System

Abstract

A system is provided.SOLUTION: The system includes a means for capturing voice data, a means for converting the captured voice data into text data, a means for collating the converted text data with user information, and a means for displaying the name of the user based on the collation result.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] This solves the problem of office staff having difficulty quickly and accurately identifying employees and visitors. Such identification problems not only waste time and effort, but also pose security risks. It also poses challenges to smooth communication, as it is difficult to easily check who you have spoken to in the past. [Means for solving the problem]

[0005] The present invention provides a system that accurately identifies and displays a user's name using voice data, short-range communication means, and facial recognition technology. Specifically, the system includes a means for capturing voice data and converting it into text data, a means for comparing the converted text data with user information, and a means for displaying the user's name based on the comparison result. The system also includes a means for detecting a device ID using short-range communication means, comparing the device ID with user information, and displaying the user's name based on the comparison result. Furthermore, the system includes a means for capturing a facial image, comparing the facial image with user information, and displaying the user's name based on the comparison result. This allows for more reliable user identification by combining multiple identification methods. The system also includes a means for temporarily saving user information acquired based on voice data, device ID, and facial image, recording it as a conversation history, and referencing it, making it easy to check past communication history.

[0006] "Audio data" refers to a signal in digital or analog form containing human speech sounds obtained from a voice input device such as a microphone.

[0007] "Text data" is character string information obtained by converting voice data into text format.

[0008] "User information" is data related to an individual, such as a name, ID, or facial image, that identifies a specific person.

[0009] "Near-field communication means" refers to technology that exchanges signals between devices using short-range wireless communication technology such as Bluetooth or NFC.

[0010] A "device ID" is an identification code assigned to a specific device to uniquely identify it.

[0011] A "face image" is image data of a person's face acquired by an image input device such as a camera.

[0012] "Matching" is the process of comparing acquired data with an existing database to search for matching information.

[0013] A "pop-up display" is a method of temporarily displaying specific information on the screen in a conspicuous manner.

[0014] "Conversation history" refers to information that records the content, person, date, time, and location of past conversations.

[0015] "Temporary storage" is an operation that holds data for a certain period of time so that it can be accessed quickly when needed.

[0016] A "user ID" is a unique identification code assigned to identify a particular user. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention relates to a system that enables general affairs staff and receptionists in an office to quickly and accurately identify employees and visitors. This system provides the function of identifying and displaying the user's name by combining voice data, short-range communication means, and facial recognition technology. Specific embodiments are described below.

[0039] 1. Audio Identification

[0040] The device has the ability to capture voice data when a user speaks. For example, speech is recognized using a microphone installed on the desk of a general affairs employee. After capturing the voice data, it is encoded and sent to a server. The server uses a voice recognition engine to convert the voice data into text data. This converted text data is compared with a database to obtain matching user information. Based on the comparison results, the device displays the user's name in a pop-up window.

[0041] Example: When a user says "Hello, I'm Tanaka," the voice is captured by a microphone and the voice data is sent to the server. The voice recognition engine converts this into text data "Hello, I'm Tanaka," and information about "Tanaka" is retrieved from the database. Finally, "Tanaka-san" is displayed on the device.

[0042] 2. Identification by short-range communication means

[0043] The device uses Bluetooth or an NFC reader to detect an approaching device or employee ID. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information. The device then displays a pop-up with the user's name based on the match.

[0044] Example: When a user (employee) approaches the general affairs desk with an iPhone (registered trademark) in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which checks it against the database and retrieves the information for "Sato." Finally, "Mr. Sato" is displayed on the device.

[0045] 3. Facial Recognition Identification

[0046] The device's camera captures the user's face. The captured facial image data is sent to the server. The server uses a facial recognition engine to match the facial image data with a database and obtains matching user information. The device then displays the user's name in a pop-up based on the matching results.

[0047] Example: When a user stands in front of the general affairs desk, a camera captures a facial image. The facial image data is sent to a server, where a facial recognition engine performs a match and obtains information about "Yamada." Finally, "Yamada-san" is displayed on the terminal.

[0048] 4. Historical Data Management

[0049] The terminal has a function to temporarily store information about the user who spoke to it. When the conversation ends, information such as the user's information (user ID), time, and location is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the historical information is retrieved from the database via the terminal and displayed.

[0050] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and history information such as "Tanaka-san, Sato-san" is displayed from the database.

[0051] This allows general affairs personnel to efficiently identify employees and easily manage and view interaction history.

[0052] The processing flow will be explained below.

[0053] Voice Identification

[0054] Step 1:

[0055] The terminal uses a microphone to capture voice data when the user speaks.

[0056] Step 2:

[0057] The terminal encodes the captured audio data and transmits it to a server via a network.

[0058] Step 3:

[0059] The server decodes the encoded voice data and passes it to a voice recognition engine.

[0060] Step 4:

[0061] The server uses a speech recognition engine to convert the voice data into text data.

[0062] Step 5:

[0063] The server extracts the name portion from the converted text data and compares it with user information in a database.

[0064] Step 6:

[0065] The server returns the result of the check to the terminal.

[0066] Step 7:

[0067] The terminal displays the received matching result (user's name) in a pop-up.

[0068] Identification by short-range communication means

[0069] Step 1:

[0070] The device uses Bluetooth or an NFC reader to detect signals from nearby devices.

[0071] Step 2:

[0072] The terminal encodes the detected device ID and transmits it to the server via the network.

[0073] Step 3:

[0074] The server decodes the received device ID and checks it against the user information in the database.

[0075] Step 4:

[0076] The server returns the result of the check to the terminal.

[0077] Step 5:

[0078] The terminal displays the received matching result (user's name) in a pop-up.

[0079] Facial Recognition Identification

[0080] Step 1:

[0081] The terminal uses a camera to capture an image of the user's face.

[0082] Step 2:

[0083] The terminal encodes the captured facial image data and transmits it to a server via a network.

[0084] Step 3:

[0085] The server decodes the received facial image data and passes it to the facial recognition engine.

[0086] Step 4:

[0087] The server uses a facial recognition engine to match the facial image data with user information in a database.

[0088] Step 5:

[0089] The server returns the result of the check to the terminal.

[0090] Step 6:

[0091] The terminal displays the received matching result (user's name) in a pop-up.

[0092] Historical Data Management

[0093] Step 1:

[0094] The device temporarily stores user information obtained based on voice, device ID, and facial information.

[0095] Step 2:

[0096] When the conversation ends, the terminal encodes the user information, timestamp, and location and transmits them to the server via the network.

[0097] Step 3:

[0098] The server records the received conversation information in a database.

[0099] Step 4:

[0100] The user operates the terminal to request the history data.

[0101] Step 5:

[0102] The terminal encodes the request and sends it over the network to the server.

[0103] Step 6:

[0104] The server searches the database for the relevant history data and returns the search results to the terminal.

[0105] Step 7:

[0106] The terminal displays the received history data to the user.

[0107] By following these steps, general affairs staff can efficiently identify employees and manage and view their interaction history.

[0108] Example 1

[0109] 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."

[0110] There is a need for a method that enables office staff and receptionists to quickly and accurately identify employees and visitors. A key challenge is improving accuracy by combining various methods, such as voice, short-range communication, and facial recognition. Also important is the ability to manage and reference user history information after identification.

[0111] 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.

[0112] In this invention, the server includes a means for converting voice data into text data, a means for comparing the converted text data with a database to acquire user information, a means for comparing a device ID with a database to acquire user information, a means for comparing face image data with a database to acquire user information, and a means for managing history information. This allows general affairs staff and reception staff to quickly and accurately identify employees and visitors using a variety of methods. Furthermore, by managing and referencing past communication history, work efficiency can be improved.

[0113] "Voice data" is data that represents the voice signal emitted by the user in digital form.

[0114] "Encoding" is the process of converting and compressing digital data according to certain rules, and is a technology used to efficiently transfer and store data.

[0115] A "server" is a computer system that provides services to other computers and terminals on a network, and plays a central role in data processing and information management.

[0116] A "voice recognition engine" is a software component that analyzes voice data and converts the content into text data.

[0117] "Text data" is a data format that expresses information using characters and symbols, and is character string data generated by a voice recognition engine.

[0118] A "database" is a collection of data that is constructed to efficiently search, manage, and update large amounts of data, and the data is organized based on specific rules.

[0119] "User information" is information used to identify individual users, and includes identifiers such as names and employee numbers.

[0120] "Short-range communication means" refers to technology that enables communication between devices over short distances using Bluetooth, NFC, etc.

[0121] A "device ID" is an identifier that uniquely identifies an individual device, and is specific information assigned to Bluetooth or NFC devices.

[0122] "Facial image data" is data that digitally represents an image of a user's face, and is captured by a camera.

[0123] A "face recognition engine" is a software component that analyzes facial image data and matches its contents with existing information in a database.

[0124] "History information" refers to information that includes records of past interactions and identifications with a user, including data such as user ID, time, and location.

[0125] This invention relates to a system that enables general affairs staff and receptionists in an office to quickly and accurately identify employees and visitors. This system provides the function of identifying and displaying the user's name by combining voice data, short-range communication means, and face recognition technology. The specific configuration and operation of this system are described below.

[0126] Voice Identification

[0127] The device has the ability to capture voice data when a user speaks. Specifically, a microphone installed on the general affairs staff member's desk is used to recognize the user's speech. After capturing the voice data, it is encoded and sent to the server. The server then uses a voice recognition engine (e.g., Google® Cloud Speech-to-Text API) to convert the voice data into text data. This converted text data is then compared with a database to obtain matching user information. Finally, the device displays the user's name as a pop-up based on the comparison results.

[0128] Example: When a user says "Hello, I'm Tanaka," the voice is captured by a microphone and the voice data is sent to the server. The voice recognition engine converts this into text data "Hello, I'm Tanaka," and information about "Tanaka" is retrieved from the database. Finally, "Tanaka-san" is displayed on the device.

[0129] Example prompt sentence:

[0130] "General office system that identifies individuals using their voice and displays their name. User says 'Hello, I'm Tanaka.' System captures voice, sends to server which converts it to text and fetches 'Tanaka' from the database to be displayed on the terminal."

[0131] Identification by short-range communication means

[0132] The device uses Bluetooth or an NFC reader to detect an approaching device or employee ID. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information. The device then displays a pop-up with the user's name based on the match.

[0133] Example: When a user (employee) approaches the general affairs desk with their smartphone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which checks it against the database and retrieves the information for "Sato." Finally, "Mr. Sato" is displayed on the device.

[0134] Example prompt sentence:

[0135] "General office system that identifies individuals using NFC or Bluetooth technology and displays their name. User with smartphone approaches, system reads Bluetooth ID and fetches 'Sato' from database to be displayed on the terminal."

[0136] Facial Recognition Identification

[0137] The device's camera captures the user's face. The captured facial image data is encoded and sent to the server. The server uses a facial recognition engine (e.g., Amazon Rekognition) to match the facial image data with a database and obtain matching user information. The device then displays the user's name in a pop-up based on the matching results.

[0138] Example: When a user stands in front of the general affairs desk, a camera captures a facial image. The facial image data is sent to a server, where a facial recognition engine performs a match and obtains information about "Yamada." Finally, "Yamada-san" is displayed on the terminal.

[0139] Example prompt sentence:

[0140] "General office system that identifies individuals using facial recognition technology and displays their name. User stands in front of the desk, camera captures face, server processes and fetches 'Yamada' from the database to be displayed on the terminal."

[0141] Historical Data Management

[0142] The terminal has a function to temporarily store information about the user who spoke to it. When the conversation ends, information such as the user's information (user ID), time, and location is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the historical information is retrieved from the database via the terminal and displayed.

[0143] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and history information such as "Tanaka-san, Sato-san" is displayed from the database.

[0144] Example prompt sentence:

[0145] "General office system that manages conversation history including user ID, timestamp, and location. User greets employees, data is recorded and can later be retrieved to see 'talked to Tanaka and Sato'."

[0146] As described above, this system combines voice recognition, short-range communication, and facial recognition to efficiently and accurately identify users and support the work of general affairs personnel. Furthermore, by managing historical information, past conversation records can be easily referenced.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] Voice Identification

[0149] Step 1:

[0150] The device captures voice data. When a user speaks, a microphone installed on the general affairs staff member's desk captures the user's speech as voice data.

[0151] Input: User's voice

[0152] Output: Captured audio data

[0153] Step 2:

[0154] The device encodes the captured audio data and sends it to the server. Encoding makes data transfer and storage more efficient.

[0155] Input: Captured audio data

[0156] Output: Encoded audio data

[0157] Step 3:

[0158] The server converts the voice data into text data using a speech recognition engine, for example, the Google Cloud Speech-to-Text API.

[0159] Input: Encoded audio data

[0160] Output: Text data

[0161] Step 4:

[0162] The server compares the text data with a database to obtain user information, using a matching algorithm to search for names and other characters within the text data.

[0163] Input: Text data

[0164] Output: User information as a match result

[0165] Step 5:

[0166] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[0167] Input: User information as a match result

[0168] Output: On-screen popup

[0169] Identification by short-range communication means

[0170] Step 1:

[0171] The terminal detects near-field communication devices. When a user approaches the general affairs desk, a Bluetooth or NFC reader captures the device ID.

[0172] Input: User's device signal

[0173] Output: Device ID

[0174] Step 2:

[0175] The device detects the device ID, encodes it, and sends it to the server.

[0176] Input: Device ID

[0177] Output: Encoded device ID

[0178] Step 3:

[0179] The server uses the device ID to check against the database to obtain user information. The check yields matching user information.

[0180] Input: Encoded device ID

[0181] Output: User information as a match result

[0182] Step 4:

[0183] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[0184] Input: User information as a match result

[0185] Output: On-screen popup

[0186] Facial Recognition Identification

[0187] Step 1:

[0188] The device's camera captures the user's face, and a facial image is acquired when the user stands in front of the general affairs desk.

[0189] Input: User's face

[0190] Output: Face image data

[0191] Step 2:

[0192] The terminal encodes the captured facial image data and transmits it to the server.

[0193] Input: Face image data

[0194] Output: Encoded face image data

[0195] Step 3:

[0196] The server uses facial image data to match it with a database and obtain user information. A facial recognition engine such as Amazon Rekognition is used.

[0197] Input: Encoded face image data

[0198] Output: User information as a match result

[0199] Step 4:

[0200] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[0201] Input: User information as a match result

[0202] Output: On-screen popup

[0203] Historical Data Management

[0204] Step 1:

[0205] The device temporarily stores user information when a conversation ends, and also records the timing and location of the conversation.

[0206] Input: User information, timestamp, and location after conversation ends

[0207] Output: Temporarily held information

[0208] Step 2:

[0209] The terminal transmits the user information to the server.

[0210] Input: Temporarily held information

[0211] Output: User information sent

[0212] Step 3:

[0213] The server records the received user information in a database, including the user ID, time, and location.

[0214] Input: Submitted user information

[0215] Output: Information recorded in the database

[0216] Step 4:

[0217] The terminal acquires and displays history information. This is used when the user wants to refer to the history of past interactions.

[0218] Input: Request History Information

[0219] Output: Display history information

[0220] (Application example 1)

[0221] 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."

[0222] To efficiently manage and support work within a factory, a system that can quickly and accurately identify managers and workers is necessary. However, existing identification systems often rely on a limited number of identification methods, and have the problem of being unable to handle a variety of situations. In addition, there is a lack of systems in place to properly execute managers' instructions and support workers.

[0223] 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.

[0224] In this invention, the server includes means for capturing voice data, means for converting the captured voice data into text data, means for comparing the converted text data with user information, means for displaying the user's name based on the comparison result, means for detecting a device ID using short-range communication means, means for comparing the detected device ID with user information, means for capturing a facial image, means for comparing the captured facial image with user information, and means for identifying a factory manager or worker based on the comparison result and providing appropriate work or support. This enables quick and accurate identification of managers and workers, enabling efficient management and work support within the factory.

[0225] "Voice data" refers to an acoustic signal that records the user's speech.

[0226] "Text data" is digital information that is obtained by converting voice data into a string of characters.

[0227] "User information" is a database record containing a user's identity.

[0228] "Verification" is the process of comparing and verifying acquired data with existing databases.

[0229] "Near-field communication means" refers to a means of exchanging data between devices using short-range wireless communication technologies such as Bluetooth and NFC.

[0230] A "device ID" is a unique identifier for identifying a particular device.

[0231] A "face image" is image data of a person's face photographed using a camera.

[0232] "Historical data" is data that records a user's activities and interactions.

[0233] A "factory manager" is a person in charge of operations and work instructions in a factory.

[0234] "Workers" are employees who perform physical tasks or operate equipment within a factory.

[0235] "Support" means providing support to workers to carry out their work efficiently.

[0236] "Management" means monitoring and adjusting the progress of operations and work within the factory.

[0237] The "matching result" is the user's identification information obtained as a result of database matching.

[0238] This invention is a system for identifying managers and workers in a factory and providing appropriate work support and management. This system integrates voice recognition, short-range communication, and face recognition functions, and can collate and display user information.

[0239] Voice recognition identification

[0240] The server is equipped with a microphone to capture voice data, and when a user speaks, the voice data is acquired. The acquired voice data is converted into text data using the speech_recognition library. The server then uses the converted text data to match the user information with the database and obtains matching information. The user's name as a matched result is displayed as a pop-up on the terminal.

[0241] Example: When a factory manager says, "Hello, I'm the manager," the voice is captured by a microphone, converted into text by a speech recognition engine on the server, and then matched with a database to identify the person as "manager."

[0242] Identification by short-range communication

[0243] The server uses Bluetooth or NFC readers to detect the ID of approaching devices and employees. The detected device ID is encoded and sent to the server, which then checks it against its database to obtain matching user information. The results are displayed in a pop-up on the terminal.

[0244] Example: When a worker approaches a factory with a smartphone in their pocket, their Bluetooth signal is detected and the server checks the database to identify them as a "worker."

[0245] Facial Recognition Identification

[0246] The server uses a camera to capture a facial image and sends the facial image data to the server. The server uses a facial recognition engine to match the facial image data with a database and obtains matching user information. The results are displayed on the terminal.

[0247] Example: When a manager stops at the entrance to a factory, a camera captures his / her facial image, which is then matched against a database by a facial recognition engine and identified as a "manager."

[0248] Historical Data Management

[0249] The server has a function to temporarily store information about the user who spoke to it, and when the conversation ends, records the user information, time, location, and other information in a database, allowing users to refer to their past communication history.

[0250] For example, at the end of the day, when a manager requests from a terminal "Who did you talk to today?", the server retrieves historical information such as "Worker 1, Engineer A" from the database and displays it on the terminal.

[0251] Specific examples

[0252] For example, if a factory manager approaches a robot, the robot will detect the manager's Bluetooth ID and greet him with, "Hello, manager!" If the manager instructs the robot to "check the work efficiency," the robot will analyze the work efficiency of the entire factory in real time and report it.

[0253] An example of a prompt sentence to be input to the generative AI model to realize this system is as follows:

[0254] "Please explain an example of a system implementation that uses voice recognition, near-field communication, and facial recognition technology to enable robots working in a factory to identify factory managers and workers and take appropriate action."

[0255] This makes it possible to efficiently identify managers and workers and effectively manage and support operations within the factory.

[0256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0257] Step 1:

[0258] Capture audio data

[0259] If the user is a manager or worker, voice data is captured by speaking into a microphone, which is a raw acoustic signal.

[0260] Step 2:

[0261] Converting audio data to text

[0262] The server converts the captured audio data into text data using the speech_recognition library. The input is audio data, and the output is the corresponding text data. The audio waveform data is analyzed and string data is generated based on a language model.

[0263] Step 3:

[0264] Matching text data with user information

[0265] The server checks the converted text data against the user information in the database. The input to this process is the text data, and the output is the matching user information. The server compares the text data with the user information database.

[0266] Step 4:

[0267] Viewing User Information

[0268] The server pops up the user's name on the terminal based on the match result. The input of this process is the match result, and the output is the user's name displayed on the terminal.

[0269] Step 5:

[0270] Device ID detection through short-range communication

[0271] The terminal uses Bluetooth or an NFC reader to detect the user's device ID. The input of this process is the surrounding device signal, and the output is the detected device ID.

[0272] Step 6:

[0273] Matching device ID with user information

[0274] The server checks the detected device ID against the user information in the database. The input to this process is the device ID, and the output is the matching user information. The server compares the device ID with the user information database.

[0275] Step 7:

[0276] Facial image capture

[0277] The terminal uses a camera to capture the user's face image, and the input of this process is the camera video signal and the output is the captured face image data.

[0278] Step 8:

[0279] Matching face images with user information

[0280] The server matches the captured facial image data with user information in a database. The input to this process is facial image data, and the output is the matching user information. The server uses a facial recognition engine to compare the facial image with the facial data in the database.

[0281] Step 9:

[0282] Integrated display of user information

[0283] The server identifies the user information by combining the results of matching the voice, device ID, and facial image, and displays it on the device. The input to this process is the three matching results, and the output is the final user information.

[0284] Step 10:

[0285] Historical Data Recording

[0286] The server records the user information, time, and location in a database. The inputs to this process are user information, timestamp, and location information, and the output is an updated database record.

[0287] By executing each step in this way, it is possible to identify managers and workers within the factory and provide appropriate support.

[0288] 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.

[0289] The present invention relates to a system that enables office staff and receptionists to quickly and accurately identify employees and visitors and grasp their emotional states. This system provides a function to identify and display the user's emotional state by combining voice data, short-range communication means, facial recognition technology, and an emotion engine. Specific embodiments are described below.

[0290] 1. Voice recognition and emotion recognition

[0291] The device has the ability to capture voice data when a user speaks. For example, a microphone installed on the desk of a general affairs employee can be used to recognize speech. The voice data is then sent to an emotion engine to recognize the user's emotional state. After capturing the voice data, it is encoded and sent to a server. The server then uses a voice recognition engine to convert the voice data into text data, and compares the converted text data with a database to obtain matching user information.

[0292] Example: When a user says "Hello, I'm Tanaka," the voice is captured by the microphone. At the same time, the emotion engine recognizes the emotional state of "Tanaka is happy." The voice data is sent to the server, where it is converted into text data "Hello, I'm Tanaka" by the speech recognition engine. Information about "Tanaka" is retrieved from the database, and finally "Tanaka" and his emotional state of "happy" are displayed on the device.

[0293] 2. Identification and emotion recognition using short-range communication methods

[0294] The terminal uses Bluetooth or an NFC reader to detect approaching devices or employee IDs. At the same time, it recognizes the user's emotional state from voice data and facial images. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information.

[0295] Example: When a user (employee) approaches the general affairs desk with their iPhone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which matches it with the database and retrieves the information of "Sato." Finally, the device displays "Sato-san" and his / her active emotional state.

[0296] 3. Facial Recognition and Emotion Recognition

[0297] The device's camera captures the user's face, and sends the facial image data to an emotion engine to recognize the user's emotional state. The captured facial image data is then sent to a server, where the facial recognition engine matches the facial image data with user information in a database.

[0298] Example: When a user stands in front of the general affairs desk, the camera captures a facial image. At the same time, the emotion engine recognizes the emotional state of "Yamada is tired." The facial image data is sent to the server, where it is matched by the facial recognition engine to obtain information about "Yamada." Finally, "Yamada" and his emotional state of "fatigue" are displayed on the terminal.

[0299] 4. Historical data management and emotion history

[0300] The device has the ability to temporarily store user information and emotional state obtained based on voice, device ID, and facial information. When the conversation ends, information such as user information, timestamp, location, and emotional state is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the device retrieves and displays the history information and emotional state from the database.

[0301] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and the database will display historical information and emotional state such as "Tanaka-san, happy" or "Sato-san, active."

[0302] This allows general affairs personnel to efficiently identify employees and understand their emotional state. In addition, the quality of communication is improved because conversation history and emotional state can be easily managed and referenced.

[0303] The processing flow will be explained below.

[0304] Speech and emotion recognition

[0305] Step 1:

[0306] The terminal uses a microphone to capture voice data when the user speaks.

[0307] Step 2:

[0308] The terminal encodes the captured audio data and transmits it to a server via a network.

[0309] Step 3:

[0310] The server decodes the encoded voice data and passes it to a voice recognition engine.

[0311] Step 4:

[0312] The server uses a speech recognition engine to convert the voice data into text data.

[0313] Step 5:

[0314] The server extracts the name portion from the converted text data and compares it with user information in a database.

[0315] Step 6:

[0316] At the same time, the server sends the voice data to the emotion engine to analyze the user's emotional state.

[0317] Step 7:

[0318] The server returns the matching results and emotion analysis results to the terminal.

[0319] Step 8:

[0320] The device displays the received matching result (user's name) and emotional state in a pop-up.

[0321] Identification and emotion recognition using short-range communication methods

[0322] Step 1:

[0323] The device uses Bluetooth or an NFC reader to detect signals from nearby devices.

[0324] Step 2:

[0325] The terminal encodes the detected device ID and transmits it to the server via the network.

[0326] Step 3:

[0327] The server decodes the received device ID and checks it against the user information in the database.

[0328] Step 4:

[0329] The server returns the result of the check to the terminal.

[0330] Step 5:

[0331] At the same time, the device sends the captured voice data and facial images to the emotion engine to analyze the user's emotional state.

[0332] Step 6:

[0333] The server returns the matching results and emotion analysis results to the terminal.

[0334] Step 7:

[0335] The device displays the received matching result (user's name) and emotional state in a pop-up.

[0336] Facial recognition and emotion recognition

[0337] Step 1:

[0338] The terminal uses a camera to capture an image of the user's face.

[0339] Step 2:

[0340] The terminal encodes the captured facial image data and transmits it to a server via a network.

[0341] Step 3:

[0342] The server decodes the received facial image data and passes it to the facial recognition engine.

[0343] Step 4:

[0344] The server uses a facial recognition engine to match the facial image data with user information in a database.

[0345] Step 5:

[0346] The server returns the result of the check to the terminal.

[0347] Step 6:

[0348] At the same time, the device sends the captured facial image to the emotion engine to analyze the user's emotional state.

[0349] Step 7:

[0350] The server returns the matching results and emotion analysis results to the terminal.

[0351] Step 8:

[0352] The device displays the received matching result (user's name) and emotional state in a pop-up.

[0353] Historical data management and emotion history

[0354] Step 1:

[0355] The device temporarily stores user information obtained based on voice, device ID, facial information, and emotional state.

[0356] Step 2:

[0357] When the conversation ends, the terminal encodes the user information, timestamp, location, and emotional state and transmits them to the server via the network.

[0358] Step 3:

[0359] The server records the received conversation information in a database.

[0360] Step 4:

[0361] The user operates the terminal to request the history data.

[0362] Step 5:

[0363] The terminal encodes the request and sends it over the network to the server.

[0364] Step 6:

[0365] The server searches the database for the relevant history data and emotional state, and returns the search results to the terminal.

[0366] Step 7:

[0367] The terminal displays the received historical data and emotional state to the user.

[0368] This process flow allows general affairs personnel to efficiently identify employees and understand their emotional state. It also allows for easy management and reference of conversation history and emotional state, improving the quality of communication.

[0369] Example 2

[0370] 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."

[0371] In an office, it is important for general affairs staff and receptionists to quickly and accurately identify employees and visitors and understand their emotional state, but doing this manually is time-consuming and prone to human error. Furthermore, understanding a user's emotional state when interacting with them allows for more appropriate responses, but this is also difficult to do manually. Furthermore, there is a need for a function to manage past communication history and emotional state and refer to them later.

[0372] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for capturing voice data, means for transmitting the captured voice data to an emotion engine to recognize an emotional state, means for converting the captured voice data into text data, means for comparing the converted text data with user information, and means for displaying the user's name and emotional state based on the comparison result. This allows for automatic identification of the user and recognition of their emotional state, thereby improving the efficiency of the work of general affairs staff and reception staff and enabling more appropriate responses.

[0373] "Voice data" is information that digitally captures a user's speech.

[0374] "Capturing means" refers to a combination of hardware and software for capturing audio data and facial images.

[0375] The "emotion engine" is software that analyzes and recognizes the user's emotional state from acquired voice data and facial images.

[0376] The "means for converting into text data" is software that includes voice recognition technology that converts voice data into text information.

[0377] "User information" refers to identification information and attribute information about individual users that is stored in a database.

[0378] "Means for matching" refers to the process of comparing acquired voice data, text data, device ID, or facial image data with a database to identify matching user information.

[0379] "Device ID" is a unique identifier of a device obtained by short-range communication means.

[0380] A "face image" is image data that digitally represents a user's face.

[0381] The "displaying means" refers to a display device or software for visually displaying the acquired user information and emotional state.

[0382] "Short-range communication means" refers to communication technologies for exchanging data over short distances, such as Bluetooth and NFC.

[0383] A "database" is an information management system for systematically organizing and storing multiple user information.

[0384] This invention relates to a system that enables office staff and receptionists to quickly and accurately identify employees and visitors and grasp their emotional state. This system uses voice data, short-range communication means, and face recognition technology, as well as an emotion engine to identify and display the user's emotional state.

[0385] 1. Voice recognition and emotion recognition

[0386] The device uses a microphone to capture voice data when the user speaks. For example, a microphone installed on a desk captures the user's voice, such as "Hello, I'm Tanaka." This voice data is sent to an emotion engine, which analyzes the user's emotional state and recognizes, for example, "joy." The voice data is then encoded and sent to the server. The server uses a voice recognition engine to convert the voice data into text data, and compares the text data with a database to obtain matching user information.

[0387] Example: When a user says "Hello, I'm Tanaka," the microphone captures the voice. At the same time, the emotion engine recognizes that "Tanaka-san is happy." The voice data is sent to the server, where it is converted into "Hello, I'm Tanaka-san" by the speech recognition engine. Information about "Tanaka-san" is retrieved from the database, and "Tanaka-san" and his emotional state of "happy" are displayed on the device.

[0388] Example prompt: Describe the data processing flow when a user says, "Hello, I'm Tanaka."

[0389] 2. Identification and emotion recognition using short-range communication methods

[0390] The terminal uses Bluetooth or an NFC reader to detect approaching devices or employee IDs. At the same time, it recognizes the user's emotional state from voice data and facial images. The detected device ID is encoded and sent to the server, which then compares it with a database to obtain matching user information.

[0391] Example: When a user (employee) approaches the general affairs desk with their smartphone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which compares it with the database and retrieves information about "Mr. Sato." The device displays "Mr. Sato" and his active emotional state.

[0392] Example prompt: Describe the data processing flow when a user approaches the administration desk and a Bluetooth signal is detected.

[0393] 3. Facial Recognition and Emotion Recognition

[0394] The device's camera captures a user's facial image, and sends the facial image data to an emotion engine to recognize the user's emotional state. This facial image data is then sent to a server, which uses a facial recognition engine to match the facial image data with user information in a database.

[0395] Example: When a user stands in front of the general affairs desk, the camera captures a facial image. At the same time, the emotion engine recognizes the user's emotional state as "Yamada-san is tired." The facial image data is sent to the server, and the facial recognition engine obtains information about "Yamada-san." The device displays "Yamada-san" and his emotional state of "fatigue."

[0396] Example prompt: Describe the process flow for facial and emotion recognition when a user stands in front of the general affairs desk.

[0397] 4. Historical data management and emotion history

[0398] The device temporarily stores user information and emotional state obtained based on voice, device ID, and facial information. When the conversation ends, this information is sent to the server and recorded in a database. When the user wants to view their past communication history, the device retrieves the history information from the database on the server and displays it on the device.

[0399] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and the database will display historical information and emotional state such as "Tanaka-san, happy" or "Sato-san, active."

[0400] This allows general affairs personnel to efficiently identify employees and understand their emotional state. In addition, the quality of communication is improved because conversation history and emotional state can be easily managed and referenced.

[0401] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0402] 1. Voice recognition and emotion recognition

[0403] Step 1:

[0404] The device captures the user's voice data through a microphone. The input is the user's speech, and the output is the captured voice data. For example, a voice such as "Hello, I'm Tanaka" is captured by the microphone.

[0405] Step 2:

[0406] The device sends the captured voice data to the emotion engine, which analyzes the voice data and recognizes the user's emotional state. In this process, the input is the captured voice data, and the output is the emotional state obtained from the emotion engine, such as "joy."

[0407] Step 3:

[0408] The device encodes the audio data and sends it to the server. The input is the captured audio data and the recognized emotional state, and the output is the encoded audio data.

[0409] Step 4:

[0410] The server receives the transmitted voice data and converts it into text data using a speech recognition engine. The input is the encoded voice data, and the output is text data such as "Hello, I'm Tanaka."

[0411] Step 5:

[0412] The server compares the converted text data with the database to obtain matching user information. The input is text data, and the output is user information (e.g., "Mr. Tanaka") obtained from the database.

[0413] Step 6:

[0414] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[0415] Step 7:

[0416] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Mr. Tanaka" and "Joy").

[0417] 2. Identification and emotion recognition using short-range communication methods

[0418] Step 1:

[0419] The terminal detects the device ID using Bluetooth or an NFC reader. The input is the signal from the device held by the user, and the output is the detected device ID.

[0420] Step 2:

[0421] The device recognizes the emotional state from voice data and facial images. The input is voice data and facial images, and the output is the recognized emotional state.

[0422] Step 3:

[0423] The device encodes the device ID and sends it to the server. The input is the device ID and emotional state, and the output is the encoded data.

[0424] Step 4:

[0425] The server checks the received device ID against the database and retrieves the matching user information. The input is the encoded device ID, and the output is the user information retrieved from the database.

[0426] Step 5:

[0427] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[0428] Step 6:

[0429] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Mr. Sato" and "Active").

[0430] 3. Facial Recognition and Emotion Recognition

[0431] Step 1:

[0432] The device's camera captures the user's face image. The input is the user's face, and the output is the captured face image data.

[0433] Step 2:

[0434] The device sends the captured facial image to the emotion engine to recognize the emotional state. The input is the facial image data, and the output is the recognized emotional state.

[0435] Step 3:

[0436] The terminal encodes the facial image data and sends it to the server. The input is the facial image data and the emotional state, and the output is the encoded data.

[0437] Step 4:

[0438] The server passes the received facial image data to a facial recognition engine, which compares it with the database to obtain matching user information. The input is the encoded facial image data, and the output is the user information obtained from the database.

[0439] Step 5:

[0440] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[0441] Step 6:

[0442] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Yamada-san" and "fatigue").

[0443] 4. Historical data management and emotion history

[0444] Step 1:

[0445] The device temporarily stores the acquired user information and emotional state. The input is user information such as voice data, device ID, and facial information, as well as the emotional state, and the output is the temporarily stored data.

[0446] Step 2:

[0447] At the end of the conversation, the device encodes these data and sends them to the server. The input is the user information, timestamp, location, and emotional state at the end of each conversation, and the output is the encoded data.

[0448] Step 3:

[0449] The server stores the received history data in a database. The input is the encoded history data, and the output is the history information stored in the database.

[0450] Step 4:

[0451] When a user wants to view past communication history, the terminal sends a request to the server. The input is a view request, and the output is a send request.

[0452] Step 5:

[0453] The server retrieves the relevant history information and emotional state from the database and sends them to the terminal. The input is the reference request, and the output is the history information and emotional state.

[0454] Step 6:

[0455] The terminal displays the received history information and emotional state. The input is the history information and emotional state, and the output is the information displayed on the display.

[0456] This allows general affairs personnel to efficiently identify employees and understand their emotional state. The quality of communication is also improved because conversation history and emotional state can be easily managed and referenced.

[0457] (Application example 2)

[0458] 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."

[0459] In conventional brick-and-mortar stores, it is difficult to identify individual customers and grasp their emotional state, making it difficult to provide appropriate customer service to increase customer satisfaction. This can result in a decrease in customer satisfaction and a loss of repeat customers. Furthermore, it takes time and effort for customer service staff to grasp the information of all customers, making it inefficient. To solve these issues, a system is needed that can quickly and accurately identify customers who visit a store and grasp their emotional state.

[0460] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing voice data, means for converting the captured voice data into text data, means for comparing the converted text data with user information, means for displaying the user's name based on the comparison result, means for recognizing the user's emotional state using an emotion engine, and means for displaying the recognized emotional state. This makes it possible to quickly and accurately identify customers who visit a store and grasp their emotional state in real time.

[0461] "Audio data" refers to data in which audio is recorded in digital format.

[0462] "Capturing" means collecting and recording audio data, image data, etc. using a device.

[0463] "Text data" is data that represents text information such as letters and numbers in a digital format.

[0464] "Converting" is the process of changing data in one format into data in another format.

[0465] "User information" is information related to a user, such as personal identification information, name, and job title.

[0466] "Matching" is the process of comparing acquired data with existing data to see if they match.

[0467] "Display" means to visually represent characters or images on the screen of a device or the like.

[0468] "Short-range communication means" refers to short-range wireless communication technologies such as Bluetooth and NFC.

[0469] A "device ID" is an identifier uniquely assigned to each device.

[0470] A "face image" is an image of a person's face captured using a camera or the like.

[0471] An "emotion engine" is software or a system for analyzing and recognizing emotions from data such as voice and images.

[0472] "Emotional state" refers to a person's emotional state, such as joy, anger, sadness, or surprise.

[0473] A "server" is a computer system for storing and processing data.

[0474] This invention relates to a system for quickly and accurately identifying customers and grasping their emotional state in a brick-and-mortar store. The system provides the ability to recognize and display the emotional state of customers by combining voice data capture, near-field communication means, facial recognition technology, and an emotion engine.

[0475] The system configuration is as follows:

[0476] 1. Voice recognition and emotion recognition

[0477] The device has the ability to capture voice data when the user speaks. Specifically, it recognizes voice using the microphone of the smart device (smartphone or smart glasses). The voice data is converted into text data using the Google Cloud Speech-to-Text API. The converted text data is sent to a server, which compares it with a database to obtain matching user information. It also uses an emotion engine (Affectiva SDK) to recognize the user's emotional state from the voice data. The recognized emotional state is visually displayed on the device screen.

[0478] Example: When a user says "Hello, I'm Sato," the voice is captured by the microphone on the smart device. At the same time, the emotion engine recognizes the emotional state of "Mr. Sato is happy." The voice data is sent to the server, where the voice recognition engine converts it into text data of "Hello, I'm Sato." Information about "Sato" is retrieved from the database, and finally "Mr. Sato" and his emotional state of "joy" are displayed on the device.

[0479] 2. Identification and emotion recognition using short-range communication methods

[0480] The terminal uses Bluetooth and NFC readers to detect approaching customers' devices, while simultaneously recognizing their emotional state through voice data and facial images. Once the device ID is detected, it is sent to the server, where it is compared with a database to retrieve matching customer information.

[0481] Example: When a customer enters a store with a smart device, the Bluetooth signal is detected by the terminal. The device ID is sent to the server, which matches it with the database to obtain information about "Sato." Finally, the terminal displays "Sato-san" and his active emotional state.

[0482] 3. Facial Recognition and Emotion Recognition

[0483] The device's camera captures the customer's face. The facial image data is then sent to an emotion engine to recognize the user's emotional state. The captured facial image data is then sent to a server, where the facial recognition engine (Microsoft® Azure® Face API) matches it with user information in a database.

[0484] Example: When a customer stands in front of a reception robot, a camera captures a facial image. At the same time, an emotion engine recognizes the customer's emotional state as "Yamada is tired." The facial image data is sent to a server, where it is collated by the facial recognition engine to obtain information about "Yamada." Finally, "Yamada" and his emotional state of "fatigue" are displayed on the terminal.

[0485] 4. Historical data management and emotion history

[0486] The device has the ability to temporarily store customer information and their emotional state obtained based on voice, device ID, and facial information. At the end of the conversation, information such as customer information, timestamp, location, and emotional state is sent to the server and recorded in a database. If the customer wants to view their past communication history, the device retrieves and displays the historical information and emotional state from the database.

[0487] Example: A customer speaks with multiple staff members during a morning greeting. At the end of each conversation, customer information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a staff member wants to refer to the history later, they can request "who they spoke to today" from their terminal, and historical information and emotional state, such as "Mr. Sato, happy" or "Mr. Yamada, tired," are displayed from the database.

[0488] These processes enable staff to efficiently identify customers and understand their emotional state. In addition, the quality of customer service is improved because conversation history and emotional state can be easily managed and referenced.

[0489] Specific prompt examples:

[0490] "Hello, can you please outline a system that uses voice, Bluetooth, and facial recognition to identify customers when they enter your store, analyze their emotions, and display their impressions? Explain specifically how the system works."

[0491] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0492] Step 1:

[0493] The device captures the user's speech with a microphone. The input is the user's speech. The device captures this as audio data and sends the audio data to the Google Cloud Speech-to-Text API.

[0494] Step 2:

[0495] The server uses the Google Cloud Speech-to-Text API to convert the transmitted voice data into text data. The input is the voice data. The output is the converted text data.

[0496] Step 3:

[0497] The server compares the converted text data with the database. The input is the text data. Based on the user information stored in the database, matching user information is obtained as a comparison result. The output is the user information.

[0498] Step 4:

[0499] The device uses the emotion engine (Affectiva SDK) to recognize the user's emotional state from voice data. The input is voice data. The emotion engine analyzes the voice features and recognizes the emotional state. The output is the recognized emotional state.

[0500] Step 5:

[0501] The terminal displays the user's name and the recognized emotional state based on the matching result. The input is user information and emotional state, which are visually displayed on the terminal screen. The output is a screen displaying the user's name and emotional state.

[0502] Step 6:

[0503] The terminal detects the device ID via near-field communication using a Bluetooth or NFC reader. The input is the Bluetooth signal or NFC tag of the device held by the user. The output is the detected device ID.

[0504] Step 7:

[0505] The server uses the detected device ID to check against the database and obtain matching user information. The input is the device ID. The database is checked and the user information is obtained. The output is the user information.

[0506] Step 8:

[0507] Use the device camera to capture a user's face image. The input is a face image. Capture face image data and send it to the Microsoft Azure Face API. The output is the captured face image data.

[0508] Step 9:

[0509] The server uses the Microsoft Azure Face API to compare face image data with a database and obtain user information. The input is face image data. The API is used to perform face recognition and compare the data with the database to obtain user information. The output is user information.

[0510] Step 10:

[0511] The terminal sends facial image data to the emotion engine to recognize the user's emotional state. The input is the facial image data. The emotion engine analyzes the facial features and recognizes the emotional state. The output is the recognized emotional state.

[0512] Step 11:

[0513] The terminal displays user information and the recognized emotional state. The input is user information and emotional state. These are visually displayed on the terminal screen. The output is a screen showing the user's name and emotional state.

[0514] Step 12:

[0515] At the end of the conversation, the device sends information such as user information, timestamp, location, and emotional state to the server. The input is user information, timestamp, location, and emotional state. This data is temporarily stored and sent to the server. The output is the history data sent to the server.

[0516] Step 13:

[0517] When a user wants to refer to their past communication history, the history information and emotional state are obtained from the database through the terminal. The input is a reference request. The server extracts the user's history data from the history database and sends it to the terminal. The output is the history information and emotional state that can be referred to.

[0518] 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.

[0519] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0520] 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.

[0521] [Second embodiment]

[0522] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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).

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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."

[0534] The present invention relates to a system that enables general affairs staff and receptionists in an office to quickly and accurately identify employees and visitors. This system provides the function of identifying and displaying the user's name by combining voice data, short-range communication means, and facial recognition technology. Specific embodiments are described below.

[0535] 1. Audio Identification

[0536] The device has the ability to capture voice data when a user speaks. For example, speech is recognized using a microphone installed on the desk of a general affairs employee. After capturing the voice data, it is encoded and sent to a server. The server uses a voice recognition engine to convert the voice data into text data. This converted text data is compared with a database to obtain matching user information. Based on the comparison results, the device displays the user's name in a pop-up window.

[0537] Example: When a user says "Hello, I'm Tanaka," the voice is captured by a microphone and the voice data is sent to the server. The voice recognition engine converts this into text data "Hello, I'm Tanaka," and information about "Tanaka" is retrieved from the database. Finally, "Tanaka-san" is displayed on the device.

[0538] 2. Identification by short-range communication means

[0539] The device uses Bluetooth or an NFC reader to detect an approaching device or employee ID. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information. The device then displays a pop-up with the user's name based on the match.

[0540] Example: When a user (employee) approaches the general affairs desk with their iPhone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which checks it against the database and retrieves the information for "Sato." Finally, "Mr. Sato" is displayed on the device.

[0541] 3. Facial Recognition Identification

[0542] The device's camera captures the user's face. The captured facial image data is sent to the server. The server uses a facial recognition engine to match the facial image data with a database and obtains matching user information. The device then displays the user's name in a pop-up based on the matching results.

[0543] Example: When a user stands in front of the general affairs desk, a camera captures a facial image. The facial image data is sent to a server, where a facial recognition engine performs a match and obtains information about "Yamada." Finally, "Yamada-san" is displayed on the terminal.

[0544] 4. Historical Data Management

[0545] The terminal has a function to temporarily store information about the user who spoke to it. When the conversation ends, information such as the user's information (user ID), time, and location is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the historical information is retrieved from the database via the terminal and displayed.

[0546] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and history information such as "Tanaka-san, Sato-san" is displayed from the database.

[0547] This allows general affairs personnel to efficiently identify employees and easily manage and view interaction history.

[0548] The processing flow will be explained below.

[0549] Voice Identification

[0550] Step 1:

[0551] The terminal uses a microphone to capture voice data when the user speaks.

[0552] Step 2:

[0553] The terminal encodes the captured audio data and transmits it to a server via a network.

[0554] Step 3:

[0555] The server decodes the encoded voice data and passes it to a voice recognition engine.

[0556] Step 4:

[0557] The server uses a speech recognition engine to convert the voice data into text data.

[0558] Step 5:

[0559] The server extracts the name portion from the converted text data and compares it with user information in a database.

[0560] Step 6:

[0561] The server returns the result of the check to the terminal.

[0562] Step 7:

[0563] The terminal displays the received matching result (user's name) in a pop-up.

[0564] Identification by short-range communication means

[0565] Step 1:

[0566] The device uses Bluetooth or an NFC reader to detect signals from nearby devices.

[0567] Step 2:

[0568] The terminal encodes the detected device ID and transmits it to the server via the network.

[0569] Step 3:

[0570] The server decodes the received device ID and checks it against the user information in the database.

[0571] Step 4:

[0572] The server returns the result of the check to the terminal.

[0573] Step 5:

[0574] The terminal displays the received matching result (user's name) in a pop-up.

[0575] Facial Recognition Identification

[0576] Step 1:

[0577] The terminal uses a camera to capture an image of the user's face.

[0578] Step 2:

[0579] The terminal encodes the captured facial image data and transmits it to a server via a network.

[0580] Step 3:

[0581] The server decodes the received facial image data and passes it to the facial recognition engine.

[0582] Step 4:

[0583] The server uses a facial recognition engine to match the facial image data with user information in a database.

[0584] Step 5:

[0585] The server returns the result of the check to the terminal.

[0586] Step 6:

[0587] The terminal displays the received matching result (user's name) in a pop-up.

[0588] Historical Data Management

[0589] Step 1:

[0590] The device temporarily stores user information obtained based on voice, device ID, and facial information.

[0591] Step 2:

[0592] When the conversation ends, the terminal encodes the user information, timestamp, and location and transmits them to the server via the network.

[0593] Step 3:

[0594] The server records the received conversation information in a database.

[0595] Step 4:

[0596] The user operates the terminal to request the history data.

[0597] Step 5:

[0598] The terminal encodes the request and sends it over the network to the server.

[0599] Step 6:

[0600] The server searches the database for the relevant history data and returns the search results to the terminal.

[0601] Step 7:

[0602] The terminal displays the received history data to the user.

[0603] By following these steps, general affairs staff can efficiently identify employees and manage and view their interaction history.

[0604] Example 1

[0605] 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."

[0606] There is a need for a method that enables office staff and receptionists to quickly and accurately identify employees and visitors. A key challenge is improving accuracy by combining various methods, such as voice, short-range communication, and facial recognition. Also important is the ability to manage and reference user history information after identification.

[0607] 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.

[0608] In this invention, the server includes a means for converting voice data into text data, a means for comparing the converted text data with a database to acquire user information, a means for comparing a device ID with a database to acquire user information, a means for comparing face image data with a database to acquire user information, and a means for managing history information. This allows general affairs staff and reception staff to quickly and accurately identify employees and visitors using a variety of methods. Furthermore, by managing and referencing past communication history, work efficiency can be improved.

[0609] "Voice data" is data that represents the voice signal emitted by the user in digital form.

[0610] "Encoding" is the process of converting and compressing digital data according to certain rules, and is a technology used to efficiently transfer and store data.

[0611] A "server" is a computer system that provides services to other computers and terminals on a network, and plays a central role in data processing and information management.

[0612] A "voice recognition engine" is a software component that analyzes voice data and converts the content into text data.

[0613] "Text data" is a data format that expresses information using characters and symbols, and is character string data generated by a voice recognition engine.

[0614] A "database" is a collection of data that is constructed to efficiently search, manage, and update large amounts of data, and the data is organized based on specific rules.

[0615] "User information" is information used to identify individual users, and includes identifiers such as names and employee numbers.

[0616] "Short-range communication means" refers to technology that enables communication between devices over short distances using Bluetooth, NFC, etc.

[0617] A "device ID" is an identifier that uniquely identifies an individual device, and is specific information assigned to Bluetooth or NFC devices.

[0618] "Facial image data" is data that digitally represents an image of a user's face, and is captured by a camera.

[0619] A "face recognition engine" is a software component that analyzes facial image data and matches its contents with existing information in a database.

[0620] "History information" refers to information that includes records of past interactions and identifications with a user, including data such as user ID, time, and location.

[0621] This invention relates to a system that enables general affairs staff and receptionists in an office to quickly and accurately identify employees and visitors. This system provides the function of identifying and displaying the user's name by combining voice data, short-range communication means, and face recognition technology. The specific configuration and operation of this system are described below.

[0622] Voice Identification

[0623] The device is equipped with a function to capture voice data when a user speaks. Specifically, the user's speech is recognized using a microphone installed on the general affairs employee's desk. After capturing the voice data, it is encoded and sent to the server. The server then converts the voice data into text data using a speech recognition engine (e.g., Google Cloud Speech-to-Text API). This converted text data is then compared with a database to obtain matching user information. Finally, the device displays the user's name as a pop-up based on the comparison results.

[0624] Example: When a user says "Hello, I'm Tanaka," the voice is captured by a microphone and the voice data is sent to the server. The voice recognition engine converts this into text data "Hello, I'm Tanaka," and information about "Tanaka" is retrieved from the database. Finally, "Tanaka-san" is displayed on the device.

[0625] Example prompt sentence:

[0626] "General office system that identifies individuals using their voice and displays their name. User says 'Hello, I'm Tanaka.' System captures voice, sends to server which converts it to text and fetches 'Tanaka' from the database to be displayed on the terminal."

[0627] Identification by short-range communication means

[0628] The device uses Bluetooth or an NFC reader to detect an approaching device or employee ID. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information. The device then displays a pop-up with the user's name based on the match.

[0629] Example: When a user (employee) approaches the general affairs desk with their smartphone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which checks it against the database and retrieves the information for "Sato." Finally, "Mr. Sato" is displayed on the device.

[0630] Example prompt sentence:

[0631] "General office system that identifies individuals using NFC or Bluetooth technology and displays their name. User with smartphone approaches, system reads Bluetooth ID and fetches 'Sato' from database to be displayed on the terminal."

[0632] Facial Recognition Identification

[0633] The device's camera captures the user's face. The captured facial image data is encoded and sent to the server. The server uses a facial recognition engine (e.g., Amazon Rekognition) to match the facial image data with a database and obtain matching user information. The device then displays the user's name in a pop-up based on the matching results.

[0634] Example: When a user stands in front of the general affairs desk, a camera captures a facial image. The facial image data is sent to a server, where a facial recognition engine performs a match and obtains information about "Yamada." Finally, "Yamada-san" is displayed on the terminal.

[0635] Example prompt sentence:

[0636] "General office system that identifies individuals using facial recognition technology and displays their name. User stands in front of the desk, camera captures face, server processes and fetches 'Yamada' from the database to be displayed on the terminal."

[0637] Historical Data Management

[0638] The terminal has a function to temporarily store information about the user who spoke to it. When the conversation ends, information such as the user's information (user ID), time, and location is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the historical information is retrieved from the database via the terminal and displayed.

[0639] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and history information such as "Tanaka-san, Sato-san" is displayed from the database.

[0640] Example prompt sentence:

[0641] "General office system that manages conversation history including user ID, timestamp, and location. User greets employees, data is recorded and can later be retrieved to see 'talked to Tanaka and Sato'."

[0642] As described above, this system combines voice recognition, short-range communication, and facial recognition to efficiently and accurately identify users and support the work of general affairs personnel. Furthermore, by managing historical information, past conversation records can be easily referenced.

[0643] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0644] Voice Identification

[0645] Step 1:

[0646] The device captures voice data. When a user speaks, a microphone installed on the general affairs staff member's desk captures the user's speech as voice data.

[0647] Input: User's voice

[0648] Output: Captured audio data

[0649] Step 2:

[0650] The device encodes the captured audio data and sends it to the server. Encoding makes data transfer and storage more efficient.

[0651] Input: Captured audio data

[0652] Output: Encoded audio data

[0653] Step 3:

[0654] The server converts the voice data into text data using a speech recognition engine, for example, the Google Cloud Speech-to-Text API.

[0655] Input: Encoded audio data

[0656] Output: Text data

[0657] Step 4:

[0658] The server compares the text data with a database to obtain user information, using a matching algorithm to search for names and other characters within the text data.

[0659] Input: Text data

[0660] Output: User information as a match result

[0661] Step 5:

[0662] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[0663] Input: User information as a match result

[0664] Output: On-screen popup

[0665] Identification by short-range communication means

[0666] Step 1:

[0667] The terminal detects near-field communication devices. When a user approaches the general affairs desk, a Bluetooth or NFC reader captures the device ID.

[0668] Input: User's device signal

[0669] Output: Device ID

[0670] Step 2:

[0671] The device detects the device ID, encodes it, and sends it to the server.

[0672] Input: Device ID

[0673] Output: Encoded device ID

[0674] Step 3:

[0675] The server uses the device ID to check against the database to obtain user information. The check yields matching user information.

[0676] Input: Encoded device ID

[0677] Output: User information as a match result

[0678] Step 4:

[0679] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[0680] Input: User information as a match result

[0681] Output: On-screen popup

[0682] Facial Recognition Identification

[0683] Step 1:

[0684] The device's camera captures the user's face, and a facial image is acquired when the user stands in front of the general affairs desk.

[0685] Input: User's face

[0686] Output: Face image data

[0687] Step 2:

[0688] The terminal encodes the captured facial image data and transmits it to the server.

[0689] Input: Face image data

[0690] Output: Encoded face image data

[0691] Step 3:

[0692] The server uses facial image data to match it with a database and obtain user information. A facial recognition engine such as Amazon Rekognition is used.

[0693] Input: Encoded face image data

[0694] Output: User information as a match result

[0695] Step 4:

[0696] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[0697] Input: User information as a match result

[0698] Output: On-screen popup

[0699] Historical Data Management

[0700] Step 1:

[0701] The device temporarily stores user information when a conversation ends, and also records the timing and location of the conversation.

[0702] Input: User information, timestamp, and location after conversation ends

[0703] Output: Temporarily held information

[0704] Step 2:

[0705] The terminal transmits the user information to the server.

[0706] Input: Temporarily held information

[0707] Output: User information sent

[0708] Step 3:

[0709] The server records the received user information in a database, including the user ID, time, and location.

[0710] Input: Submitted user information

[0711] Output: Information recorded in the database

[0712] Step 4:

[0713] The terminal acquires and displays history information. This is used when the user wants to refer to the history of past interactions.

[0714] Input: Request History Information

[0715] Output: Display history information

[0716] (Application example 1)

[0717] 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."

[0718] To efficiently manage and support work within a factory, a system that can quickly and accurately identify managers and workers is necessary. However, existing identification systems often rely on a limited number of identification methods, and have the problem of being unable to handle a variety of situations. In addition, there is a lack of systems in place to properly execute managers' instructions and support workers.

[0719] 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.

[0720] In this invention, the server includes means for capturing voice data, means for converting the captured voice data into text data, means for comparing the converted text data with user information, means for displaying the user's name based on the comparison result, means for detecting a device ID using short-range communication means, means for comparing the detected device ID with user information, means for capturing a facial image, means for comparing the captured facial image with user information, and means for identifying a factory manager or worker based on the comparison result and providing appropriate work or support. This enables quick and accurate identification of managers and workers, enabling efficient management and work support within the factory.

[0721] "Voice data" refers to an acoustic signal that records the user's speech.

[0722] "Text data" is digital information that is obtained by converting voice data into a string of characters.

[0723] "User information" is a database record containing a user's identity.

[0724] "Verification" is the process of comparing and verifying acquired data with existing databases.

[0725] "Near-field communication means" refers to a means of exchanging data between devices using short-range wireless communication technologies such as Bluetooth and NFC.

[0726] A "device ID" is a unique identifier for identifying a particular device.

[0727] A "face image" is image data of a person's face photographed using a camera.

[0728] "Historical data" is data that records a user's activities and interactions.

[0729] A "factory manager" is a person in charge of operations and work instructions in a factory.

[0730] "Workers" are employees who perform physical tasks or operate equipment within a factory.

[0731] "Support" means providing support to workers to carry out their work efficiently.

[0732] "Management" means monitoring and adjusting the progress of operations and work within the factory.

[0733] The "matching result" is the user's identification information obtained as a result of database matching.

[0734] This invention is a system for identifying managers and workers in a factory and providing appropriate work support and management. This system integrates voice recognition, short-range communication, and face recognition functions, and can collate and display user information.

[0735] Voice recognition identification

[0736] The server is equipped with a microphone to capture voice data, and when a user speaks, the voice data is acquired. The acquired voice data is converted into text data using the speech_recognition library. The server then uses the converted text data to match the user information with the database and obtains matching information. The user's name as a matched result is displayed as a pop-up on the terminal.

[0737] Example: When a factory manager says, "Hello, I'm the manager," the voice is captured by a microphone, converted into text by a speech recognition engine on the server, and then matched with a database to identify the person as "manager."

[0738] Identification by short-range communication

[0739] The server uses Bluetooth or NFC readers to detect the ID of approaching devices and employees. The detected device ID is encoded and sent to the server, which then checks it against its database to obtain matching user information. The results are displayed in a pop-up on the terminal.

[0740] Example: When a worker approaches a factory with a smartphone in their pocket, their Bluetooth signal is detected and the server checks the database to identify them as a "worker."

[0741] Facial Recognition Identification

[0742] The server uses a camera to capture a facial image and sends the facial image data to the server. The server uses a facial recognition engine to match the facial image data with a database and obtains matching user information. The results are displayed on the terminal.

[0743] Example: When a manager stops at the entrance to a factory, a camera captures his / her facial image, which is then matched against a database by a facial recognition engine and identified as a "manager."

[0744] Historical Data Management

[0745] The server has a function to temporarily store information about the user who spoke to it, and when the conversation ends, records the user information, time, location, and other information in a database, allowing users to refer to their past communication history.

[0746] For example, at the end of the day, when a manager requests from a terminal "Who did you talk to today?", the server retrieves historical information such as "Worker 1, Engineer A" from the database and displays it on the terminal.

[0747] Specific examples

[0748] For example, if a factory manager approaches a robot, the robot will detect the manager's Bluetooth ID and greet him with, "Hello, manager!" If the manager instructs the robot to "check the work efficiency," the robot will analyze the work efficiency of the entire factory in real time and report it.

[0749] An example of a prompt sentence to be input to the generative AI model to realize this system is as follows:

[0750] "Please explain an example of a system implementation that uses voice recognition, near-field communication, and facial recognition technology to enable robots working in a factory to identify factory managers and workers and take appropriate action."

[0751] This makes it possible to efficiently identify managers and workers and effectively manage and support operations within the factory.

[0752] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0753] Step 1:

[0754] Capture audio data

[0755] If the user is a manager or worker, voice data is captured by speaking into a microphone, which is a raw acoustic signal.

[0756] Step 2:

[0757] Converting audio data to text

[0758] The server converts the captured audio data into text data using the speech_recognition library. The input is audio data, and the output is the corresponding text data. The audio waveform data is analyzed and string data is generated based on a language model.

[0759] Step 3:

[0760] Matching text data with user information

[0761] The server checks the converted text data against the user information in the database. The input to this process is the text data, and the output is the matching user information. The server compares the text data with the user information database.

[0762] Step 4:

[0763] Viewing User Information

[0764] The server pops up the user's name on the terminal based on the match result. The input of this process is the match result, and the output is the user's name displayed on the terminal.

[0765] Step 5:

[0766] Device ID detection through short-range communication

[0767] The terminal uses Bluetooth or an NFC reader to detect the user's device ID. The input of this process is the surrounding device signal, and the output is the detected device ID.

[0768] Step 6:

[0769] Matching device ID with user information

[0770] The server checks the detected device ID against the user information in the database. The input to this process is the device ID, and the output is the matching user information. The server compares the device ID with the user information database.

[0771] Step 7:

[0772] Facial image capture

[0773] The terminal uses a camera to capture the user's face image, and the input of this process is the camera video signal and the output is the captured face image data.

[0774] Step 8:

[0775] Matching face images with user information

[0776] The server matches the captured facial image data with user information in a database. The input to this process is facial image data, and the output is the matching user information. The server uses a facial recognition engine to compare the facial image with the facial data in the database.

[0777] Step 9:

[0778] Integrated display of user information

[0779] The server identifies the user information by combining the results of matching the voice, device ID, and facial image, and displays it on the device. The input to this process is the three matching results, and the output is the final user information.

[0780] Step 10:

[0781] Historical Data Recording

[0782] The server records the user information, time, and location in a database. The inputs to this process are user information, timestamp, and location information, and the output is an updated database record.

[0783] By executing each step in this way, it is possible to identify managers and workers within the factory and provide appropriate support.

[0784] 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.

[0785] The present invention relates to a system that enables office staff and receptionists to quickly and accurately identify employees and visitors and grasp their emotional states. This system provides a function to identify and display the user's emotional state by combining voice data, short-range communication means, facial recognition technology, and an emotion engine. Specific embodiments are described below.

[0786] 1. Voice recognition and emotion recognition

[0787] The device has the ability to capture voice data when a user speaks. For example, a microphone installed on the desk of a general affairs employee can be used to recognize speech. The voice data is then sent to an emotion engine to recognize the user's emotional state. After capturing the voice data, it is encoded and sent to a server. The server then uses a voice recognition engine to convert the voice data into text data, and compares the converted text data with a database to obtain matching user information.

[0788] Example: When a user says "Hello, I'm Tanaka," the voice is captured by the microphone. At the same time, the emotion engine recognizes the emotional state of "Tanaka is happy." The voice data is sent to the server, where it is converted into text data "Hello, I'm Tanaka" by the speech recognition engine. Information about "Tanaka" is retrieved from the database, and finally "Tanaka" and his emotional state of "happy" are displayed on the device.

[0789] 2. Identification and emotion recognition using short-range communication methods

[0790] The terminal uses Bluetooth or an NFC reader to detect approaching devices or employee IDs. At the same time, it recognizes the user's emotional state from voice data and facial images. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information.

[0791] Example: When a user (employee) approaches the general affairs desk with their iPhone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which matches it with the database and retrieves the information of "Sato." Finally, the device displays "Sato-san" and his / her active emotional state.

[0792] 3. Facial Recognition and Emotion Recognition

[0793] The device's camera captures the user's face, and sends the facial image data to an emotion engine to recognize the user's emotional state. The captured facial image data is then sent to a server, where the facial recognition engine matches the facial image data with user information in a database.

[0794] Example: When a user stands in front of the general affairs desk, the camera captures a facial image. At the same time, the emotion engine recognizes the emotional state of "Yamada is tired." The facial image data is sent to the server, where it is matched by the facial recognition engine to obtain information about "Yamada." Finally, "Yamada" and his emotional state of "fatigue" are displayed on the terminal.

[0795] 4. Historical data management and emotion history

[0796] The device has the ability to temporarily store user information and emotional state obtained based on voice, device ID, and facial information. When the conversation ends, information such as user information, timestamp, location, and emotional state is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the device retrieves and displays the history information and emotional state from the database.

[0797] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and the database will display historical information and emotional state such as "Tanaka-san, happy" or "Sato-san, active."

[0798] This allows general affairs personnel to efficiently identify employees and understand their emotional state. In addition, the quality of communication is improved because conversation history and emotional state can be easily managed and referenced.

[0799] The processing flow will be explained below.

[0800] Speech and emotion recognition

[0801] Step 1:

[0802] The terminal uses a microphone to capture voice data when the user speaks.

[0803] Step 2:

[0804] The terminal encodes the captured audio data and transmits it to a server via a network.

[0805] Step 3:

[0806] The server decodes the encoded voice data and passes it to a voice recognition engine.

[0807] Step 4:

[0808] The server uses a speech recognition engine to convert the voice data into text data.

[0809] Step 5:

[0810] The server extracts the name portion from the converted text data and compares it with user information in a database.

[0811] Step 6:

[0812] At the same time, the server sends the voice data to the emotion engine to analyze the user's emotional state.

[0813] Step 7:

[0814] The server returns the matching results and emotion analysis results to the terminal.

[0815] Step 8:

[0816] The device displays the received matching result (user's name) and emotional state in a pop-up.

[0817] Identification and emotion recognition using short-range communication methods

[0818] Step 1:

[0819] The device uses Bluetooth or an NFC reader to detect signals from nearby devices.

[0820] Step 2:

[0821] The terminal encodes the detected device ID and transmits it to the server via the network.

[0822] Step 3:

[0823] The server decodes the received device ID and checks it against the user information in the database.

[0824] Step 4:

[0825] The server returns the result of the check to the terminal.

[0826] Step 5:

[0827] At the same time, the device sends the captured voice data and facial images to the emotion engine to analyze the user's emotional state.

[0828] Step 6:

[0829] The server returns the matching results and emotion analysis results to the terminal.

[0830] Step 7:

[0831] The device displays the received matching result (user's name) and emotional state in a pop-up.

[0832] Facial recognition and emotion recognition

[0833] Step 1:

[0834] The terminal uses a camera to capture an image of the user's face.

[0835] Step 2:

[0836] The terminal encodes the captured facial image data and transmits it to a server via a network.

[0837] Step 3:

[0838] The server decodes the received facial image data and passes it to the facial recognition engine.

[0839] Step 4:

[0840] The server uses a facial recognition engine to match the facial image data with user information in a database.

[0841] Step 5:

[0842] The server returns the result of the check to the terminal.

[0843] Step 6:

[0844] At the same time, the device sends the captured facial image to the emotion engine to analyze the user's emotional state.

[0845] Step 7:

[0846] The server returns the matching results and emotion analysis results to the terminal.

[0847] Step 8:

[0848] The device displays the received matching result (user's name) and emotional state in a pop-up.

[0849] Historical data management and emotion history

[0850] Step 1:

[0851] The device temporarily stores user information obtained based on voice, device ID, facial information, and emotional state.

[0852] Step 2:

[0853] When the conversation ends, the terminal encodes the user information, timestamp, location, and emotional state and transmits them to the server via the network.

[0854] Step 3:

[0855] The server records the received conversation information in a database.

[0856] Step 4:

[0857] The user operates the terminal to request the history data.

[0858] Step 5:

[0859] The terminal encodes the request and sends it over the network to the server.

[0860] Step 6:

[0861] The server searches the database for the relevant history data and emotional state, and returns the search results to the terminal.

[0862] Step 7:

[0863] The terminal displays the received historical data and emotional state to the user.

[0864] This process flow allows general affairs personnel to efficiently identify employees and understand their emotional state. It also allows for easy management and reference of conversation history and emotional state, improving the quality of communication.

[0865] Example 2

[0866] 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."

[0867] In an office, it is important for general affairs staff and receptionists to quickly and accurately identify employees and visitors and understand their emotional state, but doing this manually is time-consuming and prone to human error. Furthermore, understanding a user's emotional state when interacting with them allows for more appropriate responses, but this is also difficult to do manually. Furthermore, there is a need for a function to manage past communication history and emotional state and refer to them later.

[0868] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for capturing voice data, means for transmitting the captured voice data to an emotion engine to recognize an emotional state, means for converting the captured voice data into text data, means for comparing the converted text data with user information, and means for displaying the user's name and emotional state based on the comparison result. This allows for automatic identification of the user and recognition of their emotional state, thereby improving the efficiency of the work of general affairs staff and reception staff and enabling more appropriate responses.

[0869] "Voice data" is information that digitally captures a user's speech.

[0870] "Capturing means" refers to a combination of hardware and software for capturing audio data and facial images.

[0871] The "emotion engine" is software that analyzes and recognizes the user's emotional state from acquired voice data and facial images.

[0872] The "means for converting into text data" is software that includes voice recognition technology that converts voice data into text information.

[0873] "User information" refers to identification information and attribute information about individual users that is stored in a database.

[0874] "Means for matching" refers to the process of comparing acquired voice data, text data, device ID, or facial image data with a database to identify matching user information.

[0875] "Device ID" is a unique identifier of a device obtained by short-range communication means.

[0876] A "face image" is image data that digitally represents a user's face.

[0877] The "displaying means" refers to a display device or software for visually displaying the acquired user information and emotional state.

[0878] "Short-range communication means" refers to communication technologies for exchanging data over short distances, such as Bluetooth and NFC.

[0879] A "database" is an information management system for systematically organizing and storing multiple user information.

[0880] This invention relates to a system that enables office staff and receptionists to quickly and accurately identify employees and visitors and grasp their emotional state. This system uses voice data, short-range communication means, and face recognition technology, as well as an emotion engine to identify and display the user's emotional state.

[0881] 1. Voice recognition and emotion recognition

[0882] The device uses a microphone to capture voice data when the user speaks. For example, a microphone installed on a desk captures the user's voice, such as "Hello, I'm Tanaka." This voice data is sent to an emotion engine, which analyzes the user's emotional state and recognizes, for example, "joy." The voice data is then encoded and sent to the server. The server uses a voice recognition engine to convert the voice data into text data, and compares the text data with a database to obtain matching user information.

[0883] Example: When a user says "Hello, I'm Tanaka," the microphone captures the voice. At the same time, the emotion engine recognizes that "Tanaka-san is happy." The voice data is sent to the server, where it is converted into "Hello, I'm Tanaka-san" by the speech recognition engine. Information about "Tanaka-san" is retrieved from the database, and "Tanaka-san" and his emotional state of "happy" are displayed on the device.

[0884] Example prompt: Describe the data processing flow when a user says, "Hello, I'm Tanaka."

[0885] 2. Identification and emotion recognition using short-range communication methods

[0886] The terminal uses Bluetooth or an NFC reader to detect approaching devices or employee IDs. At the same time, it recognizes the user's emotional state from voice data and facial images. The detected device ID is encoded and sent to the server, which then compares it with a database to obtain matching user information.

[0887] Example: When a user (employee) approaches the general affairs desk with their smartphone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which compares it with the database and retrieves information about "Mr. Sato." The device displays "Mr. Sato" and his active emotional state.

[0888] Example prompt: Describe the data processing flow when a user approaches the administration desk and a Bluetooth signal is detected.

[0889] 3. Facial Recognition and Emotion Recognition

[0890] The device's camera captures a user's facial image, and sends the facial image data to an emotion engine to recognize the user's emotional state. This facial image data is then sent to a server, which uses a facial recognition engine to match the facial image data with user information in a database.

[0891] Example: When a user stands in front of the general affairs desk, the camera captures a facial image. At the same time, the emotion engine recognizes the user's emotional state as "Yamada-san is tired." The facial image data is sent to the server, and the facial recognition engine obtains information about "Yamada-san." The device displays "Yamada-san" and his emotional state of "fatigue."

[0892] Example prompt: Describe the process flow for facial and emotion recognition when a user stands in front of the general affairs desk.

[0893] 4. Historical data management and emotion history

[0894] The device temporarily stores user information and emotional state obtained based on voice, device ID, and facial information. When the conversation ends, this information is sent to the server and recorded in a database. When the user wants to view their past communication history, the device retrieves the history information from the database on the server and displays it on the device.

[0895] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and the database will display historical information and emotional state such as "Tanaka-san, happy" or "Sato-san, active."

[0896] This allows general affairs personnel to efficiently identify employees and understand their emotional state. In addition, the quality of communication is improved because conversation history and emotional state can be easily managed and referenced.

[0897] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0898] 1. Voice recognition and emotion recognition

[0899] Step 1:

[0900] The device captures the user's voice data through a microphone. The input is the user's speech, and the output is the captured voice data. For example, a voice such as "Hello, I'm Tanaka" is captured by the microphone.

[0901] Step 2:

[0902] The device sends the captured voice data to the emotion engine, which analyzes the voice data and recognizes the user's emotional state. In this process, the input is the captured voice data, and the output is the emotional state obtained from the emotion engine, such as "joy."

[0903] Step 3:

[0904] The device encodes the audio data and sends it to the server. The input is the captured audio data and the recognized emotional state, and the output is the encoded audio data.

[0905] Step 4:

[0906] The server receives the transmitted voice data and converts it into text data using a speech recognition engine. The input is the encoded voice data, and the output is text data such as "Hello, I'm Tanaka."

[0907] Step 5:

[0908] The server compares the converted text data with the database to obtain matching user information. The input is text data, and the output is user information (e.g., "Mr. Tanaka") obtained from the database.

[0909] Step 6:

[0910] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[0911] Step 7:

[0912] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Mr. Tanaka" and "Joy").

[0913] 2. Identification and emotion recognition using short-range communication methods

[0914] Step 1:

[0915] The terminal detects the device ID using Bluetooth or an NFC reader. The input is the signal from the device held by the user, and the output is the detected device ID.

[0916] Step 2:

[0917] The device recognizes the emotional state from voice data and facial images. The input is voice data and facial images, and the output is the recognized emotional state.

[0918] Step 3:

[0919] The device encodes the device ID and sends it to the server. The input is the device ID and emotional state, and the output is the encoded data.

[0920] Step 4:

[0921] The server checks the received device ID against the database and retrieves the matching user information. The input is the encoded device ID, and the output is the user information retrieved from the database.

[0922] Step 5:

[0923] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[0924] Step 6:

[0925] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Mr. Sato" and "Active").

[0926] 3. Facial Recognition and Emotion Recognition

[0927] Step 1:

[0928] The device's camera captures the user's face image. The input is the user's face, and the output is the captured face image data.

[0929] Step 2:

[0930] The device sends the captured facial image to the emotion engine to recognize the emotional state. The input is the facial image data, and the output is the recognized emotional state.

[0931] Step 3:

[0932] The terminal encodes the facial image data and sends it to the server. The input is the facial image data and the emotional state, and the output is the encoded data.

[0933] Step 4:

[0934] The server passes the received facial image data to a facial recognition engine, which compares it with the database to obtain matching user information. The input is the encoded facial image data, and the output is the user information obtained from the database.

[0935] Step 5:

[0936] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[0937] Step 6:

[0938] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Yamada-san" and "fatigue").

[0939] 4. Historical data management and emotion history

[0940] Step 1:

[0941] The device temporarily stores the acquired user information and emotional state. The input is user information such as voice data, device ID, and facial information, as well as the emotional state, and the output is the temporarily stored data.

[0942] Step 2:

[0943] At the end of the conversation, the device encodes these data and sends them to the server. The input is the user information, timestamp, location, and emotional state at the end of each conversation, and the output is the encoded data.

[0944] Step 3:

[0945] The server stores the received history data in a database. The input is the encoded history data, and the output is the history information stored in the database.

[0946] Step 4:

[0947] When a user wants to view past communication history, the terminal sends a request to the server. The input is a view request, and the output is a send request.

[0948] Step 5:

[0949] The server retrieves the relevant history information and emotional state from the database and sends them to the terminal. The input is the reference request, and the output is the history information and emotional state.

[0950] Step 6:

[0951] The terminal displays the received history information and emotional state. The input is the history information and emotional state, and the output is the information displayed on the display.

[0952] This allows general affairs personnel to efficiently identify employees and understand their emotional state. The quality of communication is also improved because conversation history and emotional state can be easily managed and referenced.

[0953] (Application example 2)

[0954] 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."

[0955] In conventional brick-and-mortar stores, it is difficult to identify individual customers and grasp their emotional state, making it difficult to provide appropriate customer service to increase customer satisfaction. This can result in a decrease in customer satisfaction and a loss of repeat customers. Furthermore, it takes time and effort for customer service staff to grasp the information of all customers, making it inefficient. To solve these issues, a system is needed that can quickly and accurately identify customers who visit a store and grasp their emotional state.

[0956] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing voice data, means for converting the captured voice data into text data, means for comparing the converted text data with user information, means for displaying the user's name based on the comparison result, means for recognizing the user's emotional state using an emotion engine, and means for displaying the recognized emotional state. This makes it possible to quickly and accurately identify customers who visit a store and grasp their emotional state in real time.

[0957] "Audio data" refers to data in which audio is recorded in digital format.

[0958] "Capturing" means collecting and recording audio data, image data, etc. using a device.

[0959] "Text data" is data that represents text information such as letters and numbers in a digital format.

[0960] "Converting" is the process of changing data in one format into data in another format.

[0961] "User information" is information related to a user, such as personal identification information, name, and job title.

[0962] "Matching" is the process of comparing acquired data with existing data to see if they match.

[0963] "Display" means to visually represent characters or images on the screen of a device or the like.

[0964] "Short-range communication means" refers to short-range wireless communication technologies such as Bluetooth and NFC.

[0965] A "device ID" is an identifier uniquely assigned to each device.

[0966] A "face image" is an image of a person's face captured using a camera or the like.

[0967] An "emotion engine" is software or a system for analyzing and recognizing emotions from data such as voice and images.

[0968] "Emotional state" refers to a person's emotional state, such as joy, anger, sadness, or surprise.

[0969] A "server" is a computer system for storing and processing data.

[0970] This invention relates to a system for quickly and accurately identifying customers and grasping their emotional state in a brick-and-mortar store. The system provides the ability to recognize and display the emotional state of customers by combining voice data capture, near-field communication means, facial recognition technology, and an emotion engine.

[0971] The system configuration is as follows:

[0972] 1. Voice recognition and emotion recognition

[0973] The device has the ability to capture voice data when the user speaks. Specifically, it recognizes voice using the microphone of the smart device (smartphone or smart glasses). The voice data is converted into text data using the Google Cloud Speech-to-Text API. The converted text data is sent to a server, which compares it with a database to obtain matching user information. It also uses an emotion engine (Affectiva SDK) to recognize the user's emotional state from the voice data. The recognized emotional state is visually displayed on the device screen.

[0974] Example: When a user says "Hello, I'm Sato," the voice is captured by the microphone on the smart device. At the same time, the emotion engine recognizes the emotional state of "Mr. Sato is happy." The voice data is sent to the server, where the voice recognition engine converts it into text data of "Hello, I'm Sato." Information about "Sato" is retrieved from the database, and finally "Mr. Sato" and his emotional state of "joy" are displayed on the device.

[0975] 2. Identification and emotion recognition using short-range communication methods

[0976] The terminal uses Bluetooth and NFC readers to detect approaching customers' devices, while simultaneously recognizing their emotional state through voice data and facial images. Once the device ID is detected, it is sent to the server, where it is compared with a database to retrieve matching customer information.

[0977] Example: When a customer enters a store with a smart device, the Bluetooth signal is detected by the terminal. The device ID is sent to the server, which matches it with the database to obtain information about "Sato." Finally, the terminal displays "Sato-san" and his active emotional state.

[0978] 3. Facial Recognition and Emotion Recognition

[0979] The device's camera captures the customer's face. The facial image data is then sent to an emotion engine to recognize the user's emotional state. The captured facial image data is then sent to a server, where it is matched with user information in a database by a facial recognition engine (Microsoft Azure Face API).

[0980] Example: When a customer stands in front of a reception robot, a camera captures a facial image. At the same time, an emotion engine recognizes the customer's emotional state as "Yamada is tired." The facial image data is sent to a server, where it is collated by the facial recognition engine to obtain information about "Yamada." Finally, "Yamada" and his emotional state of "fatigue" are displayed on the terminal.

[0981] 4. Historical data management and emotion history

[0982] The device has the ability to temporarily store customer information and their emotional state obtained based on voice, device ID, and facial information. At the end of the conversation, information such as customer information, timestamp, location, and emotional state is sent to the server and recorded in a database. If the customer wants to view their past communication history, the device retrieves and displays the historical information and emotional state from the database.

[0983] Example: A customer speaks with multiple staff members during a morning greeting. At the end of each conversation, customer information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a staff member wants to refer to the history later, they can request "who they spoke to today" from their terminal, and historical information and emotional state, such as "Mr. Sato, happy" or "Mr. Yamada, tired," are displayed from the database.

[0984] These processes enable staff to efficiently identify customers and understand their emotional state. In addition, the quality of customer service is improved because conversation history and emotional state can be easily managed and referenced.

[0985] Specific prompt examples:

[0986] "Hello, can you please outline a system that uses voice, Bluetooth, and facial recognition to identify customers when they enter your store, analyze their emotions, and display their impressions? Explain specifically how the system works."

[0987] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0988] Step 1:

[0989] The device captures the user's speech with a microphone. The input is the user's speech. The device captures this as audio data and sends the audio data to the Google Cloud Speech-to-Text API.

[0990] Step 2:

[0991] The server uses the Google Cloud Speech-to-Text API to convert the transmitted voice data into text data. The input is the voice data. The output is the converted text data.

[0992] Step 3:

[0993] The server compares the converted text data with the database. The input is the text data. Based on the user information stored in the database, matching user information is obtained as a comparison result. The output is the user information.

[0994] Step 4:

[0995] The device uses the emotion engine (Affectiva SDK) to recognize the user's emotional state from voice data. The input is voice data. The emotion engine analyzes the voice features and recognizes the emotional state. The output is the recognized emotional state.

[0996] Step 5:

[0997] The terminal displays the user's name and the recognized emotional state based on the matching result. The input is user information and emotional state, which are visually displayed on the terminal screen. The output is a screen displaying the user's name and emotional state.

[0998] Step 6:

[0999] The terminal detects the device ID via near-field communication using a Bluetooth or NFC reader. The input is the Bluetooth signal or NFC tag of the device held by the user. The output is the detected device ID.

[1000] Step 7:

[1001] The server uses the detected device ID to check against the database and obtain matching user information. The input is the device ID. The database is checked and the user information is obtained. The output is the user information.

[1002] Step 8:

[1003] Use the device camera to capture a user's face image. The input is a face image. Capture face image data and send it to the Microsoft Azure Face API. The output is the captured face image data.

[1004] Step 9:

[1005] The server uses the Microsoft Azure Face API to compare face image data with a database and obtain user information. The input is face image data. The API is used to perform face recognition and compare the data with the database to obtain user information. The output is user information.

[1006] Step 10:

[1007] The terminal sends facial image data to the emotion engine to recognize the user's emotional state. The input is the facial image data. The emotion engine analyzes the facial features and recognizes the emotional state. The output is the recognized emotional state.

[1008] Step 11:

[1009] The terminal displays user information and the recognized emotional state. The input is user information and emotional state. These are visually displayed on the terminal screen. The output is a screen showing the user's name and emotional state.

[1010] Step 12:

[1011] At the end of the conversation, the device sends information such as user information, timestamp, location, and emotional state to the server. The input is user information, timestamp, location, and emotional state. This data is temporarily stored and sent to the server. The output is the history data sent to the server.

[1012] Step 13:

[1013] When a user wants to refer to their past communication history, the history information and emotional state are obtained from the database through the terminal. The input is a reference request. The server extracts the user's history data from the history database and sends it to the terminal. The output is the history information and emotional state that can be referred to.

[1014] 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.

[1015] 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.

[1016] 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.

[1017] [Third embodiment]

[1018] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1019] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1020] 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).

[1021] 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.

[1022] 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.

[1023] 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).

[1024] 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.

[1025] 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.

[1026] 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.

[1027] 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.

[1028] 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.

[1029] 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."

[1030] The present invention relates to a system that enables general affairs staff and receptionists in an office to quickly and accurately identify employees and visitors. This system provides the function of identifying and displaying the user's name by combining voice data, short-range communication means, and facial recognition technology. Specific embodiments are described below.

[1031] 1. Audio Identification

[1032] The device has the ability to capture voice data when a user speaks. For example, speech is recognized using a microphone installed on the desk of a general affairs employee. After capturing the voice data, it is encoded and sent to a server. The server uses a voice recognition engine to convert the voice data into text data. This converted text data is compared with a database to obtain matching user information. Based on the comparison results, the device displays the user's name in a pop-up window.

[1033] Example: When a user says "Hello, I'm Tanaka," the voice is captured by a microphone and the voice data is sent to the server. The voice recognition engine converts this into text data "Hello, I'm Tanaka," and information about "Tanaka" is retrieved from the database. Finally, "Tanaka-san" is displayed on the device.

[1034] 2. Identification by short-range communication means

[1035] The device uses Bluetooth or an NFC reader to detect an approaching device or employee ID. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information. The device then displays a pop-up with the user's name based on the match.

[1036] Example: When a user (employee) approaches the general affairs desk with their iPhone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which checks it against the database and retrieves the information for "Sato." Finally, "Mr. Sato" is displayed on the device.

[1037] 3. Facial Recognition Identification

[1038] The device's camera captures the user's face. The captured facial image data is sent to the server. The server uses a facial recognition engine to match the facial image data with a database and obtains matching user information. The device then displays the user's name in a pop-up based on the matching results.

[1039] Example: When a user stands in front of the general affairs desk, a camera captures a facial image. The facial image data is sent to a server, where a facial recognition engine performs a match and obtains information about "Yamada." Finally, "Yamada-san" is displayed on the terminal.

[1040] 4. Historical Data Management

[1041] The terminal has a function to temporarily store information about the user who spoke to it. When the conversation ends, information such as the user's information (user ID), time, and location is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the historical information is retrieved from the database via the terminal and displayed.

[1042] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and history information such as "Tanaka-san, Sato-san" is displayed from the database.

[1043] This allows general affairs personnel to efficiently identify employees and easily manage and view interaction history.

[1044] The processing flow will be explained below.

[1045] Voice Identification

[1046] Step 1:

[1047] The terminal uses a microphone to capture voice data when the user speaks.

[1048] Step 2:

[1049] The terminal encodes the captured audio data and transmits it to a server via a network.

[1050] Step 3:

[1051] The server decodes the encoded voice data and passes it to a voice recognition engine.

[1052] Step 4:

[1053] The server uses a speech recognition engine to convert the voice data into text data.

[1054] Step 5:

[1055] The server extracts the name portion from the converted text data and compares it with user information in a database.

[1056] Step 6:

[1057] The server returns the result of the check to the terminal.

[1058] Step 7:

[1059] The terminal displays the received matching result (user's name) in a pop-up.

[1060] Identification by short-range communication means

[1061] Step 1:

[1062] The device uses Bluetooth or an NFC reader to detect signals from nearby devices.

[1063] Step 2:

[1064] The terminal encodes the detected device ID and transmits it to the server via the network.

[1065] Step 3:

[1066] The server decodes the received device ID and checks it against the user information in the database.

[1067] Step 4:

[1068] The server returns the result of the check to the terminal.

[1069] Step 5:

[1070] The terminal displays the received matching result (user's name) in a pop-up.

[1071] Facial Recognition Identification

[1072] Step 1:

[1073] The terminal uses a camera to capture an image of the user's face.

[1074] Step 2:

[1075] The terminal encodes the captured facial image data and transmits it to a server via a network.

[1076] Step 3:

[1077] The server decodes the received facial image data and passes it to the facial recognition engine.

[1078] Step 4:

[1079] The server uses a facial recognition engine to match the facial image data with user information in a database.

[1080] Step 5:

[1081] The server returns the result of the check to the terminal.

[1082] Step 6:

[1083] The terminal displays the received matching result (user's name) in a pop-up.

[1084] Historical Data Management

[1085] Step 1:

[1086] The device temporarily stores user information obtained based on voice, device ID, and facial information.

[1087] Step 2:

[1088] When the conversation ends, the terminal encodes the user information, timestamp, and location and transmits them to the server via the network.

[1089] Step 3:

[1090] The server records the received conversation information in a database.

[1091] Step 4:

[1092] The user operates the terminal to request the history data.

[1093] Step 5:

[1094] The terminal encodes the request and sends it over the network to the server.

[1095] Step 6:

[1096] The server searches the database for the relevant history data and returns the search results to the terminal.

[1097] Step 7:

[1098] The terminal displays the received history data to the user.

[1099] By following these steps, general affairs staff can efficiently identify employees and manage and view their interaction history.

[1100] Example 1

[1101] 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."

[1102] There is a need for a method that enables office staff and receptionists to quickly and accurately identify employees and visitors. A key challenge is improving accuracy by combining various methods, such as voice, short-range communication, and facial recognition. Also important is the ability to manage and reference user history information after identification.

[1103] 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.

[1104] In this invention, the server includes a means for converting voice data into text data, a means for comparing the converted text data with a database to acquire user information, a means for comparing a device ID with a database to acquire user information, a means for comparing face image data with a database to acquire user information, and a means for managing history information. This allows general affairs staff and reception staff to quickly and accurately identify employees and visitors using a variety of methods. Furthermore, by managing and referencing past communication history, work efficiency can be improved.

[1105] "Voice data" is data that represents the voice signal emitted by the user in digital form.

[1106] "Encoding" is the process of converting and compressing digital data according to certain rules, and is a technology used to efficiently transfer and store data.

[1107] A "server" is a computer system that provides services to other computers and terminals on a network, and plays a central role in data processing and information management.

[1108] A "voice recognition engine" is a software component that analyzes voice data and converts the content into text data.

[1109] "Text data" is a data format that expresses information using characters and symbols, and is character string data generated by a voice recognition engine.

[1110] A "database" is a collection of data that is constructed to efficiently search, manage, and update large amounts of data, and the data is organized based on specific rules.

[1111] "User information" is information used to identify individual users, and includes identifiers such as names and employee numbers.

[1112] "Short-range communication means" refers to technology that enables communication between devices over short distances using Bluetooth, NFC, etc.

[1113] A "device ID" is an identifier that uniquely identifies an individual device, and is specific information assigned to Bluetooth or NFC devices.

[1114] "Facial image data" is data that digitally represents an image of a user's face, and is captured by a camera.

[1115] A "face recognition engine" is a software component that analyzes facial image data and matches its contents with existing information in a database.

[1116] "History information" refers to information that includes records of past interactions and identifications with a user, including data such as user ID, time, and location.

[1117] This invention relates to a system that enables general affairs staff and receptionists in an office to quickly and accurately identify employees and visitors. This system provides the function of identifying and displaying the user's name by combining voice data, short-range communication means, and face recognition technology. The specific configuration and operation of this system are described below.

[1118] Voice Identification

[1119] The device is equipped with a function to capture voice data when a user speaks. Specifically, the user's speech is recognized using a microphone installed on the general affairs employee's desk. After capturing the voice data, it is encoded and sent to the server. The server then converts the voice data into text data using a speech recognition engine (e.g., Google Cloud Speech-to-Text API). This converted text data is then compared with a database to obtain matching user information. Finally, the device displays the user's name as a pop-up based on the comparison results.

[1120] Example: When a user says "Hello, I'm Tanaka," the voice is captured by a microphone and the voice data is sent to the server. The voice recognition engine converts this into text data "Hello, I'm Tanaka," and information about "Tanaka" is retrieved from the database. Finally, "Tanaka-san" is displayed on the device.

[1121] Example prompt sentence:

[1122] "General office system that identifies individuals using their voice and displays their name. User says 'Hello, I'm Tanaka.' System captures voice, sends to server which converts it to text and fetches 'Tanaka' from the database to be displayed on the terminal."

[1123] Identification by short-range communication means

[1124] The device uses Bluetooth or an NFC reader to detect an approaching device or employee ID. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information. The device then displays a pop-up with the user's name based on the match.

[1125] Example: When a user (employee) approaches the general affairs desk with their smartphone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which checks it against the database and retrieves the information for "Sato." Finally, "Mr. Sato" is displayed on the device.

[1126] Example prompt sentence:

[1127] "General office system that identifies individuals using NFC or Bluetooth technology and displays their name. User with smartphone approaches, system reads Bluetooth ID and fetches 'Sato' from database to be displayed on the terminal."

[1128] Facial Recognition Identification

[1129] The device's camera captures the user's face. The captured facial image data is encoded and sent to the server. The server uses a facial recognition engine (e.g., Amazon Rekognition) to match the facial image data with a database and obtain matching user information. The device then displays the user's name in a pop-up based on the matching results.

[1130] Example: When a user stands in front of the general affairs desk, a camera captures a facial image. The facial image data is sent to a server, where a facial recognition engine performs a match and obtains information about "Yamada." Finally, "Yamada-san" is displayed on the terminal.

[1131] Example prompt sentence:

[1132] "General office system that identifies individuals using facial recognition technology and displays their name. User stands in front of the desk, camera captures face, server processes and fetches 'Yamada' from the database to be displayed on the terminal."

[1133] Historical Data Management

[1134] The terminal has a function to temporarily store information about the user who spoke to it. When the conversation ends, information such as the user's information (user ID), time, and location is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the historical information is retrieved from the database via the terminal and displayed.

[1135] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and history information such as "Tanaka-san, Sato-san" is displayed from the database.

[1136] Example prompt sentence:

[1137] "General office system that manages conversation history including user ID, timestamp, and location. User greets employees, data is recorded and can later be retrieved to see 'talked to Tanaka and Sato'."

[1138] As described above, this system combines voice recognition, short-range communication, and facial recognition to efficiently and accurately identify users and support the work of general affairs personnel. Furthermore, by managing historical information, past conversation records can be easily referenced.

[1139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1140] Voice Identification

[1141] Step 1:

[1142] The device captures voice data. When a user speaks, a microphone installed on the general affairs staff member's desk captures the user's speech as voice data.

[1143] Input: User's voice

[1144] Output: Captured audio data

[1145] Step 2:

[1146] The device encodes the captured audio data and sends it to the server. Encoding makes data transfer and storage more efficient.

[1147] Input: Captured audio data

[1148] Output: Encoded audio data

[1149] Step 3:

[1150] The server converts the voice data into text data using a speech recognition engine, for example, the Google Cloud Speech-to-Text API.

[1151] Input: Encoded audio data

[1152] Output: Text data

[1153] Step 4:

[1154] The server compares the text data with a database to obtain user information, using a matching algorithm to search for names and other characters within the text data.

[1155] Input: Text data

[1156] Output: User information as a match result

[1157] Step 5:

[1158] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[1159] Input: User information as a match result

[1160] Output: On-screen popup

[1161] Identification by short-range communication means

[1162] Step 1:

[1163] The terminal detects near-field communication devices. When a user approaches the general affairs desk, a Bluetooth or NFC reader captures the device ID.

[1164] Input: User's device signal

[1165] Output: Device ID

[1166] Step 2:

[1167] The device detects the device ID, encodes it, and sends it to the server.

[1168] Input: Device ID

[1169] Output: Encoded device ID

[1170] Step 3:

[1171] The server uses the device ID to check against the database to obtain user information. The check yields matching user information.

[1172] Input: Encoded device ID

[1173] Output: User information as a match result

[1174] Step 4:

[1175] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[1176] Input: User information as a match result

[1177] Output: On-screen popup

[1178] Facial Recognition Identification

[1179] Step 1:

[1180] The device's camera captures the user's face, and a facial image is acquired when the user stands in front of the general affairs desk.

[1181] Input: User's face

[1182] Output: Face image data

[1183] Step 2:

[1184] The terminal encodes the captured facial image data and transmits it to the server.

[1185] Input: Face image data

[1186] Output: Encoded face image data

[1187] Step 3:

[1188] The server uses facial image data to match it with a database and obtain user information. A facial recognition engine such as Amazon Rekognition is used.

[1189] Input: Encoded face image data

[1190] Output: User information as a match result

[1191] Step 4:

[1192] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[1193] Input: User information as a match result

[1194] Output: On-screen popup

[1195] Historical Data Management

[1196] Step 1:

[1197] The device temporarily stores user information when a conversation ends, and also records the timing and location of the conversation.

[1198] Input: User information, timestamp, and location after conversation ends

[1199] Output: Temporarily held information

[1200] Step 2:

[1201] The terminal transmits the user information to the server.

[1202] Input: Temporarily held information

[1203] Output: User information sent

[1204] Step 3:

[1205] The server records the received user information in a database, including the user ID, time, and location.

[1206] Input: Submitted user information

[1207] Output: Information recorded in the database

[1208] Step 4:

[1209] The terminal acquires and displays history information. This is used when the user wants to refer to the history of past interactions.

[1210] Input: Request History Information

[1211] Output: Display history information

[1212] (Application example 1)

[1213] 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."

[1214] To efficiently manage and support work within a factory, a system that can quickly and accurately identify managers and workers is necessary. However, existing identification systems often rely on a limited number of identification methods, and have the problem of being unable to handle a variety of situations. In addition, there is a lack of systems in place to properly execute managers' instructions and support workers.

[1215] 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.

[1216] In this invention, the server includes means for capturing voice data, means for converting the captured voice data into text data, means for comparing the converted text data with user information, means for displaying the user's name based on the comparison result, means for detecting a device ID using short-range communication means, means for comparing the detected device ID with user information, means for capturing a facial image, means for comparing the captured facial image with user information, and means for identifying a factory manager or worker based on the comparison result and providing appropriate work or support. This enables quick and accurate identification of managers and workers, enabling efficient management and work support within the factory.

[1217] "Voice data" refers to an acoustic signal that records the user's speech.

[1218] "Text data" is digital information that is obtained by converting voice data into a string of characters.

[1219] "User information" is a database record containing a user's identity.

[1220] "Verification" is the process of comparing and verifying acquired data with existing databases.

[1221] "Near-field communication means" refers to a means of exchanging data between devices using short-range wireless communication technologies such as Bluetooth and NFC.

[1222] A "device ID" is a unique identifier for identifying a particular device.

[1223] A "face image" is image data of a person's face photographed using a camera.

[1224] "Historical data" is data that records a user's activities and interactions.

[1225] A "factory manager" is a person in charge of operations and work instructions in a factory.

[1226] "Workers" are employees who perform physical tasks or operate equipment within a factory.

[1227] "Support" means providing support to workers to carry out their work efficiently.

[1228] "Management" means monitoring and adjusting the progress of operations and work within the factory.

[1229] The "matching result" is the user's identification information obtained as a result of database matching.

[1230] This invention is a system for identifying managers and workers in a factory and providing appropriate work support and management. This system integrates voice recognition, short-range communication, and face recognition functions, and can collate and display user information.

[1231] Voice recognition identification

[1232] The server is equipped with a microphone to capture voice data, and when a user speaks, the voice data is acquired. The acquired voice data is converted into text data using the speech_recognition library. The server then uses the converted text data to match the user information with the database and obtains matching information. The user's name as a matched result is displayed as a pop-up on the terminal.

[1233] Example: When a factory manager says, "Hello, I'm the manager," the voice is captured by a microphone, converted into text by a speech recognition engine on the server, and then matched with a database to identify the person as "manager."

[1234] Identification by short-range communication

[1235] The server uses Bluetooth or NFC readers to detect the ID of approaching devices and employees. The detected device ID is encoded and sent to the server, which then checks it against its database to obtain matching user information. The results are displayed in a pop-up on the terminal.

[1236] Example: When a worker approaches a factory with a smartphone in their pocket, their Bluetooth signal is detected and the server checks the database to identify them as a "worker."

[1237] Facial Recognition Identification

[1238] The server uses a camera to capture a facial image and sends the facial image data to the server. The server uses a facial recognition engine to match the facial image data with a database and obtains matching user information. The results are displayed on the terminal.

[1239] Example: When a manager stops at the entrance to a factory, a camera captures his / her facial image, which is then matched against a database by a facial recognition engine and identified as a "manager."

[1240] Historical Data Management

[1241] The server has a function to temporarily store information about the user who spoke to it, and when the conversation ends, records the user information, time, location, and other information in a database, allowing users to refer to their past communication history.

[1242] For example, at the end of the day, when a manager requests from a terminal "Who did you talk to today?", the server retrieves historical information such as "Worker 1, Engineer A" from the database and displays it on the terminal.

[1243] Specific examples

[1244] For example, if a factory manager approaches a robot, the robot will detect the manager's Bluetooth ID and greet him with, "Hello, manager!" If the manager instructs the robot to "check the work efficiency," the robot will analyze the work efficiency of the entire factory in real time and report it.

[1245] An example of a prompt sentence to be input to the generative AI model to realize this system is as follows:

[1246] "Please explain an example of a system implementation that uses voice recognition, near-field communication, and facial recognition technology to enable robots working in a factory to identify factory managers and workers and take appropriate action."

[1247] This makes it possible to efficiently identify managers and workers and effectively manage and support operations within the factory.

[1248] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1249] Step 1:

[1250] Capture audio data

[1251] If the user is a manager or worker, voice data is captured by speaking into a microphone, which is a raw acoustic signal.

[1252] Step 2:

[1253] Converting audio data to text

[1254] The server converts the captured audio data into text data using the speech_recognition library. The input is audio data, and the output is the corresponding text data. The audio waveform data is analyzed and string data is generated based on a language model.

[1255] Step 3:

[1256] Matching text data with user information

[1257] The server checks the converted text data against the user information in the database. The input to this process is the text data, and the output is the matching user information. The server compares the text data with the user information database.

[1258] Step 4:

[1259] Viewing User Information

[1260] The server pops up the user's name on the terminal based on the match result. The input of this process is the match result, and the output is the user's name displayed on the terminal.

[1261] Step 5:

[1262] Device ID detection through short-range communication

[1263] The terminal uses Bluetooth or an NFC reader to detect the user's device ID. The input of this process is the surrounding device signal, and the output is the detected device ID.

[1264] Step 6:

[1265] Matching device ID with user information

[1266] The server checks the detected device ID against the user information in the database. The input to this process is the device ID, and the output is the matching user information. The server compares the device ID with the user information database.

[1267] Step 7:

[1268] Facial image capture

[1269] The terminal uses a camera to capture the user's face image, and the input of this process is the camera video signal and the output is the captured face image data.

[1270] Step 8:

[1271] Matching face images with user information

[1272] The server matches the captured facial image data with user information in a database. The input to this process is facial image data, and the output is the matching user information. The server uses a facial recognition engine to compare the facial image with the facial data in the database.

[1273] Step 9:

[1274] Integrated display of user information

[1275] The server identifies the user information by combining the results of matching the voice, device ID, and facial image, and displays it on the device. The input to this process is the three matching results, and the output is the final user information.

[1276] Step 10:

[1277] Historical Data Recording

[1278] The server records the user information, time, and location in a database. The inputs to this process are user information, timestamp, and location information, and the output is an updated database record.

[1279] By executing each step in this way, it is possible to identify managers and workers within the factory and provide appropriate support.

[1280] 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.

[1281] The present invention relates to a system that enables office staff and receptionists to quickly and accurately identify employees and visitors and grasp their emotional states. This system provides a function to identify and display the user's emotional state by combining voice data, short-range communication means, facial recognition technology, and an emotion engine. Specific embodiments are described below.

[1282] 1. Voice recognition and emotion recognition

[1283] The device has the ability to capture voice data when a user speaks. For example, a microphone installed on the desk of a general affairs employee can be used to recognize speech. The voice data is then sent to an emotion engine to recognize the user's emotional state. After capturing the voice data, it is encoded and sent to a server. The server then uses a voice recognition engine to convert the voice data into text data, and compares the converted text data with a database to obtain matching user information.

[1284] Example: When a user says "Hello, I'm Tanaka," the voice is captured by the microphone. At the same time, the emotion engine recognizes the emotional state of "Tanaka is happy." The voice data is sent to the server, where it is converted into text data "Hello, I'm Tanaka" by the speech recognition engine. Information about "Tanaka" is retrieved from the database, and finally "Tanaka" and his emotional state of "happy" are displayed on the device.

[1285] 2. Identification and emotion recognition using short-range communication methods

[1286] The terminal uses Bluetooth or an NFC reader to detect approaching devices or employee IDs. At the same time, it recognizes the user's emotional state from voice data and facial images. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information.

[1287] Example: When a user (employee) approaches the general affairs desk with their iPhone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which matches it with the database and retrieves the information of "Sato." Finally, the device displays "Sato-san" and his / her active emotional state.

[1288] 3. Facial Recognition and Emotion Recognition

[1289] The device's camera captures the user's face, and sends the facial image data to an emotion engine to recognize the user's emotional state. The captured facial image data is then sent to a server, where the facial recognition engine matches the facial image data with user information in a database.

[1290] Example: When a user stands in front of the general affairs desk, the camera captures a facial image. At the same time, the emotion engine recognizes the emotional state of "Yamada is tired." The facial image data is sent to the server, where it is matched by the facial recognition engine to obtain information about "Yamada." Finally, "Yamada" and his emotional state of "fatigue" are displayed on the terminal.

[1291] 4. Historical data management and emotion history

[1292] The device has the ability to temporarily store user information and emotional state obtained based on voice, device ID, and facial information. When the conversation ends, information such as user information, timestamp, location, and emotional state is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the device retrieves and displays the history information and emotional state from the database.

[1293] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and the database will display historical information and emotional state such as "Tanaka-san, happy" or "Sato-san, active."

[1294] This allows general affairs personnel to efficiently identify employees and understand their emotional state. In addition, the quality of communication is improved because conversation history and emotional state can be easily managed and referenced.

[1295] The processing flow will be explained below.

[1296] Speech and emotion recognition

[1297] Step 1:

[1298] The terminal uses a microphone to capture voice data when the user speaks.

[1299] Step 2:

[1300] The terminal encodes the captured audio data and transmits it to a server via a network.

[1301] Step 3:

[1302] The server decodes the encoded voice data and passes it to a voice recognition engine.

[1303] Step 4:

[1304] The server uses a speech recognition engine to convert the voice data into text data.

[1305] Step 5:

[1306] The server extracts the name portion from the converted text data and compares it with user information in a database.

[1307] Step 6:

[1308] At the same time, the server sends the voice data to the emotion engine to analyze the user's emotional state.

[1309] Step 7:

[1310] The server returns the matching results and emotion analysis results to the terminal.

[1311] Step 8:

[1312] The device displays the received matching result (user's name) and emotional state in a pop-up.

[1313] Identification and emotion recognition using short-range communication methods

[1314] Step 1:

[1315] The device uses Bluetooth or an NFC reader to detect signals from nearby devices.

[1316] Step 2:

[1317] The terminal encodes the detected device ID and transmits it to the server via the network.

[1318] Step 3:

[1319] The server decodes the received device ID and checks it against the user information in the database.

[1320] Step 4:

[1321] The server returns the result of the check to the terminal.

[1322] Step 5:

[1323] At the same time, the device sends the captured voice data and facial images to the emotion engine to analyze the user's emotional state.

[1324] Step 6:

[1325] The server returns the matching results and emotion analysis results to the terminal.

[1326] Step 7:

[1327] The device displays the received matching result (user's name) and emotional state in a pop-up.

[1328] Facial recognition and emotion recognition

[1329] Step 1:

[1330] The terminal uses a camera to capture an image of the user's face.

[1331] Step 2:

[1332] The terminal encodes the captured facial image data and transmits it to a server via a network.

[1333] Step 3:

[1334] The server decodes the received facial image data and passes it to the facial recognition engine.

[1335] Step 4:

[1336] The server uses a facial recognition engine to match the facial image data with user information in a database.

[1337] Step 5:

[1338] The server returns the result of the check to the terminal.

[1339] Step 6:

[1340] At the same time, the device sends the captured facial image to the emotion engine to analyze the user's emotional state.

[1341] Step 7:

[1342] The server returns the matching results and emotion analysis results to the terminal.

[1343] Step 8:

[1344] The device displays the received matching result (user's name) and emotional state in a pop-up.

[1345] Historical data management and emotion history

[1346] Step 1:

[1347] The device temporarily stores user information obtained based on voice, device ID, facial information, and emotional state.

[1348] Step 2:

[1349] When the conversation ends, the terminal encodes the user information, timestamp, location, and emotional state and transmits them to the server via the network.

[1350] Step 3:

[1351] The server records the received conversation information in a database.

[1352] Step 4:

[1353] The user operates the terminal to request the history data.

[1354] Step 5:

[1355] The terminal encodes the request and sends it over the network to the server.

[1356] Step 6:

[1357] The server searches the database for the relevant history data and emotional state, and returns the search results to the terminal.

[1358] Step 7:

[1359] The terminal displays the received historical data and emotional state to the user.

[1360] This process flow allows general affairs personnel to efficiently identify employees and understand their emotional state. It also allows for easy management and reference of conversation history and emotional state, improving the quality of communication.

[1361] Example 2

[1362] 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."

[1363] In an office, it is important for general affairs staff and receptionists to quickly and accurately identify employees and visitors and understand their emotional state, but doing this manually is time-consuming and prone to human error. Furthermore, understanding a user's emotional state when interacting with them allows for more appropriate responses, but this is also difficult to do manually. Furthermore, there is a need for a function to manage past communication history and emotional state and refer to them later.

[1364] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for capturing voice data, means for transmitting the captured voice data to an emotion engine to recognize an emotional state, means for converting the captured voice data into text data, means for comparing the converted text data with user information, and means for displaying the user's name and emotional state based on the comparison result. This allows for automatic identification of the user and recognition of their emotional state, thereby improving the efficiency of the work of general affairs staff and reception staff and enabling more appropriate responses.

[1365] "Voice data" is information that digitally captures a user's speech.

[1366] "Capturing means" refers to a combination of hardware and software for capturing audio data and facial images.

[1367] The "emotion engine" is software that analyzes and recognizes the user's emotional state from acquired voice data and facial images.

[1368] The "means for converting into text data" is software that includes voice recognition technology that converts voice data into text information.

[1369] "User information" refers to identification information and attribute information about individual users that is stored in a database.

[1370] "Means for matching" refers to the process of comparing acquired voice data, text data, device ID, or facial image data with a database to identify matching user information.

[1371] "Device ID" is a unique identifier of a device obtained by short-range communication means.

[1372] A "face image" is image data that digitally represents a user's face.

[1373] The "displaying means" refers to a display device or software for visually displaying the acquired user information and emotional state.

[1374] "Short-range communication means" refers to communication technologies for exchanging data over short distances, such as Bluetooth and NFC.

[1375] A "database" is an information management system for systematically organizing and storing multiple user information.

[1376] This invention relates to a system that enables office staff and receptionists to quickly and accurately identify employees and visitors and grasp their emotional state. This system uses voice data, short-range communication means, and face recognition technology, as well as an emotion engine to identify and display the user's emotional state.

[1377] 1. Voice recognition and emotion recognition

[1378] The device uses a microphone to capture voice data when the user speaks. For example, a microphone installed on a desk captures the user's voice, such as "Hello, I'm Tanaka." This voice data is sent to an emotion engine, which analyzes the user's emotional state and recognizes, for example, "joy." The voice data is then encoded and sent to the server. The server uses a voice recognition engine to convert the voice data into text data, and compares the text data with a database to obtain matching user information.

[1379] Example: When a user says "Hello, I'm Tanaka," the microphone captures the voice. At the same time, the emotion engine recognizes that "Tanaka-san is happy." The voice data is sent to the server, where it is converted into "Hello, I'm Tanaka-san" by the speech recognition engine. Information about "Tanaka-san" is retrieved from the database, and "Tanaka-san" and his emotional state of "happy" are displayed on the device.

[1380] Example prompt: Describe the data processing flow when a user says, "Hello, I'm Tanaka."

[1381] 2. Identification and emotion recognition using short-range communication methods

[1382] The terminal uses Bluetooth or an NFC reader to detect approaching devices or employee IDs. At the same time, it recognizes the user's emotional state from voice data and facial images. The detected device ID is encoded and sent to the server, which then compares it with a database to obtain matching user information.

[1383] Example: When a user (employee) approaches the general affairs desk with their smartphone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which compares it with the database and retrieves information about "Mr. Sato." The device displays "Mr. Sato" and his active emotional state.

[1384] Example prompt: Describe the data processing flow when a user approaches the administration desk and a Bluetooth signal is detected.

[1385] 3. Facial Recognition and Emotion Recognition

[1386] The device's camera captures a user's facial image, and sends the facial image data to an emotion engine to recognize the user's emotional state. This facial image data is then sent to a server, which uses a facial recognition engine to match the facial image data with user information in a database.

[1387] Example: When a user stands in front of the general affairs desk, the camera captures a facial image. At the same time, the emotion engine recognizes the user's emotional state as "Yamada-san is tired." The facial image data is sent to the server, and the facial recognition engine obtains information about "Yamada-san." The device displays "Yamada-san" and his emotional state of "fatigue."

[1388] Example prompt: Describe the process flow for facial and emotion recognition when a user stands in front of the general affairs desk.

[1389] 4. Historical data management and emotion history

[1390] The device temporarily stores user information and emotional state obtained based on voice, device ID, and facial information. When the conversation ends, this information is sent to the server and recorded in a database. When the user wants to view their past communication history, the device retrieves the history information from the database on the server and displays it on the device.

[1391] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and the database will display historical information and emotional state such as "Tanaka-san, happy" or "Sato-san, active."

[1392] This allows general affairs personnel to efficiently identify employees and understand their emotional state. In addition, the quality of communication is improved because conversation history and emotional state can be easily managed and referenced.

[1393] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1394] 1. Voice recognition and emotion recognition

[1395] Step 1:

[1396] The device captures the user's voice data through a microphone. The input is the user's speech, and the output is the captured voice data. For example, a voice such as "Hello, I'm Tanaka" is captured by the microphone.

[1397] Step 2:

[1398] The device sends the captured voice data to the emotion engine, which analyzes the voice data and recognizes the user's emotional state. In this process, the input is the captured voice data, and the output is the emotional state obtained from the emotion engine, such as "joy."

[1399] Step 3:

[1400] The device encodes the audio data and sends it to the server. The input is the captured audio data and the recognized emotional state, and the output is the encoded audio data.

[1401] Step 4:

[1402] The server receives the transmitted voice data and converts it into text data using a speech recognition engine. The input is the encoded voice data, and the output is text data such as "Hello, I'm Tanaka."

[1403] Step 5:

[1404] The server compares the converted text data with the database to obtain matching user information. The input is text data, and the output is user information (e.g., "Mr. Tanaka") obtained from the database.

[1405] Step 6:

[1406] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[1407] Step 7:

[1408] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Mr. Tanaka" and "Joy").

[1409] 2. Identification and emotion recognition using short-range communication methods

[1410] Step 1:

[1411] The terminal detects the device ID using Bluetooth or an NFC reader. The input is the signal from the device held by the user, and the output is the detected device ID.

[1412] Step 2:

[1413] The device recognizes the emotional state from voice data and facial images. The input is voice data and facial images, and the output is the recognized emotional state.

[1414] Step 3:

[1415] The device encodes the device ID and sends it to the server. The input is the device ID and emotional state, and the output is the encoded data.

[1416] Step 4:

[1417] The server checks the received device ID against the database and retrieves the matching user information. The input is the encoded device ID, and the output is the user information retrieved from the database.

[1418] Step 5:

[1419] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[1420] Step 6:

[1421] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Mr. Sato" and "Active").

[1422] 3. Facial Recognition and Emotion Recognition

[1423] Step 1:

[1424] The device's camera captures the user's face image. The input is the user's face, and the output is the captured face image data.

[1425] Step 2:

[1426] The device sends the captured facial image to the emotion engine to recognize the emotional state. The input is the facial image data, and the output is the recognized emotional state.

[1427] Step 3:

[1428] The terminal encodes the facial image data and sends it to the server. The input is the facial image data and the emotional state, and the output is the encoded data.

[1429] Step 4:

[1430] The server passes the received facial image data to a facial recognition engine, which compares it with the database to obtain matching user information. The input is the encoded facial image data, and the output is the user information obtained from the database.

[1431] Step 5:

[1432] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[1433] Step 6:

[1434] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Yamada-san" and "fatigue").

[1435] 4. Historical data management and emotion history

[1436] Step 1:

[1437] The device temporarily stores the acquired user information and emotional state. The input is user information such as voice data, device ID, and facial information, as well as the emotional state, and the output is the temporarily stored data.

[1438] Step 2:

[1439] At the end of the conversation, the device encodes these data and sends them to the server. The input is the user information, timestamp, location, and emotional state at the end of each conversation, and the output is the encoded data.

[1440] Step 3:

[1441] The server stores the received history data in a database. The input is the encoded history data, and the output is the history information stored in the database.

[1442] Step 4:

[1443] When a user wants to view past communication history, the terminal sends a request to the server. The input is a view request, and the output is a send request.

[1444] Step 5:

[1445] The server retrieves the relevant history information and emotional state from the database and sends them to the terminal. The input is the reference request, and the output is the history information and emotional state.

[1446] Step 6:

[1447] The terminal displays the received history information and emotional state. The input is the history information and emotional state, and the output is the information displayed on the display.

[1448] This allows general affairs personnel to efficiently identify employees and understand their emotional state. The quality of communication is also improved because conversation history and emotional state can be easily managed and referenced.

[1449] (Application example 2)

[1450] 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."

[1451] In conventional brick-and-mortar stores, it is difficult to identify individual customers and grasp their emotional state, making it difficult to provide appropriate customer service to increase customer satisfaction. This can result in a decrease in customer satisfaction and a loss of repeat customers. Furthermore, it takes time and effort for customer service staff to grasp the information of all customers, making it inefficient. To solve these issues, a system is needed that can quickly and accurately identify customers who visit a store and grasp their emotional state.

[1452] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing voice data, means for converting the captured voice data into text data, means for comparing the converted text data with user information, means for displaying the user's name based on the comparison result, means for recognizing the user's emotional state using an emotion engine, and means for displaying the recognized emotional state. This makes it possible to quickly and accurately identify customers who visit a store and grasp their emotional state in real time.

[1453] "Audio data" refers to data in which audio is recorded in digital format.

[1454] "Capturing" means collecting and recording audio data, image data, etc. using a device.

[1455] "Text data" is data that represents text information such as letters and numbers in a digital format.

[1456] "Converting" is the process of changing data in one format into data in another format.

[1457] "User information" is information related to a user, such as personal identification information, name, and job title.

[1458] "Matching" is the process of comparing acquired data with existing data to see if they match.

[1459] "Display" means to visually represent characters or images on the screen of a device or the like.

[1460] "Short-range communication means" refers to short-range wireless communication technologies such as Bluetooth and NFC.

[1461] A "device ID" is an identifier uniquely assigned to each device.

[1462] A "face image" is an image of a person's face captured using a camera or the like.

[1463] An "emotion engine" is software or a system for analyzing and recognizing emotions from data such as voice and images.

[1464] "Emotional state" refers to a person's emotional state, such as joy, anger, sadness, or surprise.

[1465] A "server" is a computer system for storing and processing data.

[1466] This invention relates to a system for quickly and accurately identifying customers and grasping their emotional state in a brick-and-mortar store. The system provides the ability to recognize and display the emotional state of customers by combining voice data capture, near-field communication means, facial recognition technology, and an emotion engine.

[1467] The system configuration is as follows:

[1468] 1. Voice recognition and emotion recognition

[1469] The device has the ability to capture voice data when the user speaks. Specifically, it recognizes voice using the microphone of the smart device (smartphone or smart glasses). The voice data is converted into text data using the Google Cloud Speech-to-Text API. The converted text data is sent to a server, which compares it with a database to obtain matching user information. It also uses an emotion engine (Affectiva SDK) to recognize the user's emotional state from the voice data. The recognized emotional state is visually displayed on the device screen.

[1470] Example: When a user says "Hello, I'm Sato," the voice is captured by the microphone on the smart device. At the same time, the emotion engine recognizes the emotional state of "Mr. Sato is happy." The voice data is sent to the server, where the voice recognition engine converts it into text data of "Hello, I'm Sato." Information about "Sato" is retrieved from the database, and finally "Mr. Sato" and his emotional state of "joy" are displayed on the device.

[1471] 2. Identification and emotion recognition using short-range communication methods

[1472] The terminal uses Bluetooth and NFC readers to detect approaching customers' devices, while simultaneously recognizing their emotional state through voice data and facial images. Once the device ID is detected, it is sent to the server, where it is compared with a database to retrieve matching customer information.

[1473] Example: When a customer enters a store with a smart device, the Bluetooth signal is detected by the terminal. The device ID is sent to the server, which matches it with the database to obtain information about "Sato." Finally, the terminal displays "Sato-san" and his active emotional state.

[1474] 3. Facial Recognition and Emotion Recognition

[1475] The device's camera captures the customer's face. The facial image data is then sent to an emotion engine to recognize the user's emotional state. The captured facial image data is then sent to a server, where it is matched with user information in a database by a facial recognition engine (Microsoft Azure Face API).

[1476] Example: When a customer stands in front of a reception robot, a camera captures a facial image. At the same time, an emotion engine recognizes the customer's emotional state as "Yamada is tired." The facial image data is sent to a server, where it is collated by the facial recognition engine to obtain information about "Yamada." Finally, "Yamada" and his emotional state of "fatigue" are displayed on the terminal.

[1477] 4. Historical data management and emotion history

[1478] The device has the ability to temporarily store customer information and their emotional state obtained based on voice, device ID, and facial information. At the end of the conversation, information such as customer information, timestamp, location, and emotional state is sent to the server and recorded in a database. If the customer wants to view their past communication history, the device retrieves and displays the historical information and emotional state from the database.

[1479] Example: A customer speaks with multiple staff members during a morning greeting. At the end of each conversation, customer information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a staff member wants to refer to the history later, they can request "who they spoke to today" from their terminal, and historical information and emotional state, such as "Mr. Sato, happy" or "Mr. Yamada, tired," are displayed from the database.

[1480] These processes enable staff to efficiently identify customers and understand their emotional state. In addition, the quality of customer service is improved because conversation history and emotional state can be easily managed and referenced.

[1481] Specific prompt examples:

[1482] "Hello, can you please outline a system that uses voice, Bluetooth, and facial recognition to identify customers when they enter your store, analyze their emotions, and display their impressions? Explain specifically how the system works."

[1483] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1484] Step 1:

[1485] The device captures the user's speech with a microphone. The input is the user's speech. The device captures this as audio data and sends the audio data to the Google Cloud Speech-to-Text API.

[1486] Step 2:

[1487] The server uses the Google Cloud Speech-to-Text API to convert the transmitted voice data into text data. The input is the voice data. The output is the converted text data.

[1488] Step 3:

[1489] The server compares the converted text data with the database. The input is the text data. Based on the user information stored in the database, matching user information is obtained as a comparison result. The output is the user information.

[1490] Step 4:

[1491] The device uses the emotion engine (Affectiva SDK) to recognize the user's emotional state from voice data. The input is voice data. The emotion engine analyzes the voice features and recognizes the emotional state. The output is the recognized emotional state.

[1492] Step 5:

[1493] The terminal displays the user's name and the recognized emotional state based on the matching result. The input is user information and emotional state, which are visually displayed on the terminal screen. The output is a screen displaying the user's name and emotional state.

[1494] Step 6:

[1495] The terminal detects the device ID via near-field communication using a Bluetooth or NFC reader. The input is the Bluetooth signal or NFC tag of the device held by the user. The output is the detected device ID.

[1496] Step 7:

[1497] The server uses the detected device ID to check against the database and obtain matching user information. The input is the device ID. The database is checked and the user information is obtained. The output is the user information.

[1498] Step 8:

[1499] Use the device camera to capture a user's face image. The input is a face image. Capture face image data and send it to the Microsoft Azure Face API. The output is the captured face image data.

[1500] Step 9:

[1501] The server uses the Microsoft Azure Face API to compare face image data with a database and obtain user information. The input is face image data. The API is used to perform face recognition and compare the data with the database to obtain user information. The output is user information.

[1502] Step 10:

[1503] The terminal sends facial image data to the emotion engine to recognize the user's emotional state. The input is the facial image data. The emotion engine analyzes the facial features and recognizes the emotional state. The output is the recognized emotional state.

[1504] Step 11:

[1505] The terminal displays user information and the recognized emotional state. The input is user information and emotional state. These are visually displayed on the terminal screen. The output is a screen showing the user's name and emotional state.

[1506] Step 12:

[1507] At the end of the conversation, the device sends information such as user information, timestamp, location, and emotional state to the server. The input is user information, timestamp, location, and emotional state. This data is temporarily stored and sent to the server. The output is the history data sent to the server.

[1508] Step 13:

[1509] When a user wants to refer to their past communication history, the history information and emotional state are obtained from the database through the terminal. The input is a reference request. The server extracts the user's history data from the history database and sends it to the terminal. The output is the history information and emotional state that can be referred to.

[1510] 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.

[1511] 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.

[1512] 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.

[1513] [Fourth embodiment]

[1514] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1515] 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.

[1516] 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).

[1517] 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.

[1518] 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.

[1519] 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).

[1520] 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.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] 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.

[1525] 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.

[1526] 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."

[1527] The present invention relates to a system that enables general affairs staff and receptionists in an office to quickly and accurately identify employees and visitors. This system provides the function of identifying and displaying the user's name by combining voice data, short-range communication means, and facial recognition technology. Specific embodiments are described below.

[1528] 1. Audio Identification

[1529] The device has the ability to capture voice data when a user speaks. For example, speech is recognized using a microphone installed on the desk of a general affairs employee. After capturing the voice data, it is encoded and sent to a server. The server uses a voice recognition engine to convert the voice data into text data. This converted text data is compared with a database to obtain matching user information. Based on the comparison results, the device displays the user's name in a pop-up window.

[1530] Example: When a user says "Hello, I'm Tanaka," the voice is captured by a microphone and the voice data is sent to the server. The voice recognition engine converts this into text data "Hello, I'm Tanaka," and information about "Tanaka" is retrieved from the database. Finally, "Tanaka-san" is displayed on the device.

[1531] 2. Identification by short-range communication means

[1532] The device uses Bluetooth or an NFC reader to detect an approaching device or employee ID. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information. The device then displays a pop-up with the user's name based on the match.

[1533] Example: When a user (employee) approaches the general affairs desk with their iPhone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which checks it against the database and retrieves the information for "Sato." Finally, "Mr. Sato" is displayed on the device.

[1534] 3. Facial Recognition Identification

[1535] The device's camera captures the user's face. The captured facial image data is sent to the server. The server uses a facial recognition engine to match the facial image data with a database and obtains matching user information. The device then displays the user's name in a pop-up based on the matching results.

[1536] Example: When a user stands in front of the general affairs desk, a camera captures a facial image. The facial image data is sent to a server, where a facial recognition engine performs a match and obtains information about "Yamada." Finally, "Yamada-san" is displayed on the terminal.

[1537] 4. Historical Data Management

[1538] The terminal has a function to temporarily store information about the user who spoke to it. When the conversation ends, information such as the user's information (user ID), time, and location is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the historical information is retrieved from the database via the terminal and displayed.

[1539] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and history information such as "Tanaka-san, Sato-san" is displayed from the database.

[1540] This allows general affairs personnel to efficiently identify employees and easily manage and view interaction history.

[1541] The processing flow will be explained below.

[1542] Voice Identification

[1543] Step 1:

[1544] The terminal uses a microphone to capture voice data when the user speaks.

[1545] Step 2:

[1546] The terminal encodes the captured audio data and transmits it to a server via a network.

[1547] Step 3:

[1548] The server decodes the encoded voice data and passes it to a voice recognition engine.

[1549] Step 4:

[1550] The server uses a speech recognition engine to convert the voice data into text data.

[1551] Step 5:

[1552] The server extracts the name portion from the converted text data and compares it with user information in a database.

[1553] Step 6:

[1554] The server returns the result of the check to the terminal.

[1555] Step 7:

[1556] The terminal displays the received matching result (user's name) in a pop-up.

[1557] Identification by short-range communication means

[1558] Step 1:

[1559] The device uses Bluetooth or an NFC reader to detect signals from nearby devices.

[1560] Step 2:

[1561] The terminal encodes the detected device ID and transmits it to the server via the network.

[1562] Step 3:

[1563] The server decodes the received device ID and checks it against the user information in the database.

[1564] Step 4:

[1565] The server returns the result of the check to the terminal.

[1566] Step 5:

[1567] The terminal displays the received matching result (user's name) in a pop-up.

[1568] Facial Recognition Identification

[1569] Step 1:

[1570] The terminal uses a camera to capture an image of the user's face.

[1571] Step 2:

[1572] The terminal encodes the captured facial image data and transmits it to a server via a network.

[1573] Step 3:

[1574] The server decodes the received facial image data and passes it to the facial recognition engine.

[1575] Step 4:

[1576] The server uses a facial recognition engine to match the facial image data with user information in a database.

[1577] Step 5:

[1578] The server returns the result of the check to the terminal.

[1579] Step 6:

[1580] The terminal displays the received matching result (user's name) in a pop-up.

[1581] Historical Data Management

[1582] Step 1:

[1583] The device temporarily stores user information obtained based on voice, device ID, and facial information.

[1584] Step 2:

[1585] When the conversation ends, the terminal encodes the user information, timestamp, and location and transmits them to the server via the network.

[1586] Step 3:

[1587] The server records the received conversation information in a database.

[1588] Step 4:

[1589] The user operates the terminal to request the history data.

[1590] Step 5:

[1591] The terminal encodes the request and sends it over the network to the server.

[1592] Step 6:

[1593] The server searches the database for the relevant history data and returns the search results to the terminal.

[1594] Step 7:

[1595] The terminal displays the received history data to the user.

[1596] By following these steps, general affairs staff can efficiently identify employees and manage and view their interaction history.

[1597] Example 1

[1598] 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."

[1599] There is a need for a method that enables office staff and receptionists to quickly and accurately identify employees and visitors. A key challenge is improving accuracy by combining various methods, such as voice, short-range communication, and facial recognition. Also important is the ability to manage and reference user history information after identification.

[1600] 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.

[1601] In this invention, the server includes a means for converting voice data into text data, a means for comparing the converted text data with a database to acquire user information, a means for comparing a device ID with a database to acquire user information, a means for comparing face image data with a database to acquire user information, and a means for managing history information. This allows general affairs staff and reception staff to quickly and accurately identify employees and visitors using a variety of methods. Furthermore, by managing and referencing past communication history, work efficiency can be improved.

[1602] "Voice data" is data that represents the voice signal emitted by the user in digital form.

[1603] "Encoding" is the process of converting and compressing digital data according to certain rules, and is a technology used to efficiently transfer and store data.

[1604] A "server" is a computer system that provides services to other computers and terminals on a network, and plays a central role in data processing and information management.

[1605] A "voice recognition engine" is a software component that analyzes voice data and converts the content into text data.

[1606] "Text data" is a data format that expresses information using characters and symbols, and is character string data generated by a voice recognition engine.

[1607] A "database" is a collection of data that is constructed to efficiently search, manage, and update large amounts of data, and the data is organized based on specific rules.

[1608] "User information" is information used to identify individual users, and includes identifiers such as names and employee numbers.

[1609] "Short-range communication means" refers to technology that enables communication between devices over short distances using Bluetooth, NFC, etc.

[1610] A "device ID" is an identifier that uniquely identifies an individual device, and is specific information assigned to Bluetooth or NFC devices.

[1611] "Facial image data" is data that digitally represents an image of a user's face, and is captured by a camera.

[1612] A "face recognition engine" is a software component that analyzes facial image data and matches its contents with existing information in a database.

[1613] "History information" refers to information that includes records of past interactions and identifications with a user, including data such as user ID, time, and location.

[1614] This invention relates to a system that enables general affairs staff and receptionists in an office to quickly and accurately identify employees and visitors. This system provides the function of identifying and displaying the user's name by combining voice data, short-range communication means, and face recognition technology. The specific configuration and operation of this system are described below.

[1615] Voice Identification

[1616] The device is equipped with a function to capture voice data when a user speaks. Specifically, the user's speech is recognized using a microphone installed on the general affairs employee's desk. After capturing the voice data, it is encoded and sent to the server. The server then converts the voice data into text data using a speech recognition engine (e.g., Google Cloud Speech-to-Text API). This converted text data is then compared with a database to obtain matching user information. Finally, the device displays the user's name as a pop-up based on the comparison results.

[1617] Example: When a user says "Hello, I'm Tanaka," the voice is captured by a microphone and the voice data is sent to the server. The voice recognition engine converts this into text data "Hello, I'm Tanaka," and information about "Tanaka" is retrieved from the database. Finally, "Tanaka-san" is displayed on the device.

[1618] Example prompt sentence:

[1619] "General office system that identifies individuals using their voice and displays their name. User says 'Hello, I'm Tanaka.' System captures voice, sends to server which converts it to text and fetches 'Tanaka' from the database to be displayed on the terminal."

[1620] Identification by short-range communication means

[1621] The device uses Bluetooth or an NFC reader to detect an approaching device or employee ID. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information. The device then displays a pop-up with the user's name based on the match.

[1622] Example: When a user (employee) approaches the general affairs desk with their smartphone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which checks it against the database and retrieves the information for "Sato." Finally, "Mr. Sato" is displayed on the device.

[1623] Example prompt sentence:

[1624] "General office system that identifies individuals using NFC or Bluetooth technology and displays their name. User with smartphone approaches, system reads Bluetooth ID and fetches 'Sato' from database to be displayed on the terminal."

[1625] Facial Recognition Identification

[1626] The device's camera captures the user's face. The captured facial image data is encoded and sent to the server. The server uses a facial recognition engine (e.g., Amazon Rekognition) to match the facial image data with a database and obtain matching user information. The device then displays the user's name in a pop-up based on the matching results.

[1627] Example: When a user stands in front of the general affairs desk, a camera captures a facial image. The facial image data is sent to a server, where a facial recognition engine performs a match and obtains information about "Yamada." Finally, "Yamada-san" is displayed on the terminal.

[1628] Example prompt sentence:

[1629] "General office system that identifies individuals using facial recognition technology and displays their name. User stands in front of the desk, camera captures face, server processes and fetches 'Yamada' from the database to be displayed on the terminal."

[1630] Historical Data Management

[1631] The terminal has a function to temporarily store information about the user who spoke to it. When the conversation ends, information such as the user's information (user ID), time, and location is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the historical information is retrieved from the database via the terminal and displayed.

[1632] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and history information such as "Tanaka-san, Sato-san" is displayed from the database.

[1633] Example prompt sentence:

[1634] "General office system that manages conversation history including user ID, timestamp, and location. User greets employees, data is recorded and can later be retrieved to see 'talked to Tanaka and Sato'."

[1635] As described above, this system combines voice recognition, short-range communication, and facial recognition to efficiently and accurately identify users and support the work of general affairs personnel. Furthermore, by managing historical information, past conversation records can be easily referenced.

[1636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1637] Voice Identification

[1638] Step 1:

[1639] The device captures voice data. When a user speaks, a microphone installed on the general affairs staff member's desk captures the user's speech as voice data.

[1640] Input: User's voice

[1641] Output: Captured audio data

[1642] Step 2:

[1643] The device encodes the captured audio data and sends it to the server. Encoding makes data transfer and storage more efficient.

[1644] Input: Captured audio data

[1645] Output: Encoded audio data

[1646] Step 3:

[1647] The server converts the voice data into text data using a speech recognition engine, for example, the Google Cloud Speech-to-Text API.

[1648] Input: Encoded audio data

[1649] Output: Text data

[1650] Step 4:

[1651] The server compares the text data with a database to obtain user information, using a matching algorithm to search for names and other characters within the text data.

[1652] Input: Text data

[1653] Output: User information as a match result

[1654] Step 5:

[1655] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[1656] Input: User information as a match result

[1657] Output: On-screen popup

[1658] Identification by short-range communication means

[1659] Step 1:

[1660] The terminal detects near-field communication devices. When a user approaches the general affairs desk, a Bluetooth or NFC reader captures the device ID.

[1661] Input: User's device signal

[1662] Output: Device ID

[1663] Step 2:

[1664] The device detects the device ID, encodes it, and sends it to the server.

[1665] Input: Device ID

[1666] Output: Encoded device ID

[1667] Step 3:

[1668] The server uses the device ID to check against the database to obtain user information. The check yields matching user information.

[1669] Input: Encoded device ID

[1670] Output: User information as a match result

[1671] Step 4:

[1672] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[1673] Input: User information as a match result

[1674] Output: On-screen popup

[1675] Facial Recognition Identification

[1676] Step 1:

[1677] The device's camera captures the user's face, and a facial image is acquired when the user stands in front of the general affairs desk.

[1678] Input: User's face

[1679] Output: Face image data

[1680] Step 2:

[1681] The terminal encodes the captured facial image data and transmits it to the server.

[1682] Input: Face image data

[1683] Output: Encoded face image data

[1684] Step 3:

[1685] The server uses facial image data to match it with a database and obtain user information. A facial recognition engine such as Amazon Rekognition is used.

[1686] Input: Encoded face image data

[1687] Output: User information as a match result

[1688] Step 4:

[1689] The terminal displays the user's name in a pop-up based on the user information acquired from the server.

[1690] Input: User information as a match result

[1691] Output: On-screen popup

[1692] Historical Data Management

[1693] Step 1:

[1694] The device temporarily stores user information when a conversation ends, and also records the timing and location of the conversation.

[1695] Input: User information, timestamp, and location after conversation ends

[1696] Output: Temporarily held information

[1697] Step 2:

[1698] The terminal transmits the user information to the server.

[1699] Input: Temporarily held information

[1700] Output: User information sent

[1701] Step 3:

[1702] The server records the received user information in a database, including the user ID, time, and location.

[1703] Input: Submitted user information

[1704] Output: Information recorded in the database

[1705] Step 4:

[1706] The terminal acquires and displays history information. This is used when the user wants to refer to the history of past interactions.

[1707] Input: Request History Information

[1708] Output: Display history information

[1709] (Application example 1)

[1710] 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."

[1711] To efficiently manage and support work within a factory, a system that can quickly and accurately identify managers and workers is necessary. However, existing identification systems often rely on a limited number of identification methods, and have the problem of being unable to handle a variety of situations. In addition, there is a lack of systems in place to properly execute managers' instructions and support workers.

[1712] 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.

[1713] In this invention, the server includes means for capturing voice data, means for converting the captured voice data into text data, means for comparing the converted text data with user information, means for displaying the user's name based on the comparison result, means for detecting a device ID using short-range communication means, means for comparing the detected device ID with user information, means for capturing a facial image, means for comparing the captured facial image with user information, and means for identifying a factory manager or worker based on the comparison result and providing appropriate work or support. This enables quick and accurate identification of managers and workers, enabling efficient management and work support within the factory.

[1714] "Voice data" refers to an acoustic signal that records the user's speech.

[1715] "Text data" is digital information that is obtained by converting voice data into a string of characters.

[1716] "User information" is a database record containing a user's identity.

[1717] "Verification" is the process of comparing and verifying acquired data with existing databases.

[1718] "Near-field communication means" refers to a means of exchanging data between devices using short-range wireless communication technologies such as Bluetooth and NFC.

[1719] A "device ID" is a unique identifier for identifying a particular device.

[1720] A "face image" is image data of a person's face photographed using a camera.

[1721] "Historical data" is data that records a user's activities and interactions.

[1722] A "factory manager" is a person in charge of operations and work instructions in a factory.

[1723] "Workers" are employees who perform physical tasks or operate equipment within a factory.

[1724] "Support" means providing support to workers to carry out their work efficiently.

[1725] "Management" means monitoring and adjusting the progress of operations and work within the factory.

[1726] The "matching result" is the user's identification information obtained as a result of database matching.

[1727] This invention is a system for identifying managers and workers in a factory and providing appropriate work support and management. This system integrates voice recognition, short-range communication, and face recognition functions, and can collate and display user information.

[1728] Voice recognition identification

[1729] The server is equipped with a microphone to capture voice data, and when a user speaks, the voice data is acquired. The acquired voice data is converted into text data using the speech_recognition library. The server then uses the converted text data to match the user information with the database and obtains matching information. The user's name as a matched result is displayed as a pop-up on the terminal.

[1730] Example: When a factory manager says, "Hello, I'm the manager," the voice is captured by a microphone, converted into text by a speech recognition engine on the server, and then matched with a database to identify the person as "manager."

[1731] Identification by short-range communication

[1732] The server uses Bluetooth or NFC readers to detect the ID of approaching devices and employees. The detected device ID is encoded and sent to the server, which then checks it against its database to obtain matching user information. The results are displayed in a pop-up on the terminal.

[1733] Example: When a worker approaches a factory with a smartphone in their pocket, their Bluetooth signal is detected and the server checks the database to identify them as a "worker."

[1734] Facial Recognition Identification

[1735] The server uses a camera to capture a facial image and sends the facial image data to the server. The server uses a facial recognition engine to match the facial image data with a database and obtains matching user information. The results are displayed on the terminal.

[1736] Example: When a manager stops at the entrance to a factory, a camera captures his / her facial image, which is then matched against a database by a facial recognition engine and identified as a "manager."

[1737] Historical Data Management

[1738] The server has a function to temporarily store information about the user who spoke to it, and when the conversation ends, records the user information, time, location, and other information in a database, allowing users to refer to their past communication history.

[1739] For example, at the end of the day, when a manager requests from a terminal "Who did you talk to today?", the server retrieves historical information such as "Worker 1, Engineer A" from the database and displays it on the terminal.

[1740] Specific examples

[1741] For example, if a factory manager approaches a robot, the robot will detect the manager's Bluetooth ID and greet him with, "Hello, manager!" If the manager instructs the robot to "check the work efficiency," the robot will analyze the work efficiency of the entire factory in real time and report it.

[1742] An example of a prompt sentence to be input to the generative AI model to realize this system is as follows:

[1743] "Please explain an example of a system implementation that uses voice recognition, near-field communication, and facial recognition technology to enable robots working in a factory to identify factory managers and workers and take appropriate action."

[1744] This makes it possible to efficiently identify managers and workers and effectively manage and support operations within the factory.

[1745] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1746] Step 1:

[1747] Capture audio data

[1748] If the user is a manager or worker, voice data is captured by speaking into a microphone, which is a raw acoustic signal.

[1749] Step 2:

[1750] Converting audio data to text

[1751] The server converts the captured audio data into text data using the speech_recognition library. The input is audio data, and the output is the corresponding text data. The audio waveform data is analyzed and string data is generated based on a language model.

[1752] Step 3:

[1753] Matching text data with user information

[1754] The server checks the converted text data against the user information in the database. The input to this process is the text data, and the output is the matching user information. The server compares the text data with the user information database.

[1755] Step 4:

[1756] Viewing User Information

[1757] The server pops up the user's name on the terminal based on the match result. The input of this process is the match result, and the output is the user's name displayed on the terminal.

[1758] Step 5:

[1759] Device ID detection through short-range communication

[1760] The terminal uses Bluetooth or an NFC reader to detect the user's device ID. The input of this process is the surrounding device signal, and the output is the detected device ID.

[1761] Step 6:

[1762] Matching device ID with user information

[1763] The server checks the detected device ID against the user information in the database. The input to this process is the device ID, and the output is the matching user information. The server compares the device ID with the user information database.

[1764] Step 7:

[1765] Facial image capture

[1766] The terminal uses a camera to capture the user's face image, and the input of this process is the camera video signal and the output is the captured face image data.

[1767] Step 8:

[1768] Matching face images with user information

[1769] The server matches the captured facial image data with user information in a database. The input to this process is facial image data, and the output is the matching user information. The server uses a facial recognition engine to compare the facial image with the facial data in the database.

[1770] Step 9:

[1771] Integrated display of user information

[1772] The server identifies the user information by combining the results of matching the voice, device ID, and facial image, and displays it on the device. The input to this process is the three matching results, and the output is the final user information.

[1773] Step 10:

[1774] Historical Data Recording

[1775] The server records the user information, time, and location in a database. The inputs to this process are user information, timestamp, and location information, and the output is an updated database record.

[1776] By executing each step in this way, it is possible to identify managers and workers within the factory and provide appropriate support.

[1777] 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.

[1778] The present invention relates to a system that enables office staff and receptionists to quickly and accurately identify employees and visitors and grasp their emotional states. This system provides a function to identify and display the user's emotional state by combining voice data, short-range communication means, facial recognition technology, and an emotion engine. Specific embodiments are described below.

[1779] 1. Voice recognition and emotion recognition

[1780] The device has the ability to capture voice data when a user speaks. For example, a microphone installed on the desk of a general affairs employee can be used to recognize speech. The voice data is then sent to an emotion engine to recognize the user's emotional state. After capturing the voice data, it is encoded and sent to a server. The server then uses a voice recognition engine to convert the voice data into text data, and compares the converted text data with a database to obtain matching user information.

[1781] Example: When a user says "Hello, I'm Tanaka," the voice is captured by the microphone. At the same time, the emotion engine recognizes the emotional state of "Tanaka is happy." The voice data is sent to the server, where it is converted into text data "Hello, I'm Tanaka" by the speech recognition engine. Information about "Tanaka" is retrieved from the database, and finally "Tanaka" and his emotional state of "happy" are displayed on the device.

[1782] 2. Identification and emotion recognition using short-range communication methods

[1783] The terminal uses Bluetooth or an NFC reader to detect approaching devices or employee IDs. At the same time, it recognizes the user's emotional state from voice data and facial images. The device ID is detected, encoded, and sent to the server. The server compares it with a database and retrieves matching user information.

[1784] Example: When a user (employee) approaches the general affairs desk with their iPhone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which matches it with the database and retrieves the information of "Sato." Finally, the device displays "Sato-san" and his / her active emotional state.

[1785] 3. Facial Recognition and Emotion Recognition

[1786] The device's camera captures the user's face, and sends the facial image data to an emotion engine to recognize the user's emotional state. The captured facial image data is then sent to a server, where the facial recognition engine matches the facial image data with user information in a database.

[1787] Example: When a user stands in front of the general affairs desk, the camera captures a facial image. At the same time, the emotion engine recognizes the emotional state of "Yamada is tired." The facial image data is sent to the server, where it is matched by the facial recognition engine to obtain information about "Yamada." Finally, "Yamada" and his emotional state of "fatigue" are displayed on the terminal.

[1788] 4. Historical data management and emotion history

[1789] The device has the ability to temporarily store user information and emotional state obtained based on voice, device ID, and facial information. When the conversation ends, information such as user information, timestamp, location, and emotional state is sent to the server. The server records this information in a database. When the user wants to refer to their past communication history, the device retrieves and displays the history information and emotional state from the database.

[1790] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and the database will display historical information and emotional state such as "Tanaka-san, happy" or "Sato-san, active."

[1791] This allows general affairs personnel to efficiently identify employees and understand their emotional state. In addition, the quality of communication is improved because conversation history and emotional state can be easily managed and referenced.

[1792] The processing flow will be explained below.

[1793] Speech and emotion recognition

[1794] Step 1:

[1795] The terminal uses a microphone to capture voice data when the user speaks.

[1796] Step 2:

[1797] The terminal encodes the captured audio data and transmits it to a server via a network.

[1798] Step 3:

[1799] The server decodes the encoded voice data and passes it to a voice recognition engine.

[1800] Step 4:

[1801] The server uses a speech recognition engine to convert the voice data into text data.

[1802] Step 5:

[1803] The server extracts the name portion from the converted text data and compares it with user information in a database.

[1804] Step 6:

[1805] At the same time, the server sends the voice data to the emotion engine to analyze the user's emotional state.

[1806] Step 7:

[1807] The server returns the matching results and emotion analysis results to the terminal.

[1808] Step 8:

[1809] The device displays the received matching result (user's name) and emotional state in a pop-up.

[1810] Identification and emotion recognition using short-range communication methods

[1811] Step 1:

[1812] The device uses Bluetooth or an NFC reader to detect signals from nearby devices.

[1813] Step 2:

[1814] The terminal encodes the detected device ID and transmits it to the server via the network.

[1815] Step 3:

[1816] The server decodes the received device ID and checks it against the user information in the database.

[1817] Step 4:

[1818] The server returns the result of the check to the terminal.

[1819] Step 5:

[1820] At the same time, the device sends the captured voice data and facial images to the emotion engine to analyze the user's emotional state.

[1821] Step 6:

[1822] The server returns the matching results and emotion analysis results to the terminal.

[1823] Step 7:

[1824] The device displays the received matching result (user's name) and emotional state in a pop-up.

[1825] Facial recognition and emotion recognition

[1826] Step 1:

[1827] The terminal uses a camera to capture an image of the user's face.

[1828] Step 2:

[1829] The terminal encodes the captured facial image data and transmits it to a server via a network.

[1830] Step 3:

[1831] The server decodes the received facial image data and passes it to the facial recognition engine.

[1832] Step 4:

[1833] The server uses a facial recognition engine to match the facial image data with user information in a database.

[1834] Step 5:

[1835] The server returns the result of the check to the terminal.

[1836] Step 6:

[1837] At the same time, the device sends the captured facial image to the emotion engine to analyze the user's emotional state.

[1838] Step 7:

[1839] The server returns the matching results and emotion analysis results to the terminal.

[1840] Step 8:

[1841] The device displays the received matching result (user's name) and emotional state in a pop-up.

[1842] Historical data management and emotion history

[1843] Step 1:

[1844] The device temporarily stores user information obtained based on voice, device ID, facial information, and emotional state.

[1845] Step 2:

[1846] When the conversation ends, the terminal encodes the user information, timestamp, location, and emotional state and transmits them to the server via the network.

[1847] Step 3:

[1848] The server records the received conversation information in a database.

[1849] Step 4:

[1850] The user operates the terminal to request the history data.

[1851] Step 5:

[1852] The terminal encodes the request and sends it over the network to the server.

[1853] Step 6:

[1854] The server searches the database for the relevant history data and emotional state, and returns the search results to the terminal.

[1855] Step 7:

[1856] The terminal displays the received historical data and emotional state to the user.

[1857] This process flow allows general affairs personnel to efficiently identify employees and understand their emotional state. It also allows for easy management and reference of conversation history and emotional state, improving the quality of communication.

[1858] Example 2

[1859] 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."

[1860] In an office, it is important for general affairs staff and receptionists to quickly and accurately identify employees and visitors and understand their emotional state, but doing this manually is time-consuming and prone to human error. Furthermore, understanding a user's emotional state when interacting with them allows for more appropriate responses, but this is also difficult to do manually. Furthermore, there is a need for a function to manage past communication history and emotional state and refer to them later.

[1861] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for capturing voice data, means for transmitting the captured voice data to an emotion engine to recognize an emotional state, means for converting the captured voice data into text data, means for comparing the converted text data with user information, and means for displaying the user's name and emotional state based on the comparison result. This allows for automatic identification of the user and recognition of their emotional state, thereby improving the efficiency of the work of general affairs staff and reception staff and enabling more appropriate responses.

[1862] "Voice data" is information that digitally captures a user's speech.

[1863] "Capturing means" refers to a combination of hardware and software for capturing audio data and facial images.

[1864] The "emotion engine" is software that analyzes and recognizes the user's emotional state from acquired voice data and facial images.

[1865] The "means for converting into text data" is software that includes voice recognition technology that converts voice data into text information.

[1866] "User information" refers to identification information and attribute information about individual users that is stored in a database.

[1867] "Means for matching" refers to the process of comparing acquired voice data, text data, device ID, or facial image data with a database to identify matching user information.

[1868] "Device ID" is a unique identifier of a device obtained by short-range communication means.

[1869] A "face image" is image data that digitally represents a user's face.

[1870] The "displaying means" refers to a display device or software for visually displaying the acquired user information and emotional state.

[1871] "Short-range communication means" refers to communication technologies for exchanging data over short distances, such as Bluetooth and NFC.

[1872] A "database" is an information management system for systematically organizing and storing multiple user information.

[1873] This invention relates to a system that enables office staff and receptionists to quickly and accurately identify employees and visitors and grasp their emotional state. This system uses voice data, short-range communication means, and face recognition technology, as well as an emotion engine to identify and display the user's emotional state.

[1874] 1. Voice recognition and emotion recognition

[1875] The device uses a microphone to capture voice data when the user speaks. For example, a microphone installed on a desk captures the user's voice, such as "Hello, I'm Tanaka." This voice data is sent to an emotion engine, which analyzes the user's emotional state and recognizes, for example, "joy." The voice data is then encoded and sent to the server. The server uses a voice recognition engine to convert the voice data into text data, and compares the text data with a database to obtain matching user information.

[1876] Example: When a user says "Hello, I'm Tanaka," the microphone captures the voice. At the same time, the emotion engine recognizes that "Tanaka-san is happy." The voice data is sent to the server, where it is converted into "Hello, I'm Tanaka-san" by the speech recognition engine. Information about "Tanaka-san" is retrieved from the database, and "Tanaka-san" and his emotional state of "happy" are displayed on the device.

[1877] Example prompt: Describe the data processing flow when a user says, "Hello, I'm Tanaka."

[1878] 2. Identification and emotion recognition using short-range communication methods

[1879] The terminal uses Bluetooth or an NFC reader to detect approaching devices or employee IDs. At the same time, it recognizes the user's emotional state from voice data and facial images. The detected device ID is encoded and sent to the server, which then compares it with a database to obtain matching user information.

[1880] Example: When a user (employee) approaches the general affairs desk with their smartphone in their pocket, the Bluetooth signal is detected. The Bluetooth ID is sent to the server, which compares it with the database and retrieves information about "Mr. Sato." The device displays "Mr. Sato" and his active emotional state.

[1881] Example prompt: Describe the data processing flow when a user approaches the administration desk and a Bluetooth signal is detected.

[1882] 3. Facial Recognition and Emotion Recognition

[1883] The device's camera captures a user's facial image, and sends the facial image data to an emotion engine to recognize the user's emotional state. This facial image data is then sent to a server, which uses a facial recognition engine to match the facial image data with user information in a database.

[1884] Example: When a user stands in front of the general affairs desk, the camera captures a facial image. At the same time, the emotion engine recognizes the user's emotional state as "Yamada-san is tired." The facial image data is sent to the server, and the facial recognition engine obtains information about "Yamada-san." The device displays "Yamada-san" and his emotional state of "fatigue."

[1885] Example prompt: Describe the process flow for facial and emotion recognition when a user stands in front of the general affairs desk.

[1886] 4. Historical data management and emotion history

[1887] The device temporarily stores user information and emotional state obtained based on voice, device ID, and facial information. When the conversation ends, this information is sent to the server and recorded in a database. When the user wants to view their past communication history, the device retrieves the history information from the database on the server and displays it on the device.

[1888] Example: A user exchanges morning greetings with multiple employees. At the end of each conversation, user information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a general affairs employee wants to refer to the history later, they can request "who they spoke to today" from their terminal, and the database will display historical information and emotional state such as "Tanaka-san, happy" or "Sato-san, active."

[1889] This allows general affairs personnel to efficiently identify employees and understand their emotional state. In addition, the quality of communication is improved because conversation history and emotional state can be easily managed and referenced.

[1890] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1891] 1. Voice recognition and emotion recognition

[1892] Step 1:

[1893] The device captures the user's voice data through a microphone. The input is the user's speech, and the output is the captured voice data. For example, a voice such as "Hello, I'm Tanaka" is captured by the microphone.

[1894] Step 2:

[1895] The device sends the captured voice data to the emotion engine, which analyzes the voice data and recognizes the user's emotional state. In this process, the input is the captured voice data, and the output is the emotional state obtained from the emotion engine, such as "joy."

[1896] Step 3:

[1897] The device encodes the audio data and sends it to the server. The input is the captured audio data and the recognized emotional state, and the output is the encoded audio data.

[1898] Step 4:

[1899] The server receives the transmitted voice data and converts it into text data using a speech recognition engine. The input is the encoded voice data, and the output is text data such as "Hello, I'm Tanaka."

[1900] Step 5:

[1901] The server compares the converted text data with the database to obtain matching user information. The input is text data, and the output is user information (e.g., "Mr. Tanaka") obtained from the database.

[1902] Step 6:

[1903] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[1904] Step 7:

[1905] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Mr. Tanaka" and "Joy").

[1906] 2. Identification and emotion recognition using short-range communication methods

[1907] Step 1:

[1908] The terminal detects the device ID using Bluetooth or an NFC reader. The input is the signal from the device held by the user, and the output is the detected device ID.

[1909] Step 2:

[1910] The device recognizes the emotional state from voice data and facial images. The input is voice data and facial images, and the output is the recognized emotional state.

[1911] Step 3:

[1912] The device encodes the device ID and sends it to the server. The input is the device ID and emotional state, and the output is the encoded data.

[1913] Step 4:

[1914] The server checks the received device ID against the database and retrieves the matching user information. The input is the encoded device ID, and the output is the user information retrieved from the database.

[1915] Step 5:

[1916] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[1917] Step 6:

[1918] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Mr. Sato" and "Active").

[1919] 3. Facial Recognition and Emotion Recognition

[1920] Step 1:

[1921] The device's camera captures the user's face image. The input is the user's face, and the output is the captured face image data.

[1922] Step 2:

[1923] The device sends the captured facial image to the emotion engine to recognize the emotional state. The input is the facial image data, and the output is the recognized emotional state.

[1924] Step 3:

[1925] The terminal encodes the facial image data and sends it to the server. The input is the facial image data and the emotional state, and the output is the encoded data.

[1926] Step 4:

[1927] The server passes the received facial image data to a facial recognition engine, which compares it with the database to obtain matching user information. The input is the encoded facial image data, and the output is the user information obtained from the database.

[1928] Step 5:

[1929] The server sends user information and emotional states to the terminal. The inputs are the user information and emotional states, and the outputs are those information.

[1930] Step 6:

[1931] The terminal displays the received user information and emotional state. The input is the user information and emotional state, and the output is the information displayed on the screen (e.g., "Yamada-san" and "fatigue").

[1932] 4. Historical data management and emotion history

[1933] Step 1:

[1934] The device temporarily stores the acquired user information and emotional state. The input is user information such as voice data, device ID, and facial information, as well as the emotional state, and the output is the temporarily stored data.

[1935] Step 2:

[1936] At the end of the conversation, the device encodes these data and sends them to the server. The input is the user information, timestamp, location, and emotional state at the end of each conversation, and the output is the encoded data.

[1937] Step 3:

[1938] The server stores the received history data in a database. The input is the encoded history data, and the output is the history information stored in the database.

[1939] Step 4:

[1940] When a user wants to view past communication history, the terminal sends a request to the server. The input is a view request, and the output is a send request.

[1941] Step 5:

[1942] The server retrieves the relevant history information and emotional state from the database and sends them to the terminal. The input is the reference request, and the output is the history information and emotional state.

[1943] Step 6:

[1944] The terminal displays the received history information and emotional state. The input is the history information and emotional state, and the output is the information displayed on the display.

[1945] This allows general affairs personnel to efficiently identify employees and understand their emotional state. The quality of communication is also improved because conversation history and emotional state can be easily managed and referenced.

[1946] (Application example 2)

[1947] 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."

[1948] In conventional brick-and-mortar stores, it is difficult to identify individual customers and grasp their emotional state, making it difficult to provide appropriate customer service to increase customer satisfaction. This can result in a decrease in customer satisfaction and a loss of repeat customers. Furthermore, it takes time and effort for customer service staff to grasp the information of all customers, making it inefficient. To solve these issues, a system is needed that can quickly and accurately identify customers who visit a store and grasp their emotional state.

[1949] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing voice data, means for converting the captured voice data into text data, means for comparing the converted text data with user information, means for displaying the user's name based on the comparison result, means for recognizing the user's emotional state using an emotion engine, and means for displaying the recognized emotional state. This makes it possible to quickly and accurately identify customers who visit a store and grasp their emotional state in real time.

[1950] "Audio data" refers to data in which audio is recorded in digital format.

[1951] "Capturing" means collecting and recording audio data, image data, etc. using a device.

[1952] "Text data" is data that represents text information such as letters and numbers in a digital format.

[1953] "Converting" is the process of changing data in one format into data in another format.

[1954] "User information" is information related to a user, such as personal identification information, name, and job title.

[1955] "Matching" is the process of comparing acquired data with existing data to see if they match.

[1956] "Display" means to visually represent characters or images on the screen of a device or the like.

[1957] "Short-range communication means" refers to short-range wireless communication technologies such as Bluetooth and NFC.

[1958] A "device ID" is an identifier uniquely assigned to each device.

[1959] A "face image" is an image of a person's face captured using a camera or the like.

[1960] An "emotion engine" is software or a system for analyzing and recognizing emotions from data such as voice and images.

[1961] "Emotional state" refers to a person's emotional state, such as joy, anger, sadness, or surprise.

[1962] A "server" is a computer system for storing and processing data.

[1963] This invention relates to a system for quickly and accurately identifying customers and grasping their emotional state in a brick-and-mortar store. The system provides the ability to recognize and display the emotional state of customers by combining voice data capture, near-field communication means, facial recognition technology, and an emotion engine.

[1964] The system configuration is as follows:

[1965] 1. Voice recognition and emotion recognition

[1966] The device has the ability to capture voice data when the user speaks. Specifically, it recognizes voice using the microphone of the smart device (smartphone or smart glasses). The voice data is converted into text data using the Google Cloud Speech-to-Text API. The converted text data is sent to a server, which compares it with a database to obtain matching user information. It also uses an emotion engine (Affectiva SDK) to recognize the user's emotional state from the voice data. The recognized emotional state is visually displayed on the device screen.

[1967] Example: When a user says "Hello, I'm Sato," the voice is captured by the microphone on the smart device. At the same time, the emotion engine recognizes the emotional state of "Mr. Sato is happy." The voice data is sent to the server, where the voice recognition engine converts it into text data of "Hello, I'm Sato." Information about "Sato" is retrieved from the database, and finally "Mr. Sato" and his emotional state of "joy" are displayed on the device.

[1968] 2. Identification and emotion recognition using short-range communication methods

[1969] The terminal uses Bluetooth and NFC readers to detect approaching customers' devices, while simultaneously recognizing their emotional state through voice data and facial images. Once the device ID is detected, it is sent to the server, where it is compared with a database to retrieve matching customer information.

[1970] Example: When a customer enters a store with a smart device, the Bluetooth signal is detected by the terminal. The device ID is sent to the server, which matches it with the database to obtain information about "Sato." Finally, the terminal displays "Sato-san" and his active emotional state.

[1971] 3. Facial Recognition and Emotion Recognition

[1972] The device's camera captures the customer's face. The facial image data is then sent to an emotion engine to recognize the user's emotional state. The captured facial image data is then sent to a server, where it is matched with user information in a database by a facial recognition engine (Microsoft Azure Face API).

[1973] Example: When a customer stands in front of a reception robot, a camera captures a facial image. At the same time, an emotion engine recognizes the customer's emotional state as "Yamada is tired." The facial image data is sent to a server, where it is collated by the facial recognition engine to obtain information about "Yamada." Finally, "Yamada" and his emotional state of "fatigue" are displayed on the terminal.

[1974] 4. Historical data management and emotion history

[1975] The device has the ability to temporarily store customer information and their emotional state obtained based on voice, device ID, and facial information. At the end of the conversation, information such as customer information, timestamp, location, and emotional state is sent to the server and recorded in a database. If the customer wants to view their past communication history, the device retrieves and displays the historical information and emotional state from the database.

[1976] Example: A customer speaks with multiple staff members during a morning greeting. At the end of each conversation, customer information, timestamp, location, emotional state, etc. are sent to the server and recorded. When a staff member wants to refer to the history later, they can request "who they spoke to today" from their terminal, and historical information and emotional state, such as "Mr. Sato, happy" or "Mr. Yamada, tired," are displayed from the database.

[1977] These processes enable staff to efficiently identify customers and understand their emotional state. In addition, the quality of customer service is improved because conversation history and emotional state can be easily managed and referenced.

[1978] Specific prompt examples:

[1979] "Hello, can you please outline a system that uses voice, Bluetooth, and facial recognition to identify customers when they enter your store, analyze their emotions, and display their impressions? Explain specifically how the system works."

[1980] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1981] Step 1:

[1982] The device captures the user's speech with a microphone. The input is the user's speech. The device captures this as audio data and sends the audio data to the Google Cloud Speech-to-Text API.

[1983] Step 2:

[1984] The server uses the Google Cloud Speech-to-Text API to convert the transmitted voice data into text data. The input is the voice data. The output is the converted text data.

[1985] Step 3:

[1986] The server compares the converted text data with the database. The input is the text data. Based on the user information stored in the database, matching user information is obtained as a comparison result. The output is the user information.

[1987] Step 4:

[1988] The device uses the emotion engine (Affectiva SDK) to recognize the user's emotional state from voice data. The input is voice data. The emotion engine analyzes the voice features and recognizes the emotional state. The output is the recognized emotional state.

[1989] Step 5:

[1990] The terminal displays the user's name and the recognized emotional state based on the matching result. The input is user information and emotional state, which are visually displayed on the terminal screen. The output is a screen displaying the user's name and emotional state.

[1991] Step 6:

[1992] The terminal detects the device ID via near-field communication using a Bluetooth or NFC reader. The input is the Bluetooth signal or NFC tag of the device held by the user. The output is the detected device ID.

[1993] Step 7:

[1994] The server uses the detected device ID to check against the database and obtain matching user information. The input is the device ID. The database is checked and the user information is obtained. The output is the user information.

[1995] Step 8:

[1996] Use the device camera to capture a user's face image. The input is a face image. Capture face image data and send it to the Microsoft Azure Face API. The output is the captured face image data.

[1997] Step 9:

[1998] The server uses the Microsoft Azure Face API to compare face image data with a database and obtain user information. The input is face image data. The API is used to perform face recognition and compare the data with the database to obtain user information. The output is user information.

[1999] Step 10:

[2000] The terminal sends facial image data to the emotion engine to recognize the user's emotional state. The input is the facial image data. The emotion engine analyzes the facial features and recognizes the emotional state. The output is the recognized emotional state.

[2001] Step 11:

[2002] The terminal displays user information and the recognized emotional state. The input is user information and emotional state. These are visually displayed on the terminal screen. The output is a screen showing the user's name and emotional state.

[2003] Step 12:

[2004] At the end of the conversation, the device sends information such as user information, timestamp, location, and emotional state to the server. The input is user information, timestamp, location, and emotional state. This data is temporarily stored and sent to the server. The output is the history data sent to the server.

[2005] Step 13:

[2006] When a user wants to refer to their past communication history, the history information and emotional state are obtained from the database through the terminal. The input is a reference request. The server extracts the user's history data from the history database and sends it to the terminal. The output is the history information and emotional state that can be referred to.

[2007] 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.

[2008] 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.

[2009] 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.

[2010] 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.

[2011] 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.

[2012] 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.

[2013] 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).

[2014] 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.

[2015] 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."

[2016] 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.

[2017] 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).

[2018] 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.

[2019] 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.

[2020] 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.

[2021] 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.

[2022] The hardware re...

Claims

1. means for capturing audio data; means for converting the captured audio data into text data; means for comparing the converted text data with user information; means for displaying the user's name based on the match; A system including:

2. a means for detecting a device ID using a near field communication means; A means for matching the detected device ID with user information; means for displaying the user's name based on the match; The system of claim 1 , comprising:

3. means for capturing a facial image; means for matching the captured facial image with user information; means for displaying the user's name based on the match; The system of claim 1 , comprising:

4. means for temporarily storing user information based on voice data, device ID, or facial image; means for recording user information as a conversation history; a means for viewing conversation history; The system of claim 1 , comprising:

5. When a user speaks, the device acquires user information based on a plurality of identification methods and selects the most reliable result. The system of claim 1 .

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