of the analysis.

[0692] The "means for presenting a solution to a user" refers to a device or method for presenting the generated solution to a user in an easy-to-understand manner, and includes screen display, audio output, and the like.

[0693] "Means for converting text into prompts for solution generation" refers to technology or devices for converting analyzed text data into prompts in a format that is easy for the generative AI model to understand.

[0694] "Means for generating solutions from prompt sentences using a generative AI model" refers to technology or devices that utilize a generative AI model based on prompt sentences to generate specific solutions.

[0695] To put this invention into practice, a system is constructed in which a user inputs voice using a mobile information terminal, and appropriate solutions are presented based on that input. The specific processing flow of the entire system and details of the hardware and software used are described below.

[0696] First, the user speaks into the microphone on the mobile information terminal to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi." This voice input is captured by the terminal via the microphone, which serves as a voice acquisition means.

[0697] The device then converts the user's voice into text using speech recognition technology, using voice recognition software such as the Google Cloud Speech-to-Text API, which converts the speech into text data such as "How do I set up Wi-Fi?"

[0698] The converted text data is sent from the device to a server via the Internet. The server converts the received text into a prompt for solution generation, and generates a solution using a generative AI model (e.g., OpenAI's GPT-3) based on the prompt. The prompt is converted to the format "The user is looking for information on how to set up Wi-Fi."

[0699] The solution generated using the generative AI model is sent back from the server to the device, which receives the solution and presents it to the user visually or audibly. For example, a specific solution such as "Open the Settings screen and select the Wi-Fi option" may be displayed on the device screen.

[0700] (Example)

[0701] For example, consider the case where a user speaks, "How do I set up Bluetooth?" The user's voice input is captured through the microphone of the mobile information device. The device converts this speech into text data, "How do I set up Bluetooth?", using the Google Cloud Speech-to-Text API. This text data is sent to the server, which analyzes the text and generates a specific solution, "Open the settings screen, select the Bluetooth option, and turn it on," based on a generative AI model. This solution is sent to the device and displayed to the user.

[0702] This system allows even users with little technical knowledge to easily and efficiently configure their mobile devices. The collaboration between the server and the device quickly provides users with an appropriate solution, reducing anxiety about operating the mobile device.

[0703] Example prompts to be input to the generative AI model:

[0704] Input: How do I set up Bluetooth?

[0705] Model output: Open the settings screen, select the Bluetooth option and turn it on

[0706] Thus, by implementing the present invention, a system can be realized that quickly generates and provides appropriate solutions based on voice input from a user.

[0707] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0708] Step 1:

[0709] The user speaks into the microphone of the mobile information device to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi." This voice input is captured as data to be processed in the next step.

[0710] Step 2:

[0711] The device receives voice data from the user and converts it into text data using speech recognition technology such as the Google Cloud Speech-to-Text API. The voice input "Please tell me how to set up Wi-Fi" is converted into text data "Please tell me how to set up Wi-Fi." This converted text data becomes the input for the next step.

[0712] Step 3:

[0713] The terminal transmits the converted text data to the server, where the terminal uses a secure communication protocol to ensure the security of the data while sending the text data to the server.

[0714] Step 4:

[0715] The server analyzes the received text data and converts it into a prompt for generating a solution. For example, the received text "Please tell me how to set up Wi-Fi" is converted into a prompt "The user is looking for instructions on how to set up Wi-Fi." This prompt becomes the input data for the next step.

[0716] Step 5:

[0717] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate solution from the prompt. The generative AI model inputs the prompt and generates a specific solution: "Open the Settings screen and select the Wi-Fi option." This solution becomes the output data for the next step.

[0718] Step 6:

[0719] The server sends the generated solution to the device, again using a secure communication protocol to ensure data safety. The solution arrives at the device.

[0720] Step 7:

[0721] The device will then present the received solution to the user visually or audibly, for example, by displaying a message on the device screen saying, "Open the settings screen and select the Wi-Fi option." The user can then follow the instructions to proceed with the setup.

[0722] The above is the specific processing flow of the program of this system.

[0723] (Application example 1)

[0724] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0725] Inventory management in logistics centers is a time-consuming and labor-intensive task, and efficient inventory management is required, especially when handling large quantities of items. However, it is difficult for many employees to manage inventory accurately and quickly. Furthermore, using a system that requires complex operations and knowledge can easily lead to mistakes and waste of time. This invention aims to solve these problems by providing a simple and efficient inventory management system that uses voice input.

[0726] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0727] In this invention, the server includes means for acquiring voice input, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions by voice, and means for being installed in the logistics robot to support inventory management, thereby enabling employees to easily manage inventory by voice and achieving efficient and accurate work.

[0728] "User" refers to a person who uses the system to provide voice input and obtain a solution.

[0729] "Voice input" refers to information that a user provides to the system by speaking through a microphone.

[0730] "Text" refers to character data converted from voice input using voice recognition technology.

[0731] "Analysis" is the process performed to understand text data and derive appropriate solutions.

[0732] "Solutions" refer to instructions for solving problems or operating procedures that are generated as a result of analyzing the user's voice input.

[0733] "Presenting" is the act of presenting the generated solution to the user in a visible or audible form.

[0734] "Audio presentation" refers to verbally communicating the generated solution to the user using a synthesized voice or the like.

[0735] A "logistics robot" is an automated mechanical device used to assist with inventory management, product movement, and other tasks within a logistics center.

[0736] "Installation" is the act of placing a system or software on specific hardware and making it operational.

[0737] "Inventory management" refers to the general task of monitoring and appropriately managing inventory quantities and conditions.

[0738] "Server" refers to the computer system that processes voice input from the user and performs text analysis and solution generation.

[0739] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0740] A "microphone" is a device that converts sound into an electrical signal and inputs it into a system.

[0741] This invention relates to a system that supports efficient inventory management in logistics centers. Its main components include a microphone for capturing voice input, speech recognition software for converting speech into text, natural language processing technology for analyzing text and generating solutions, speech synthesis technology for presenting solutions to users, and a logistics robot. Each component is described in detail below.

[0742] 1. Voice input

[0743] A user speaks into a microphone to describe the inventory problem or request they want to solve. This speech data is captured by speech recognition software. For example, a user might say, "Check inventory and confirm the quantity of new product."

[0744] 2. Voice Recognition

[0745] Using speech recognition software (e.g., the "speech_recognition" library) installed on the device, the user's speech is converted into text data, which generates the text information "Check inventory to see the quantity of new products."

[0746] 3. Natural Language Processing

[0747] This text data is sent to a server and analyzed by a server-side natural language processing engine (for example, Google Natural Language API). This analysis allows the system to accurately understand the user's intentions and requests and generate appropriate solutions.

[0748] 4. Solution Generation

[0749] The server generates a specific solution based on the analysis results, for example, the server generates an instruction such as "Check the inventory list and display the quantity of the new product."

[0750] 5. Providing a solution

[0751] The generated solution is converted into voice using speech synthesis technology (e.g., the "pyttsx3" library) and presented to the user via the logistics robot, allowing the user to receive the solution by voice.

[0752] 6. Logistics robots

[0753] The logistics robot assists with inventory management by performing stock checks and quantity confirmations according to generated instructions and reporting the results to the user.

[0754] Specific examples

[0755] For example, if a logistics center staff member voice-inputs, "Check inventory and confirm new product quantities," the voice is captured by a microphone and converted into text via speech recognition software. The text data is sent to a server and analyzed by a natural language processing engine. The server generates a solution, "Check inventory list and display new product quantities," and presents it to the user using speech synthesis technology. This series of steps enables fast and efficient inventory management.

[0756] Prompt Sentence Examples

[0757] Example user input: "Check inventory to see new product quantities."

[0758] Example server response: "Check inventory list and display new product quantities."

[0759] In this way, the present invention can improve the efficiency of inventory management in a logistics center through voice input, thereby improving work accuracy and reducing labor.

[0760] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0761] Step 1:

[0762] The user inputs instructions by voice.

[0763] Input: User's spoken input (e.g., "Check inventory to see new product quantities")

[0764] Output: Audio data

[0765] Specific operation: The user verbally inputs instructions regarding inventory management to the logistics robot through a microphone.

[0766] Step 2:

[0767] The device converts the voice input into text.

[0768] Input: Audio data

[0769] Output: Text data (e.g., "Check inventory to see new product quantities")

[0770] Specific operation: Using speech recognition software (the "speech_recognition" library), the voice data is analyzed and converted into appropriate text data.

[0771] Step 3:

[0772] The terminal transmits the text data to the server.

[0773] Input: Text data

[0774] Output: Send text data to the server

[0775] Specific operation: The terminal sends the converted text data to the server using a protocol such as an HTTP request.

[0776] Step 4:

[0777] The server analyzes the text data and generates solutions.

[0778] Input: Text data (e.g., "Check inventory to see new product quantities")

[0779] Output: Solution (e.g. "Check the inventory list and display the quantity of the new product")

[0780] Specific operation: The server uses a natural language processing engine (such as the Google Natural Language API) to analyze the text data, understand the user's intent, and generate an appropriate solution.

[0781] Step 5:

[0782] The server sends the solution to the device.

[0783] Input: Solution

[0784] Output: Solution sent to terminal

[0785] Specific operation: The server returns the generated solution to the terminal as text data.

[0786] Step 6:

[0787] The device will vocalize the solution and present it to the user.

[0788] Input: Solution (e.g. "Check the inventory list and display the new product quantity")

[0789] Output: A spoken solution

[0790] Specific operation: Using the speech synthesis technology ("pyttsx3" library) installed on the device, the solution is output aloud and communicated to the user.

[0791] Step 7:

[0792] Logistics robots perform inventory management tasks.

[0793] Input: Instructions for implementing the solution

[0794] Output: Stock check result

[0795] Specific operation: The logistics robot performs actual inventory checks according to instructions and reports the results to the user.

[0796] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0797] The present invention is a system including means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions to the user, and an emotion engine for acquiring the user's emotions.

[0798] The user speaks into the microphone of their mobile phone, saying something like, "Please tell me how to set up Wi-Fi." This voice input is picked up by the device's microphone. The device then converts the acquired voice data into text data using voice recognition technology. During this conversion process, voice recognition technology is used to convert the voice into character data, making it possible to identify the user's request as text.

[0799] The converted text data is sent to a server via the Internet. The server then analyzes the text data using natural language processing technology. Specifically, for example, from the text "Please tell me how to set up Wi-Fi," the server identifies that the user is looking for information about Wi-Fi settings.

[0800] The server also includes an emotion engine that identifies the user's emotions. The emotion engine analyzes elements of the voice data, such as tone, speed, and pauses, to recognize the user's emotional state (e.g., irritation, confusion, excitement, etc.). This emotional information is used to devise solutions.

[0801] The server reflects the user's emotional information acquired by the emotion engine and generates a more appropriate solution. For example, if the user is confused, it generates detailed, step-by-step instructions. Specifically, if the emotion engine identifies the user's confusion, the server can create more friendly and detailed instructions, such as "First, open the settings screen, and then select the Wi-Fi option."

[0802] The generated solution is sent to the device as text data again. The device receives this text data and displays the solution to the user. The user configures the Wi-Fi by following the solution displayed on the device screen.

[0803] For example, if a user says, "How do I set up Bluetooth?", the device converts this speech into text. The text is sent to the server, which generates instructions such as, "Open the Settings screen, select the Bluetooth option, and turn it on." If the emotion engine detects the user's confusion, it generates additional detailed instructions, such as, "You can find the Settings screen by swiping right from the home screen," and sends these to the device for display.

[0804] This system allows users to receive appropriate support for setting up and operating their mobile phones through voice input, and also provides detailed support according to their emotional state. As a result, users will be able to reduce their anxiety about operating their mobile phones and set them up more smoothly.

[0805] The processing flow will be explained below.

[0806] Step 1:

[0807] The user speaks into the microphone of the mobile phone. For example, the user says, "Please tell me how to set up Wi-Fi."

[0808] Step 2:

[0809] The device uses a microphone to capture the user's voice data, which is a recording of the user's speech.

[0810] Step 3:

[0811] The device uses voice recognition technology to convert the acquired voice data into text data, resulting in the voice being converted into text saying, "Please tell me how to set up Wi-Fi."

[0812] Step 4:

[0813] The terminal transmits the converted text data to a server via the Internet, where the text data is further analyzed.

[0814] Step 5:

[0815] The server uses natural language processing technology to analyze the content of the text data. For example, from the text "Please tell me how to set up Wi-Fi," it understands that the user is looking for information about Wi-Fi settings.

[0816] Step 6:

[0817] At the same time, the emotion engine installed on the server analyzes the voice data and recognizes the user's emotions, such as tone of voice, speed, and pauses, to determine whether the user is confused or annoyed.

[0818] Step 7:

[0819] The server generates the optimal solution based on the analysis results and emotional information according to the user's request. For example, if the user is confused, it generates detailed instructions such as "Open the settings screen and select the Wi-Fi option."

[0820] Step 8:

[0821] The server sends the generated solution to the terminal as text data, and the terminal receives the solution from the server.

[0822] Step 9:

[0823] The device visually displays the received text data to the user, specifically displaying the message "Open Settings and select the Wi-Fi option" on the device screen.

[0824] Step 10:

[0825] The user follows the instructions displayed on the device screen to set up and operate the mobile phone. For example, the user can set up Wi-Fi by opening the settings screen and selecting the Wi-Fi option.

[0826] Through this series of steps, users can receive appropriate support for setting up and operating their mobile phone through voice input. Furthermore, the introduction of an emotion engine provides detailed support according to the user's emotional state, reducing stress and anxiety when operating the device.

[0827] Example 2

[0828] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0829] Conventional technologies have had difficulty providing appropriate solutions, particularly in response to the user's emotional state, when users obtain information using voice input. This often leaves users feeling confused and anxious. Furthermore, when users need detailed guidance, they are unable to obtain appropriate and prompt assistance.

[0830] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0831] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating an appropriate solution, means for presenting the solution to the user, means for identifying the user's emotion, and means for adjusting the solution based on the result of identifying the emotion. This makes it possible to provide an appropriate solution according to the user's emotional state based on the information acquired through the voice input, reduce the user's confusion and anxiety, and provide quick and detailed guidance.

[0832] "Voice input" is data that electronically captures the words and sounds spoken by a user.

[0833] "Text conversion means" refers to technology or devices for converting acquired voice data into text data.

[0834] "Means for generating appropriate solutions" refers to technologies and devices that provide the information and instructions desired by the user based on the analyzed text data.

[0835] "Presentation means" refers to the techniques or devices used to visually or audibly communicate the generated solution to the user.

[0836] An "emotion identification means" is a technique or device that identifies a user's emotional state (e.g., confusion, excitement, irritation, etc.) from the user's voice data or other input.

[0837] The "means for adjusting a solution" refers to a technique or device for changing the content and presentation method of the solution provided according to the identified emotional state of the user.

[0838] The present invention is a system that receives voice input from a user, converts the voice into text, analyzes the text, and presents appropriate solutions. It also has the ability to identify the user's emotions and adjust solutions based on the emotional information. Detailed embodiments are described below.

[0839] Acquiring voice input

[0840] The user speaks into the device's microphone, for example, "Please tell me how to set up Wi-Fi." The device uses a high-precision microphone to capture this voice, which is then captured as digital data.

[0841] Speech recognition and text conversion

[0842] The device uses voice recognition technology to convert the captured voice data into text data. Specifically, it uses voice recognition software such as the Google Speech-to-Text API to convert the user's voice into a string of characters. For example, the voice saying "Please tell me how to set up Wi-Fi" is converted into text "Please tell me how to set up Wi-Fi."

[0843] Sending text data to the server

[0844] The converted text data is then sent to a server via the Internet. The device uses the HTTPS protocol to transfer the text data to the server securely and quickly.

[0845] Text data analysis

[0846] The server uses natural language processing technology to analyze the text data it receives. For example, a generative AI model such as OpenAI GPT-4 is used. The server analyzes the text data, understands the user's request, and determines what the user specifically wants from the text "Please tell me how to set up Wi-Fi."

[0847] Identifying emotions

[0848] The server is equipped with an emotion engine that analyzes parameters such as tone, speed, and pauses in the voice data to identify the user's emotional state. For example, it analyzes whether the user is confused or irritated and recognizes that emotional state.

[0849] Solution generation and coordination

[0850] The server takes into account the emotional information obtained by the emotion engine and generates an appropriate solution. The generated solution is provided to the user in an easy-to-understand manner using natural language generation technology. If the user is confused, a detailed guide is generated. For example, step-by-step instructions are provided, such as "First, open the settings screen, then select the Wi-Fi option."

[0851] Submitting and Viewing Solutions

[0852] The generated solution is sent to the device as text data, and the device displays the solution to the user visually or audibly. The user can then configure the Wi-Fi by following the instructions displayed on the device screen.

[0853] Examples of concrete examples and prompts

[0854] As a concrete example, consider the case where a user speaks, "How do I set up Bluetooth?" The device converts this speech into text and sends it to the server. The server generates instructions such as, "Open the Settings screen, select the Bluetooth option, and turn it on." If the emotion engine detects the user's confusion, it will generate additional detailed instructions, such as, "You can find the Settings screen by swiping right from the home screen," and send these to the device for display.

[0855] Prompt Sentence Examples

[0856] User Input: "How do I set up Bluetooth?"

[0857] User's emotional state: "Confused"

[0858] Goal: Provide users with clear, detailed instructions on how to set up Bluetooth.

[0859] output:

[0860] Open the Settings screen, select the Bluetooth option, and switch it on. You can find the Settings screen by swiping right from the home screen.

[0861] This system allows users to receive appropriate support for setting up and operating their mobile devices through voice input. Furthermore, by providing detailed support according to the user's emotional state, it is possible to reduce the user's anxiety and achieve smoother operation.

[0862] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0863] Step 1:

[0864] The user speaks into the device's microphone, saying something like, "Please tell me how to set up Wi-Fi."

[0865] Input: User voice input

[0866] Output: Audio data

[0867] What it does: The device's microphone picks up the audio and stores it as digital audio data.

[0868] Step 2:

[0869] The device uses voice recognition technology to convert voice data into text data, specifically using the Google Speech-to-Text API.

[0870] Input: Audio data

[0871] Output: Text data

[0872] Specific operation: Call the speech recognition API and send the speech data to the API. Retrieve the text data returned from the API. The speech "Please tell me how to set up Wi-Fi" is converted into text "Please tell me how to set up Wi-Fi."

[0873] Step 3:

[0874] The terminal transmits the converted text data to a server via the Internet.

[0875] Input: Text data

[0876] Output: HTTP request to the server

[0877] Specific operation: Generates an HTTPS request and securely sends the converted text data to the server.

[0878] Step 4:

[0879] The server analyzes the received text data using natural language processing technology (e.g., OpenAI GPT-4).

[0880] Input: Text data

[0881] Output: Data that recognizes the user's request

[0882] Specific operation: The natural language processing engine is started and the text is analyzed. As a result, the specific request "Set up Wi-Fi" is recognized.

[0883] Step 5:

[0884] The server analyzes the tone, speed, and timing of the voice data using an emotion engine to identify the user's emotional state.

[0885] Input: Audio parameters

[0886] Output: Emotional state (e.g., confused, annoyed, etc.)

[0887] What it does: It uses an emotion engine to analyze multiple parameters of the voice data to identify the user's emotional state.

[0888] Step 6:

[0889] The server uses a generative AI model to generate appropriate solutions based on the text and emotional state.

[0890] Input: User's request, emotional state

[0891] Output: Solution text

[0892] What it does: It uses a natural language generation engine to generate solutions based on the user's request and emotional state. It provides detailed, step-by-step instructions for confused users. For example, "First, open Settings, then select the Wi-Fi option."

[0893] Step 7:

[0894] The server transmits the generated solution to the terminal again as text data.

[0895] Input: Solution text

[0896] Output: HTTP response to the device

[0897] Specific behavior: Generate an HTTP response containing the generated solution text and send it to the terminal.

[0898] Step 8:

[0899] The terminal displays the received solution text to the user.

[0900] Input: Solution text

[0901] Output: Displayed on the user interface

[0902] Specific operation: The received solution text is displayed on the device screen, visually communicating the solution to the user.

[0903] (Application example 2)

[0904] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0905] Conventional customer service in brick-and-mortar stores has difficulty instantly understanding customers' voice-generated questions and requests and providing appropriate solutions. Furthermore, it has been impossible to respond while taking into account the customer's emotional state, limiting the improvement of customer satisfaction. There is a need for a system that can solve these problems, provide appropriate solutions in response to customer requests in real time, and provide support that takes into account the customer's emotional state.

[0906] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0907] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions to the user, means for analyzing the user's emotions, and means for adjusting the solutions based on the results of the emotion analysis. This makes it possible to understand customer questions and requests voiced in real time even in a physical store, and to provide appropriate services that take into account the customer's emotional state.

[0908] "Voice input" refers to voice data uttered by a user and acquired through a microphone.

[0909] "Text conversion" is the process of converting captured audio data into text data.

[0910] "Solution generation" is the process of generating appropriate solutions based on the analyzed text data in response to the user's requests.

[0911] "Emotion analysis" is the process of analyzing the tone, rate, and pauses of audio data to identify the user's emotional state.

[0912] "Smart glasses" are eyeglass-type devices that have the ability to display information and receive voice input.

[0913] A "smartphone" is a portable information terminal that has the ability to display information and receive voice input.

[0914] "Natural language processing technology" is a technology for analyzing text data and understanding and generating human language.

[0915] "Means for presenting to the user" refers to the method by which the generated solution is presented to the user visually or audibly.

[0916] "Adjustment based on emotion analysis results" is a process of adjusting the content of the solution and the method of providing it, reflecting the user's emotional information obtained through emotion analysis.

[0917] A "microphone" is an acoustic device for acquiring audio data.

[0918] This invention is a system for improving the efficiency of customer service in brick-and-mortar stores and responding to customer voice inquiries and requests in real time. The system includes means for acquiring voice input from a user, means for converting the acquired voice data into text data, means for analyzing the converted text data to generate appropriate solutions, means for presenting the solutions to the user, means for analyzing the user's emotions, and means for adjusting the solutions based on the results of the emotion analysis.

[0919] Hardware / Software used

[0920] The hardware used includes smart glasses and a smartphone, which are used to capture voice input and present information.

[0921] The software uses the Google Cloud Speech-to-Text API to convert voice data into text, and a natural language processing engine (e.g., Google Natural Language API, OpenAI GPT-4) to analyze text data. It also integrates an emotion engine to analyze user emotions.

[0922] System operation explanation

[0923] 1. Voice input acquisition:

[0924] The system first receives voice input from the user using the microphone in the smart glasses or smartphone. When the customer speaks a question or request, the voice data is sent to the device.

[0925] 2. Audio to text conversion:

[0926] The acquired voice data is converted into text data in real time using the Google Cloud Speech-to-Text API, etc. At this stage, voice recognition technology is utilized to convert the user's speech into text.

[0927] 3. Text data analysis:

[0928] The converted text data is sent to a cloud server and analyzed by a natural language processing engine (Google Natural Language API, OpenAI GPT-4). The server then understands the user's question or request and generates an appropriate solution.

[0929] 4. Emotion analysis:

[0930] The server is integrated with an emotion engine that analyzes the customer's emotional state from the tone, speed, and pauses of the voice data, thereby extracting information such as whether the customer is confused or excited.

[0931] 5. Coordinating and presenting solutions:

[0932] Solutions are generated as text data and adjusted based on sentiment analysis. For example, if the customer is confused, more detailed instructions will be included. The generated solutions are displayed on the screen of the smart glasses or smartphone, and staff can assist the customer based on the solution.

[0933] Specific examples

[0934] For example, if a customer asks, "Where is this item?", the system captures voice input and converts it to text in real time. The text data is then analyzed on a cloud server to generate product location instructions. If sentiment analysis determines that the customer is confused, additional details such as "Section A is on the right, straight ahead from the entrance" are provided. This allows customers to receive prompt and accurate guidance.

[0935] Prompt Sentence Examples

[0936] "Please provide an appropriate response to the following question: Where is this item?"

[0937] In this way, this system will improve the efficiency of customer service in physical stores and increase customer satisfaction.

[0938] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0939] Step 1:

[0940] The device receives voice input from the user. The input voice data is collected through the microphone of smart glasses or a smartphone. Specifically, the voice data is generated when the user speaks a question or request.

[0941] Step 2:

[0942] The device converts the collected voice data into text data using the Google Cloud Speech-to-Text API. The input for this process is voice data, and the output is text data. Specifically, the speech recognition engine combines acoustic and language models to analyze the voice signal and generate corresponding text.

[0943] Step 3:

[0944] The device sends the converted text data to a cloud server. The server uses the received text data as input and analyzes it using a natural language processing engine (e.g., Google Natural Language API or OpenAI GPT-4). The output is an initial solution proposal based on the user's question or request. Specifically, the natural language processing engine understands the meaning of the text and extracts relevant information.

[0945] Step 4:

[0946] The server inputs voice data and analyzes it using an emotion engine to determine the user's emotional state. The output is emotional information such as whether the user is distressed, angry, or happy. Specifically, the emotion analysis engine analyzes parameters such as tone, speed, and pauses in the voice and identifies the user's emotional state from the results.

[0947] Step 5:

[0948] The server adjusts the initial solution based on the sentiment analysis results. The input is the initial solution and sentiment information, and the output is the adjusted solution. Specifically, if the user is confused, for example, the server adds more detailed instructions and explanations to the solution. Also, if the user is in a hurry, the server prioritizes a quick solution.

[0949] Step 6:

[0950] The server sends the final solution to the device, which receives it and presents it to the user. Specifically, it displays it as text on the screen of smart glasses or a smartphone, or plays it as audio guidance. This allows the user to receive the appropriate solution in real time.

[0951] In this way, the system can respond to user requests quickly and accurately through the connection between the terminal and the server. In addition, by combining this with emotion analysis, it can provide a more friendly and considerate response to the user.

[0952] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0953] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0954] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0955] [Fourth embodiment]

[0956] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0957] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0958] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0959] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0960] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0961] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0962] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0963] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0964] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0965] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0966] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0967] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0968] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0969] Implementing this invention involves four main steps: a user inputting voice using a mobile phone, a terminal converting the voice into text, a server analyzing the text and generating a solution, and finally a server presenting the solution to the user.

[0970] First, the user speaks into the microphone on their mobile phone to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi."

[0971] The device then converts this voice input into text using speech recognition technology, converting the user's voice into text data such as "Please tell me how to set up Wi-Fi."

[0972] The converted text is sent to a server, which uses natural language processing techniques to analyze it and generate the specific solution the user is looking for. For example, the server might generate instructions like, "Open Settings and select the Wi-Fi option."

[0973] Finally, the terminal receives the solution from the server and presents it to the user, who can then configure the mobile phone according to the displayed solution.

[0974] For example, if a user speaks, "How do I set up Bluetooth?", the device converts this speech into text. The text is sent to a server, which then generates instructions such as, "Open the settings screen, select the Bluetooth option, and turn it on." The user can then follow the instructions to set up Bluetooth.

[0975] This system allows even users with little technical knowledge to easily and efficiently configure their mobile phones. By linking the server and the device, the system quickly provides users with an appropriate solution, reducing their anxiety about operating their mobile phones.

[0976] The processing flow will be explained below.

[0977] Step 1:

[0978] The user speaks into the microphone of the mobile phone. For example, the user says, "Please tell me how to set up Wi-Fi."

[0979] Step 2:

[0980] The device captures voice data through a microphone. This voice data is a direct recording of the user's speech.

[0981] Step 3:

[0982] The device uses voice recognition technology to convert voice data into text data. At this time, the voice input "Please tell me how to set up Wi-Fi" is converted into text format.

[0983] Step 4:

[0984] The terminal transmits the converted text data to a server via the Internet, where the text data is further analyzed.

[0985] Step 5:

[0986] The server uses natural language processing technology to understand the content of the text data and identify the user's requests and problems. For example, from the text "Please tell me how to set up Wi-Fi," it can analyze that the user is looking for information about Wi-Fi settings.

[0987] Step 6:

[0988] Based on the analysis, the server generates the optimal solution for the user's request, such as "Open the settings screen and select the Wi-Fi option."

[0989] Step 7:

[0990] The server sends the generated solution to the terminal as text data, and the terminal receives the solution from the server.

[0991] Step 8:

[0992] The device visually displays the received text data to the user. Specifically, the device displays the message "Open Settings and select the Wi-Fi option."

[0993] Step 9:

[0994] The user follows the instructions displayed on the device screen to set up and operate the mobile phone. For example, the user can set up Wi-Fi by opening the settings screen and selecting the Wi-Fi option.

[0995] These steps allow the user to receive appropriate support for setting up and operating the mobile phone through voice input.

[0996] Example 1

[0997] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0998] Existing systems have a problem in that it is difficult to provide a quick and accurate solution when a user asks about a technical problem or setting method. In particular, it is difficult to properly understand the user's request entered by voice and provide a specific solution based on that. As a result, users who lack technical knowledge often find it time-consuming to set up or solve a problem, which can be frustrating.

[0999] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1000] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for converting the text into prompt sentences for solution generation, means for generating solutions from the prompt sentences using a generative AI model, and means for presenting the solutions to the user. This makes it possible to generate prompt sentences from text and generate accurate solutions using the generative AI model, and quickly present them to the user.

[1001] The "means for obtaining voice input from a user" refers to a device or method for obtaining information input by voice from a user.

[1002] "Means for converting voice input into text" refers to technology or devices for converting acquired voice data into character data.

[1003] The "means for analyzing text and generating appropriate solutions" is a device or method that analyzes the converted text and creates appropriate solutions for the user based on the results of the analysis.

[1004] The "means for presenting a solution to a user" refers to a device or method for presenting the generated solution to a user in an easy-to-understand manner, and includes screen display, audio output, and the like.

[1005] "Means for converting text into prompts for solution generation" refers to technology or devices for converting analyzed text data into prompts in a format that is easy for the generative AI model to understand.

[1006] "Means for generating solutions from prompt sentences using a generative AI model" refers to technology or devices that utilize a generative AI model based on prompt sentences to generate specific solutions.

[1007] To put this invention into practice, a system is constructed in which a user inputs voice using a mobile information terminal, and appropriate solutions are presented based on that input. The specific processing flow of the entire system and details of the hardware and software used are described below.

[1008] First, the user speaks into the microphone on the mobile information terminal to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi." This voice input is captured by the terminal via the microphone, which serves as a voice acquisition means.

[1009] The device then converts the user's voice into text using speech recognition technology, using voice recognition software such as the Google Cloud Speech-to-Text API, which converts the speech into text data such as "How do I set up Wi-Fi?"

[1010] The converted text data is sent from the device to a server via the Internet. The server converts the received text into a prompt for solution generation, and generates a solution using a generative AI model (e.g., OpenAI's GPT-3) based on the prompt. The prompt is converted to the format "The user is looking for information on how to set up Wi-Fi."

[1011] The solution generated using the generative AI model is sent back from the server to the device, which receives the solution and presents it to the user visually or audibly. For example, a specific solution such as "Open the Settings screen and select the Wi-Fi option" may be displayed on the device screen.

[1012] (Example)

[1013] For example, consider the case where a user speaks, "How do I set up Bluetooth?" The user's voice input is captured through the microphone of the mobile information device. The device converts this speech into text data, "How do I set up Bluetooth?", using the Google Cloud Speech-to-Text API. This text data is sent to the server, which analyzes the text and generates a specific solution, "Open the settings screen, select the Bluetooth option, and turn it on," based on a generative AI model. This solution is sent to the device and displayed to the user.

[1014] This system allows even users with little technical knowledge to easily and efficiently configure their mobile devices. The collaboration between the server and the device quickly provides users with an appropriate solution, reducing anxiety about operating the mobile device.

[1015] Example prompts to be input to the generative AI model:

[1016] Input: How do I set up Bluetooth?

[1017] Model output: Open the settings screen, select the Bluetooth option and turn it on

[1018] Thus, by implementing the present invention, a system can be realized that quickly generates and provides appropriate solutions based on voice input from a user.

[1019] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1020] Step 1:

[1021] The user speaks into the microphone of the mobile information device to describe the problem they want to solve or the setting they want to change. For example, the user might say, "Please tell me how to set up Wi-Fi." This voice input is captured as data to be processed in the next step.

[1022] Step 2:

[1023] The device receives voice data from the user and converts it into text data using speech recognition technology such as the Google Cloud Speech-to-Text API. The voice input "Please tell me how to set up Wi-Fi" is converted into text data "Please tell me how to set up Wi-Fi." This converted text data becomes the input for the next step.

[1024] Step 3:

[1025] The terminal transmits the converted text data to the server, where the terminal uses a secure communication protocol to ensure the security of the data while sending the text data to the server.

[1026] Step 4:

[1027] The server analyzes the received text data and converts it into a prompt for generating a solution. For example, the received text "Please tell me how to set up Wi-Fi" is converted into a prompt "The user is looking for instructions on how to set up Wi-Fi." This prompt becomes the input data for the next step.

[1028] Step 5:

[1029] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate solution from the prompt. The generative AI model inputs the prompt and generates a specific solution: "Open the Settings screen and select the Wi-Fi option." This solution becomes the output data for the next step.

[1030] Step 6:

[1031] The server sends the generated solution to the device, again using a secure communication protocol to ensure data safety. The solution arrives at the device.

[1032] Step 7:

[1033] The device will then present the received solution to the user visually or audibly, for example, by displaying a message on the device screen saying, "Open the settings screen and select the Wi-Fi option." The user can then follow the instructions to proceed with the setup.

[1034] The above is the specific processing flow of the program of this system.

[1035] (Application example 1)

[1036] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1037] Inventory management in logistics centers is a time-consuming and labor-intensive task, and efficient inventory management is required, especially when handling large quantities of items. However, it is difficult for many employees to manage inventory accurately and quickly. Furthermore, using a system that requires complex operations and knowledge can easily lead to mistakes and waste of time. This invention aims to solve these problems by providing a simple and efficient inventory management system that uses voice input.

[1038] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1039] In this invention, the server includes means for acquiring voice input, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions by voice, and means for being installed in the logistics robot to support inventory management, thereby enabling employees to easily manage inventory by voice and achieving efficient and accurate work.

[1040] "User" refers to a person who uses the system to provide voice input and obtain a solution.

[1041] "Voice input" refers to information that a user provides to the system by speaking through a microphone.

[1042] "Text" refers to character data converted from voice input using voice recognition technology.

[1043] "Analysis" is the process performed to understand text data and derive appropriate solutions.

[1044] "Solutions" refer to instructions for solving problems or operating procedures that are generated as a result of analyzing the user's voice input.

[1045] "Presenting" is the act of presenting the generated solution to the user in a visible or audible form.

[1046] "Audio presentation" refers to verbally communicating the generated solution to the user using a synthesized voice or the like.

[1047] A "logistics robot" is an automated mechanical device used to assist with inventory management, product movement, and other tasks within a logistics center.

[1048] "Installation" is the act of placing a system or software on specific hardware and making it operational.

[1049] "Inventory management" refers to the general task of monitoring and appropriately managing inventory quantities and conditions.

[1050] "Server" refers to the computer system that processes voice input from the user and performs text analysis and solution generation.

[1051] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[1052] A "microphone" is a device that converts sound into an electrical signal and inputs it into a system.

[1053] This invention relates to a system that supports efficient inventory management in logistics centers. Its main components include a microphone for capturing voice input, speech recognition software for converting speech into text, natural language processing technology for analyzing text and generating solutions, speech synthesis technology for presenting solutions to users, and a logistics robot. Each component is described in detail below.

[1054] 1. Voice input

[1055] A user speaks into a microphone to describe the inventory problem or request they want to solve. This speech data is captured by speech recognition software. For example, a user might say, "Check inventory and confirm the quantity of new product."

[1056] 2. Voice Recognition

[1057] Using speech recognition software (e.g., the "speech_recognition" library) installed on the device, the user's speech is converted into text data, which generates the text information "Check inventory to see the quantity of new products."

[1058] 3. Natural Language Processing

[1059] This text data is sent to a server and analyzed by a server-side natural language processing engine (for example, Google Natural Language API). This analysis allows the system to accurately understand the user's intentions and requests and generate appropriate solutions.

[1060] 4. Solution Generation

[1061] The server generates a specific solution based on the analysis results, for example, the server generates an instruction such as "Check the inventory list and display the quantity of the new product."

[1062] 5. Providing a solution

[1063] The generated solution is converted into voice using speech synthesis technology (e.g., the "pyttsx3" library) and presented to the user via the logistics robot, allowing the user to receive the solution by voice.

[1064] 6. Logistics robots

[1065] The logistics robot assists with inventory management by performing stock checks and quantity confirmations according to generated instructions and reporting the results to the user.

[1066] Specific examples

[1067] For example, if a logistics center staff member voice-inputs, "Check inventory and confirm new product quantities," the voice is captured by a microphone and converted into text via speech recognition software. The text data is sent to a server and analyzed by a natural language processing engine. The server generates a solution, "Check inventory list and display new product quantities," and presents it to the user using speech synthesis technology. This series of steps enables fast and efficient inventory management.

[1068] Prompt Sentence Examples

[1069] Example user input: "Check inventory to see new product quantities."

[1070] Example server response: "Check inventory list and display new product quantities."

[1071] In this way, the present invention can improve the efficiency of inventory management in a logistics center through voice input, thereby improving work accuracy and reducing labor.

[1072] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1073] Step 1:

[1074] The user inputs instructions by voice.

[1075] Input: User's spoken input (e.g., "Check inventory to see new product quantities")

[1076] Output: Audio data

[1077] Specific operation: The user verbally inputs instructions regarding inventory management to the logistics robot through a microphone.

[1078] Step 2:

[1079] The device converts the voice input into text.

[1080] Input: Audio data

[1081] Output: Text data (e.g., "Check inventory to see new product quantities")

[1082] Specific operation: Using speech recognition software (the "speech_recognition" library), the voice data is analyzed and converted into appropriate text data.

[1083] Step 3:

[1084] The terminal transmits the text data to the server.

[1085] Input: Text data

[1086] Output: Send text data to the server

[1087] Specific operation: The terminal sends the converted text data to the server using a protocol such as an HTTP request.

[1088] Step 4:

[1089] The server analyzes the text data and generates solutions.

[1090] Input: Text data (e.g., "Check inventory to see new product quantities")

[1091] Output: Solution (e.g. "Check the inventory list and display the quantity of the new product")

[1092] Specific operation: The server uses a natural language processing engine (such as the Google Natural Language API) to analyze the text data, understand the user's intent, and generate an appropriate solution.

[1093] Step 5:

[1094] The server sends the solution to the device.

[1095] Input: Solution

[1096] Output: Solution sent to terminal

[1097] Specific operation: The server returns the generated solution to the terminal as text data.

[1098] Step 6:

[1099] The device will vocalize the solution and present it to the user.

[1100] Input: Solution (e.g. "Check the inventory list and display the new product quantity")

[1101] Output: A spoken solution

[1102] Specific operation: Using the speech synthesis technology ("pyttsx3" library) installed on the device, the solution is output aloud and communicated to the user.

[1103] Step 7:

[1104] Logistics robots perform inventory management tasks.

[1105] Input: Instructions for implementing the solution

[1106] Output: Stock check result

[1107] Specific operation: The logistics robot performs actual inventory checks according to instructions and reports the results to the user.

[1108] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1109] The present invention is a system including means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions to the user, and an emotion engine for acquiring the user's emotions.

[1110] The user speaks into the microphone of their mobile phone, saying something like, "Please tell me how to set up Wi-Fi." This voice input is picked up by the device's microphone. The device then converts the acquired voice data into text data using voice recognition technology. During this conversion process, voice recognition technology is used to convert the voice into character data, making it possible to identify the user's request as text.

[1111] The converted text data is sent to a server via the Internet. The server then analyzes the text data using natural language processing technology. Specifically, for example, from the text "Please tell me how to set up Wi-Fi," the server identifies that the user is looking for information about Wi-Fi settings.

[1112] The server also includes an emotion engine that identifies the user's emotions. The emotion engine analyzes elements of the voice data, such as tone, speed, and pauses, to recognize the user's emotional state (e.g., irritation, confusion, excitement, etc.). This emotional information is used to devise solutions.

[1113] The server reflects the user's emotional information acquired by the emotion engine and generates a more appropriate solution. For example, if the user is confused, it generates detailed, step-by-step instructions. Specifically, if the emotion engine identifies the user's confusion, the server can create more friendly and detailed instructions, such as "First, open the settings screen, and then select the Wi-Fi option."

[1114] The generated solution is sent to the device as text data again. The device receives this text data and displays the solution to the user. The user configures the Wi-Fi by following the solution displayed on the device screen.

[1115] For example, if a user says, "How do I set up Bluetooth?", the device converts this speech into text. The text is sent to the server, which generates instructions such as, "Open the Settings screen, select the Bluetooth option, and turn it on." If the emotion engine detects the user's confusion, it generates additional detailed instructions, such as, "You can find the Settings screen by swiping right from the home screen," and sends these to the device for display.

[1116] This system allows users to receive appropriate support for setting up and operating their mobile phones through voice input, and also provides detailed support according to their emotional state. As a result, users will be able to reduce their anxiety about operating their mobile phones and set them up more smoothly.

[1117] The processing flow will be explained below.

[1118] Step 1:

[1119] The user speaks into the microphone of the mobile phone. For example, the user says, "Please tell me how to set up Wi-Fi."

[1120] Step 2:

[1121] The device uses a microphone to capture the user's voice data, which is a recording of the user's speech.

[1122] Step 3:

[1123] The device uses voice recognition technology to convert the acquired voice data into text data, resulting in the voice being converted into text saying, "Please tell me how to set up Wi-Fi."

[1124] Step 4:

[1125] The terminal transmits the converted text data to a server via the Internet, where the text data is further analyzed.

[1126] Step 5:

[1127] The server uses natural language processing technology to analyze the content of the text data. For example, from the text "Please tell me how to set up Wi-Fi," it understands that the user is looking for information about Wi-Fi settings.

[1128] Step 6:

[1129] At the same time, the emotion engine installed on the server analyzes the voice data and recognizes the user's emotions, such as tone of voice, speed, and pauses, to determine whether the user is confused or annoyed.

[1130] Step 7:

[1131] The server generates the optimal solution based on the analysis results and emotional information according to the user's request. For example, if the user is confused, it generates detailed instructions such as "Open the settings screen and select the Wi-Fi option."

[1132] Step 8:

[1133] The server sends the generated solution to the terminal as text data, and the terminal receives the solution from the server.

[1134] Step 9:

[1135] The device visually displays the received text data to the user, specifically displaying the message "Open Settings and select the Wi-Fi option" on the device screen.

[1136] Step 10:

[1137] The user follows the instructions displayed on the device screen to set up and operate the mobile phone. For example, the user can set up Wi-Fi by opening the settings screen and selecting the Wi-Fi option.

[1138] Through this series of steps, users can receive appropriate support for setting up and operating their mobile phone through voice input. Furthermore, the introduction of an emotion engine provides detailed support according to the user's emotional state, reducing stress and anxiety when operating the device.

[1139] Example 2

[1140] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1141] Conventional technologies have had difficulty providing appropriate solutions, particularly in response to the user's emotional state, when users obtain information using voice input. This often leaves users feeling confused and anxious. Furthermore, when users need detailed guidance, they are unable to obtain appropriate and prompt assistance.

[1142] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1143] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating an appropriate solution, means for presenting the solution to the user, means for identifying the user's emotion, and means for adjusting the solution based on the result of identifying the emotion. This makes it possible to provide an appropriate solution according to the user's emotional state based on the information acquired through the voice input, reduce the user's confusion and anxiety, and provide quick and detailed guidance.

[1144] "Voice input" is data that electronically captures the words and sounds spoken by a user.

[1145] "Text conversion means" refers to technology or devices for converting acquired voice data into text data.

[1146] "Means for generating appropriate solutions" refers to technologies and devices that provide the information and instructions desired by the user based on the analyzed text data.

[1147] "Presentation means" refers to the techniques or devices used to visually or audibly communicate the generated solution to the user.

[1148] An "emotion identification means" is a technique or device that identifies a user's emotional state (e.g., confusion, excitement, irritation, etc.) from the user's voice data or other input.

[1149] The "means for adjusting a solution" refers to a technique or device for changing the content and presentation method of the solution provided according to the identified emotional state of the user.

[1150] The present invention is a system that receives voice input from a user, converts the voice into text, analyzes the text, and presents appropriate solutions. It also has the ability to identify the user's emotions and adjust solutions based on the emotional information. Detailed embodiments are described below.

[1151] Acquiring voice input

[1152] The user speaks into the device's microphone, for example, "Please tell me how to set up Wi-Fi." The device uses a high-precision microphone to capture this voice, which is then captured as digital data.

[1153] Speech recognition and text conversion

[1154] The device uses voice recognition technology to convert the captured voice data into text data. Specifically, it uses voice recognition software such as the Google Speech-to-Text API to convert the user's voice into a string of characters. For example, the voice saying "Please tell me how to set up Wi-Fi" is converted into text "Please tell me how to set up Wi-Fi."

[1155] Sending text data to the server

[1156] The converted text data is then sent to a server via the Internet. The device uses the HTTPS protocol to transfer the text data to the server securely and quickly.

[1157] Text data analysis

[1158] The server uses natural language processing technology to analyze the text data it receives. For example, a generative AI model such as OpenAI GPT-4 is used. The server analyzes the text data, understands the user's request, and determines what the user specifically wants from the text "Please tell me how to set up Wi-Fi."

[1159] Identifying emotions

[1160] The server is equipped with an emotion engine that analyzes parameters such as tone, speed, and pauses in the voice data to identify the user's emotional state. For example, it analyzes whether the user is confused or irritated and recognizes that emotional state.

[1161] Solution generation and coordination

[1162] The server takes into account the emotional information obtained by the emotion engine and generates an appropriate solution. The generated solution is provided to the user in an easy-to-understand manner using natural language generation technology. If the user is confused, a detailed guide is generated. For example, step-by-step instructions are provided, such as "First, open the settings screen, then select the Wi-Fi option."

[1163] Submitting and Viewing Solutions

[1164] The generated solution is sent to the device as text data, and the device displays the solution to the user visually or audibly. The user can then configure the Wi-Fi by following the instructions displayed on the device screen.

[1165] Examples of concrete examples and prompts

[1166] As a concrete example, consider the case where a user speaks, "How do I set up Bluetooth?" The device converts this speech into text and sends it to the server. The server generates instructions such as, "Open the Settings screen, select the Bluetooth option, and turn it on." If the emotion engine detects the user's confusion, it will generate additional detailed instructions, such as, "You can find the Settings screen by swiping right from the home screen," and send these to the device for display.

[1167] Prompt Sentence Examples

[1168] User Input: "How do I set up Bluetooth?"

[1169] User's emotional state: "Confused"

[1170] Goal: Provide users with clear, detailed instructions on how to set up Bluetooth.

[1171] output:

[1172] Open the Settings screen, select the Bluetooth option, and switch it on. You can find the Settings screen by swiping right from the home screen.

[1173] This system allows users to receive appropriate support for setting up and operating their mobile devices through voice input. Furthermore, by providing detailed support according to the user's emotional state, it is possible to reduce the user's anxiety and achieve smoother operation.

[1174] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1175] Step 1:

[1176] The user speaks into the device's microphone, saying something like, "Please tell me how to set up Wi-Fi."

[1177] Input: User voice input

[1178] Output: Audio data

[1179] What it does: The device's microphone picks up the audio and stores it as digital audio data.

[1180] Step 2:

[1181] The device uses voice recognition technology to convert voice data into text data, specifically using the Google Speech-to-Text API.

[1182] Input: Audio data

[1183] Output: Text data

[1184] Specific operation: Call the speech recognition API and send the speech data to the API. Retrieve the text data returned from the API. The speech "Please tell me how to set up Wi-Fi" is converted into text "Please tell me how to set up Wi-Fi."

[1185] Step 3:

[1186] The terminal transmits the converted text data to a server via the Internet.

[1187] Input: Text data

[1188] Output: HTTP request to the server

[1189] Specific operation: Generates an HTTPS request and securely sends the converted text data to the server.

[1190] Step 4:

[1191] The server analyzes the received text data using natural language processing technology (e.g., OpenAI GPT-4).

[1192] Input: Text data

[1193] Output: Data that recognizes the user's request

[1194] Specific operation: The natural language processing engine is started and the text is analyzed. As a result, the specific request "Set up Wi-Fi" is recognized.

[1195] Step 5:

[1196] The server analyzes the tone, speed, and timing of the voice data using an emotion engine to identify the user's emotional state.

[1197] Input: Audio parameters

[1198] Output: Emotional state (e.g., confused, annoyed, etc.)

[1199] What it does: It uses an emotion engine to analyze multiple parameters of the voice data to identify the user's emotional state.

[1200] Step 6:

[1201] The server uses a generative AI model to generate appropriate solutions based on the text and emotional state.

[1202] Input: User's request, emotional state

[1203] Output: Solution text

[1204] What it does: It uses a natural language generation engine to generate solutions based on the user's request and emotional state. It provides detailed, step-by-step instructions for confused users. For example, "First, open Settings, then select the Wi-Fi option."

[1205] Step 7:

[1206] The server transmits the generated solution to the terminal again as text data.

[1207] Input: Solution text

[1208] Output: HTTP response to the device

[1209] Specific behavior: Generate an HTTP response containing the generated solution text and send it to the terminal.

[1210] Step 8:

[1211] The terminal displays the received solution text to the user.

[1212] Input: Solution text

[1213] Output: Displayed on the user interface

[1214] Specific operation: The received solution text is displayed on the device screen, visually communicating the solution to the user.

[1215] (Application example 2)

[1216] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1217] Conventional customer service in brick-and-mortar stores has difficulty instantly understanding customers' voice-generated questions and requests and providing appropriate solutions. Furthermore, it has been impossible to respond while taking into account the customer's emotional state, limiting the improvement of customer satisfaction. There is a need for a system that can solve these problems, provide appropriate solutions in response to customer requests in real time, and provide support that takes into account the customer's emotional state.

[1218] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1219] In this invention, the server includes means for acquiring voice input from a user, means for converting the voice input into text, means for analyzing the text and generating appropriate solutions, means for presenting the solutions to the user, means for analyzing the user's emotions, and means for adjusting the solutions based on the results of the emotion analysis. This makes it possible to understand customer questions and requests voiced in real time even in a physical store, and to provide appropriate services that take into account the customer's emotional state.

[1220] "Voice input" refers to voice data uttered by a user and acquired through a microphone.

[1221] "Text conversion" is the process of converting captured audio data into text data.

[1222] "Solution generation" is the process of generating appropriate solutions based on the analyzed text data in response to the user's requests.

[1223] "Emotion analysis" is the process of analyzing the tone, rate, and pauses of audio data to identify the user's emotional state.

[1224] "Smart glasses" are eyeglass-type devices that have the ability to display information and receive voice input.

[1225] A "smartphone" is a portable information terminal that has the ability to display information and receive voice input.

[1226] "Natural language processing technology" is a technology for analyzing text data and understanding and generating human language.

[1227] "Means for presenting to the user" refers to the method by which the generated solution is presented to the user visually or audibly.

[1228] "Adjustment based on emotion analysis results" is a process of adjusting the content of the solution and the method of providing it, reflecting the user's emotional information obtained through emotion analysis.

[1229] A "microphone" is an acoustic device for acquiring audio data.

[1230] This invention is a system for improving the efficiency of customer service in brick-and-mortar stores and responding to customer voice inquiries and requests in real time. The system includes means for acquiring voice input from a user, means for converting the acquired voice data into text data, means for analyzing the converted text data to generate appropriate solutions, means for presenting the solutions to the user, means for analyzing the user's emotions, and means for adjusting the solutions based on the results of the emotion analysis.

[1231] Hardware / Software used

[1232] The hardware used includes smart glasses and a smartphone, which are used to capture voice input and present information.

[1233] The software uses the Google Cloud Speech-to-Text API to convert voice data into text, and a natural language processing engine (e.g., Google Natural Language API, OpenAI GPT-4) to analyze text data. It also integrates an emotion engine to analyze user emotions.

[1234] System operation explanation

[1235] 1. Voice input acquisition:

[1236] The system first receives voice input from the user using the microphone in the smart glasses or smartphone. When the customer speaks a question or request, the voice data is sent to the device.

[1237] 2. Audio to text conversion:

[1238] The acquired voice data is converted into text data in real time using the Google Cloud Speech-to-Text API, etc. At this stage, voice recognition technology is utilized to convert the user's speech into text.

[1239] 3. Text data analysis:

[1240] The converted text data is sent to a cloud server and analyzed by a natural language processing engine (Google Natural Language API, OpenAI GPT-4). The server then understands the user's question or request and generates an appropriate solution.

[1241] 4. Emotion analysis:

[1242] The server is integrated with an emotion engine that analyzes the customer's emotional state from the tone, speed, and pauses of the voice data, thereby extracting information such as whether the customer is confused or excited.

[1243] 5. Coordinating and presenting solutions:

[1244] Solutions are generated as text data and adjusted based on sentiment analysis. For example, if the customer is confused, more detailed instructions will be included. The generated solutions are displayed on the screen of the smart glasses or smartphone, and staff can assist the customer based on the solution.

[1245] Specific examples

[1246] For example, if a customer asks, "Where is this item?", the system captures voice input and converts it to text in real time. The text data is then analyzed on a cloud server to generate product location instructions. If sentiment analysis determines that the customer is confused, additional details such as "Section A is on the right, straight ahead from the entrance" are provided. This allows customers to receive prompt and accurate guidance.

[1247] Prompt Sentence Examples

[1248] "Please provide an appropriate response to the following question: Where is this item?"

[1249] In this way, this system will improve the efficiency of customer service in physical stores and increase customer satisfaction.

[1250] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1251] Step 1:

[1252] The device receives voice input from the user. The input voice data is collected through the microphone of smart glasses or a smartphone. Specifically, the voice data is generated when the user speaks a question or request.

[1253] Step 2:

[1254] The device converts the collected voice data into text data using the Google Cloud Speech-to-Text API. The input for this process is voice data, and the output is text data. Specifically, the speech recognition engine combines acoustic and language models to analyze the voice signal and generate corresponding text.

[1255] Step 3:

[1256] The device sends the converted text data to a cloud server. The server uses the received text data as input and analyzes it using a natural language processing engine (e.g., Google Natural Language API or OpenAI GPT-4). The output is an initial solution proposal based on the user's question or request. Specifically, the natural language processing engine understands the meaning of the text and extracts relevant information.

[1257] Step 4:

[1258] The server inputs voice data and analyzes it using an emotion engine to determine the user's emotional state. The output is emotional information such as whether the user is distressed, angry, or happy. Specifically, the emotion analysis engine analyzes parameters such as tone, speed, and pauses in the voice and identifies the user's emotional state from the results.

[1259] Step 5:

[1260] The server adjusts the initial solution based on the sentiment analysis results. The input is the initial solution and sentiment information, and the output is the adjusted solution. Specifically, if the user is confused, for example, the server adds more detailed instructions and explanations to the solution. Also, if the user is in a hurry, the server prioritizes a quick solution.

[1261] Step 6:

[1262] The server sends the final solution to the device, which receives it and presents it to the user. Specifically, it displays it as text on the screen of smart glasses or a smartphone, or plays it as audio guidance. This allows the user to receive the appropriate solution in real time.

[1263] In this way, the system can respond to user requests quickly and accurately through the connection between the terminal and the server. In addition, by combining this with emotion analysis, it can provide a more friendly and considerate response to the user.

[1264] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1265] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1266] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1267] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1268] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1269] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1270] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1271] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1272] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1273] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1274] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1275] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1276] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1277] 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.

[1278] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1279] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1280] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1281] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1282] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1283] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1284] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1285] The following is further disclosed regarding the above embodiment.

[1286] (Claim 1)

[1287] means for obtaining speech input from a user;

[1288] means for converting said speech input into text;

[1289] means for analyzing said text and generating appropriate solutions;

[1290] means for presenting said solution to a user;

[1291] A system including:

[1292] (Claim 2)

[1293] 2. The system according to claim 1, wherein the means for acquiring the voice input acquires the voice data using a microphone.

[1294] (Claim 3)

[1295] 10. The system of claim 1, wherein the means for analyzing the text and generating an appropriate solution utilizes natural language processing techniques.

[1296] "Example 1"

[1297] (Claim 1)

[1298] means for obtaining speech input from a user;

[1299] means for converting said speech input into text;

[1300] means for analyzing said text and generating appropriate solutions;

[1301] means for presenting said solution to a user;

[1302] means for converting said text into a prompt for solution generation;

[1303] means for generating a solution from the prompt using a generative AI model;

[1304] A system including:

[1305] (Claim 2)

[1306] 2. The system according to claim 1, wherein the means for acquiring the voice input acquires voice data using a voice acquisition means.

[1307] (Claim 3)

[1308] 10. The system of claim 1, wherein the means for analyzing the text and generating an appropriate solution utilizes natural language processing techniques.

[1309] "Application Example 1"

[1310] (Claim 1)

[1311] means for obtaining speech input from a user;

[1312] means for converting said speech input into text;

[1313] means for analyzing said text and generating appropriate solutions;

[1314] means for presenting said solution to a user;

[1315] means for audibly presenting said solution;

[1316] a means installed on the logistics robot to assist in inventory management;

[1317] A system including:

[1318] (Claim 2)

[1319] 2. The system according to claim 1, wherein the means for acquiring the voice input acquires the voice data using a microphone.

[1320] (Claim 3)

[1321] 10. The system of claim 1, wherein the means for analyzing the text and generating an appropriate solution utilizes natural language processing techniques.

[1322] "Example 2: Combining Emotion Engines"

[1323] (Claim 1)

[1324] means for obtaining speech input from a user;

[1325] means for converting said speech input into text;

[1326] means for analyzing said text and generating appropriate solutions;

[1327] means for presenting said solution to a user;

[1328] means for identifying a user's emotion;

[1329] a means for adjusting a solution based on the emotion identification;

[1330] A system including:

[1331] (Claim 2)

[1332] 2. The system according to claim 1, wherein the means for acquiring the voice input acquires the voice data using a microphone.

[1333] (Claim 3)

[1334] 10. The system of claim 1, wherein the means for analyzing the text and generating an appropriate solution utilizes natural language processing techniques.

[1335] "Application example 2 when combining emotion engines"

[1336] (Claim 1)

[1337] means for obtaining speech input from a user;

[1338] means for converting said speech input into text;

[1339] means for analyzing said text and generating appropriate solutions;

[1340] means for presenting said solution to a user;

[1341] means for analyzing user emotions;

[1342] means for adjusting a solution based on the sentiment analysis results;

[1343] A system including:

[1344] (Claim 2)

[1345] 2. The system according to claim 1, wherein the means for acquiring the voice input acquires the voice data using a microphone.

[1346] (Claim 3)

[1347] 10. The system of claim 1, wherein the means for analyzing the text and generating an appropriate solution utilizes natural language processing techniques.

[1348] (Claim 4)

[1349] 2. The system of claim 1, wherein the emotion analyzing means analyzes tone, rate, and pause of the voice data.

[1350] (Claim 5)

[1351] The system of claim 1, wherein the means for presenting the solution to the user is by using smart glasses or a smartphone. [Explanation of symbols]

[1352] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for obtaining speech input from a user; means for converting said speech input into text; means for analyzing said text and generating appropriate solutions; means for presenting said solution to a user; A system including:

2. 2. The system according to claim 1, wherein the means for acquiring voice input utilizes a microphone to acquire voice data.

3. 10. The system of claim 1, wherein the means for analyzing the text and generating appropriate solutions utilizes natural language processing techniques.

Images