system

Abstract

We provide the system. [Solution] Means for detecting incoming calls, A means to switch to answering machine mode if there is no response within a certain period of time, A means of recording the caller's voice message, A means of converting recorded audio data into text, A means of notifying the user's device of the converted text data, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional answering machine systems and call waiting functions have the problem that they need to play back voice messages to notify users, and the confirmation of messages is delayed until the call ends. Also, it is not efficient when users manage multiple messages. Furthermore, since there is a lack of a mechanism for efficiently managing voice messages by converting them into text, there is also a problem that it is difficult to utilize them for business purposes. The purpose of this invention is to solve these problems and provide a next-generation answering machine system and call waiting function that quickly and efficiently display voice messages in text.

Means for Solving the Problems

[0005] This invention proposes to solve the above problems by the following means. The system first includes means for detecting incoming calls and means for automatically switching to voicemail mode if there is no response within a certain period of time. Next, it includes means for recording the caller's voice message and means for converting the recorded voice data into text. It also includes means for notifying the user's terminal of the converted text data. Furthermore, this system includes means for detecting a new incoming call while a call is in progress and means for switching to call waiting mode if the user is unable to answer. It includes means for recording the new caller's voice message and converting the voice data into text. It also includes means for using a voice recognition API for converting voice data into text and sending a text message to the user as a push notification or via email. In this way, a system is provided that allows the user to efficiently manage voicemail and call waiting messages.

[0006] "Incoming call" refers to notifications of incoming phone calls or messages on a communication device.

[0007] "On a call" refers to a state where a user is already in a voice call with another party.

[0008] "Answering machine mode" refers to a message recording function that automatically switches on when the user does not answer an incoming call.

[0009] "Call waiting" refers to a function that notifies you of an incoming call when you receive a new call while you are already on a call.

[0010] "Recording" refers to the process of saving a voice message as digital data.

[0011] "Audio data" refers to digital data of recorded audio.

[0012] "Converting to text" refers to the process of converting audio data into written text.

[0013] A "speech recognition API" refers to an application programming interface for automatically converting speech data into text data.

[0014] "User's device" refers to communication devices such as smartphones, tablets, and computers owned by the user.

[0015] "Push notification" refers to a notification format in which information is automatically sent from the server to the user's device.

[0016] "Email notification" refers to a notification format in which information is sent to users via email. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0020] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0021] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

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

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention provides a next-generation answering machine system and call waiting function, enabling users to efficiently manage messages received on their answering machine or during calls. The system operates through the coordinated efforts of a server, terminal, and user.

[0039] System Configuration

[0040] 1. Server

[0041] It detects incoming calls and switches to voicemail mode and call waiting mode.

[0042] The system records the caller's voice message and utilizes a speech recognition API to convert the audio data into text.

[0043] The converted text data is sent to the user's device.

[0044] 2. Terminal

[0045] Displays notifications for incoming calls and call waiting.

[0046] Receive text messages sent from the server and display them to the user.

[0047] 3. User

[0048] Check the incoming call and call waiting notifications on your device's screen.

[0049] Receive and verify the text message sent from the server.

[0050] Program processing

[0051] 1. Incoming call detection

[0052] Terminal: When an incoming call is received, a notification is displayed on the screen. If the user does not answer, the terminal sends a no-response signal to the server.

[0053] Server: If no response is received within a certain period of time, it will automatically switch to voicemail mode.

[0054] 2. Switch to voicemail mode

[0055] Server: Notifies the caller via an automated voice message that the call has switched to voicemail mode.

[0056] Server: Records the caller's voice message.

[0057] 3. Recording and converting voice messages

[0058] Server: Stores recorded voice messages as digital data.

[0059] Server: Uses a speech recognition API to convert speech data into text.

[0060] 4. Text message delivery

[0061] Server: Sends the converted text data to the user's device via push notification or email.

[0062] Device: Receives notifications and displays text messages on the screen.

[0063] Specific example

[0064] Examples of how to handle voicemails:

[0065] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text: "Tomorrow's meeting has been changed to 2 PM." The server then sends a push notification to User A's device, and the message is displayed on User A's device.

[0066] Examples of how to handle call waiting:

[0067] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records the audio and uses a speech recognition API to transcribe "I forgot something at lunchtime, could you please bring it to me" into text. The server then sends a push notification to User B's device, and the message is displayed on User B's device.

[0068] This invention allows users to quickly check voicemail and call waiting messages as text, ensuring they receive important messages even while on a call.

[0069] The following describes the processing flow.

[0070] Handling voicemails

[0071] Step 1:

[0072] Device: Detects incoming calls and displays a notification on the screen.

[0073] User: I don't answer incoming calls.

[0074] Step 2:

[0075] Server: If there is no response within a certain period of time (e.g., 30 seconds), it will automatically switch to voicemail mode.

[0076] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[0077] Step 3:

[0078] Server: Records the caller's voice message.

[0079] Server: Stores recorded voice messages as digital data.

[0080] Step 4:

[0081] Server: Uses a speech recognition API (e.g., Google® Cloud Speech-to-Text) to convert recorded audio data into text format.

[0082] Step 5:

[0083] Server: Sends the converted text message to the user's device via push notification or email.

[0084] Device: Receives notifications and displays messages on the screen.

[0085] Call waiting

[0086] Step 1:

[0087] Terminal: Detects when a new call is coming in while the user is on a call.

[0088] Terminal: Notifies the user that a call is in progress.

[0089] User: I am currently on another call and cannot answer any new incoming calls.

[0090] Step 2:

[0091] Server: Automatically notifies new callers that the server is currently on another call and switches to call waiting mode.

[0092] Step 3:

[0093] Server: Records a voice message from a new caller.

[0094] Server: Stores recorded voice messages as digital data.

[0095] Step 4:

[0096] Server: Converts recorded audio data into text format using a speech recognition API.

[0097] Step 5:

[0098] Server: Sends a text-based message to the user's device via push notification or email.

[0099] Device: Receives notifications and displays text messages on the screen.

[0100] (Example 1)

[0101] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] Traditional voicemail systems and call waiting functions struggled to efficiently manage incoming messages, making it easy to miss important messages, especially when the user was away or on another call. Furthermore, the process of directly reviewing recorded messages was cumbersome, making quick responses difficult.

[0103] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0104] In this invention, the server includes means for detecting an incoming call, means for switching to voicemail mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for notifying the user's terminal of the converted text data, means for displaying an incoming call notification on the terminal, means for notifying the caller that voicemail mode is in effect, means for sending a text message via push notification or email, and means for displaying a text message on the terminal screen. This allows the user to quickly check important messages in text while away or on a call, enabling efficient management.

[0105] "Incoming call" refers to a state where there is a new call or notification to a communication device.

[0106] "Answering machine mode" is a feature that records incoming calls and allows users to play them back later if they are unable to answer the phone.

[0107] "Call waiting mode" is a feature that notifies you of an incoming call and allows you to record a message when a new call comes in while you are on a call.

[0108] A "voice message" is a recorded voice data left by the sender.

[0109] "Audio data" refers to data in which recorded audio is stored in digital format.

[0110] "Converting to text" refers to the process of analyzing audio data and converting it into written information.

[0111] "Push notifications" are a function that instantly sends specific information to a user's device to notify them.

[0112] A "terminal" refers to a communication device or equipment used by a user.

[0113] A "server" is a central processing unit that manages the entire system and handles data processing and transmission.

[0114] A "speech recognition API" is an application interface for converting speech data into text.

[0115] The "sender" is the person or device that initiates communication and leaves a message.

[0116] "Within a certain time period" refers to a specific time frame that has been set in advance.

[0117] "Recording" refers to the act of saving an audio message as digital data.

[0118] "To notify" refers to the act of informing a user of specific information.

[0119] "On a call" means that a communication connection is established and voice communication is taking place.

[0120] This invention provides a next-generation answering machine system and call waiting function to efficiently manage messages received by users while they are away or on a call. The system is configured in which the server, terminal, and user elements work together in coordination.

[0121] System Configuration

[0122] 1. Server

[0123] The server is the central processing unit of the system and is responsible for detecting incoming calls, switching to answering machine mode and call waiting mode, recording voice messages, converting voice data to text, and notifying users of the converted text data.

[0124] When a call comes in, the server receives the signal sent from the terminal, and if there is no response from the user within a certain period of time, it switches to voicemail mode.

[0125] 2. Terminal

[0126] A terminal is a communication device or equipment used by a user, and it displays incoming call notifications, communicates data with a server, and displays text messages based on recorded voice data.

[0127] When a call comes in to the device, a notification is displayed on the screen, and if the user does not answer, a no-response signal is sent to the server.

[0128] 3. User

[0129] The user uses their device to check incoming calls and call waiting notifications, and to receive and confirm text messages sent from the server.

[0130] Users view text messages on their device screen and respond or take action as needed.

[0131] Hardware and software to be used

[0132] Hardware:

[0133] The server will use standard server equipment.

[0134] The terminals used will be communication devices such as smartphones, tablets, and personal computers.

[0135] software:

[0136] The server uses the Google Cloud Speech-to-Text API as its speech recognition API to convert speech data into text.

[0137] The device uses dedicated applications or standard OS functions for incoming call notifications and text message display.

[0138] Specific example

[0139] Examples of how to handle voicemails:

[0140] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses the Google Cloud Speech-to-Text API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." The server then sends a push notification to User A's device, and the message is displayed on User A's device.

[0141] Examples of how to handle call waiting:

[0142] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records the audio and uses the Google Cloud Speech-to-Text API to transcribe the message into text. The server then sends a push notification to User B's device, and the message is displayed on User B's device.

[0143] Example of a prompt

[0144] Examples of prompts to input into a generative AI model:

[0145] Please explain the specific processing steps involved in recording voice data in an answering machine system, from recording to text conversion.

[0146] "Please explain, with specific examples, how the call waiting function transcribes and displays incoming messages while a call is in progress."

[0147] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0148] Step 1: Incoming Call Detection

[0149] Terminal: When a call comes in to the terminal, a notification pops up on the screen based on hardware signals (telephone line or internet calling protocol). The screen displays the caller's name and number.

[0150] Input: Incoming signal

[0151] Output: Incoming call notification pop-up display

[0152] Specific operation: The terminal analyzes the incoming signal received by the network module and displays a notification on the user interface.

[0153] Step 2: Check for response status

[0154] Terminal: If the user does not respond to an incoming call notification, a timer is started for a set period of time (e.g., 30 seconds). When the timer expires, a no-response signal is sent to the server.

[0155] Input: No response status and timer

[0156] Output: No response to the server.

[0157] Specific operation: The terminal's timer function activates for 30 seconds, and if there is no response, it sends a "no response" signal to the server via the network module.

[0158] Step 3: Switch to answering machine mode

[0159] Server: After receiving a no-response signal, the server plays an automated voice message to the caller informing them that the call has switched to voicemail mode.

[0160] Input: No response signal

[0161] Output: Voicemail mode notification to the caller

[0162] Specific operation: The server plays an automated voice message and notifies the caller via the voice transmission module that the call has switched to voicemail mode.

[0163] Step 4: Record a voice message

[0164] Server: Records the caller's voice message as digital data. Recording is limited to a certain time (e.g., 3 minutes).

[0165] Input: Caller's voice

[0166] Output: Digital audio data

[0167] Specific operation: The server's voice input module receives the caller's voice, converts it to a digital format, and records it.

[0168] Step 5: Convert speech to text

[0169] Server: Converts recorded audio data into text using a speech recognition API (e.g., Google Cloud Speech-to-Text API).

[0170] Input: Digital audio data

[0171] Output: Text data

[0172] Specific operation: The server sends digital audio data to the API, where it is parsed and converted before being received as text data.

[0173] Step 6: Sending Text Messages

[0174] Server: Sends the converted text data to the user's device via push notification or email.

[0175] Input: Text data

[0176] Output: Push notification or email

[0177] Specific operation: The server's communication module packets the text data and sends it to the terminal over the network.

[0178] Step 7: Displaying the message on the device

[0179] Device: The device displays the translated text message to the user via the device's notification center or a dedicated app. Example: "You have a new message: Tomorrow's meeting has been changed to 2 PM."

[0180] Input: Notification data from the server

[0181] Output: Text message on the screen

[0182] Specific operation: The terminal's user interface receives data from the server and displays it on the screen as a complete text message.

[0183] (Application Example 1)

[0184] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0185] Traditional answering machines and call waiting features require users to listen to incoming messages while on another call or away from their desk, making quick content review difficult. Furthermore, the lack of a function to convert voice messages to text makes it challenging for store employees, in particular, to efficiently manage messages while assisting customers. Additionally, there's a risk of missing important messages from callers while performing store duties.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0187] In this invention, the server includes means for detecting incoming calls, means for switching to voicemail mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for notifying the user's mobile device of the converted text data, means for automatically switching to voicemail mode if a store employee is unable to answer, and means for displaying the notified text message on the user's mobile device. This enables efficient management of messages when employees are absent or busy in physical stores, and allows for quick confirmation of important messages.

[0188] "Means for detecting incoming calls" refers to a function that recognizes that an incoming call has been received on the user's terminal via a communication network.

[0189] "A means of switching to voicemail mode when there is no response within a certain period of time" refers to a function that automatically switches to voicemail mode if the user does not answer within a certain period of time after an incoming call.

[0190] "Means for recording a caller's voice message" refers to a function that records a voice message left by a caller as digital data.

[0191] "Means for converting recorded audio data into text" refers to a function that converts recorded audio messages into text data using speech recognition technology.

[0192] "Means of notifying the user of converted text data on their mobile device" refers to a function that sends the converted text message to the user's mobile device via push notification or email.

[0193] "A mechanism to automatically switch to answering machine mode when a store employee is unavailable" refers to a function that automatically switches to answering machine mode when a store employee is busy or absent.

[0194] "Means for displaying text messages notified to the user's mobile device" refers to a function that displays text messages sent to the user's mobile device on the screen.

[0195] To implement this invention, the following system is required. This system consists of a server, a user terminal, and a communication network connecting them.

[0196] server

[0197] The server has the following functions:

[0198] 1. Means for detecting incoming calls: This is a function that recognizes that an incoming call has been received on the user terminal via the communication network.

[0199] 2. Means of switching to voicemail mode when there is no response within a certain period of time: This function automatically switches to voicemail mode when the user does not answer within a certain period of time after an incoming call.

[0200] 3. Means for recording the caller's voice message: This is a function that records the voice message left by the caller as digital data.

[0201] 4. Means for converting recorded audio data to text: This function converts recorded audio messages into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text API).

[0202] 5. Means of notifying the user of the converted text data on their mobile device: This function sends the converted text message to the user's mobile device via push notification or email.

[0203] 6. A mechanism to automatically switch to answering machine mode when store employees are unavailable: This function automatically switches to answering machine mode when store employees are busy or absent.

[0204] User terminal

[0205] User devices (for example, smartphones) have the following functions:

[0206] 1. Means for displaying incoming call, voicemail, and call waiting notifications: This is a function that receives notifications sent from the server and displays them on the screen.

[0207] 2. Means for receiving and displaying text messages sent from the server: This is a function that receives text messages sent from the server and displays them visually to the user.

[0208] User actions

[0209] The user performs the following actions:

[0210] 1. Check notifications: Check incoming call and message notifications displayed on the user's device.

[0211] 2. Check text messages: Check text messages sent from the server and take action as necessary.

[0212] Specific example

[0213] For example, consider a scenario where a store employee is assisting a customer when a phone call comes in. Since the employee cannot answer the phone, the system automatically switches to voicemail mode, and the caller leaves a message. The caller's voice message, an inquiry about inventory, is recorded and converted to text using the Google Cloud Speech-to-Text API. The text message is then sent as a push notification to the store employee's smartphone, allowing them to immediately check the message.

[0214] Example of a prompt

[0215] The following is an example of a prompt statement to input into a generative AI model:

[0216] "I'd like to practice a next-generation voicemail system that converts phone voice messages written in Japanese into text. Please describe the following scenario where a call is received but not answered. Also, please include how the caller's message is converted to text and how it is notified to the user."

[0217] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0218] Step 1:

[0219] The server detects incoming calls to user terminals via the communication network. The input is the incoming signal from the communication network, and the server analyzes this signal to determine if an incoming call has occurred. The output is an internal flag indicating that an incoming call has been detected.

[0220] Step 2:

[0221] The server automatically switches to answering machine mode if the user does not respond within a certain period of time. The inputs are the output from step 1 (incoming call detection flag) and the user's response status (no response). As data processing, a timer is started, and if there is no response within a certain period of time, the answering machine mode flag is set. The output is the signal to switch to answering machine mode.

[0222] Step 3:

[0223] After switching to answering machine mode, the server records the voice message left by the caller. The inputs are the output from step 2 (answering machine mode signal) and the caller's voice signal. As a data processing step, the voice data is recorded in digital format. The output is the recorded voice data.

[0224] Step 4:

[0225] The server converts the recorded audio data into text. The input is the output from step 3 (recorded data). As a data operation, a speech recognition API (e.g., Google Cloud Speech-to-Text) is used to convert the audio data into text data. The output is the transcribed message.

[0226] Step 5:

[0227] The server notifies the user's mobile device of the text-based message. The input is the output (text message) from step 4. As data processing, the text message is sent in the form of a push notification or email. The output is a flag indicating that the notification has been successfully sent to the user's device.

[0228] Step 6:

[0229] The user terminal receives text messages notified from the server and displays them on the screen. The input is the notification signal from the server. As part of data processing, the notification is analyzed and the content to be displayed is extracted. The output is the text message displayed on the screen.

[0230] Step 7:

[0231] The user checks the text message displayed on the device and takes action as needed. The input is the output of step 6 (the text message displayed on the screen). The user reads the displayed content and takes appropriate action based on it. The output is the user's response.

[0232] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0233] This invention provides a next-generation communication system that combines a conventional answering machine system and call waiting function with an emotion engine, enabling the system to understand not only the content of the user's voice message but also their emotional state. The system operates through the coordinated efforts of the server, terminal, and user elements.

[0234] System Configuration

[0235] 1. Server

[0236] It detects incoming calls and switches to voicemail mode and call waiting mode.

[0237] The system records the caller's voice message and utilizes a speech recognition API to convert the audio data into text.

[0238] The system uses an emotion engine to recognize and analyze emotions from recorded audio data.

[0239] The converted text data and sentiment analysis results are sent to the user's device.

[0240] 2. Terminal

[0241] Displays notifications for incoming calls and call waiting.

[0242] Receives text messages and sentiment analysis results sent from the server and displays them to the user.

[0243] 3. User

[0244] Check the incoming call and call waiting notifications on your device's screen.

[0245] Receive and review text messages and sentiment analysis results sent from the server.

[0246] Program processing

[0247] 1. Incoming call detection

[0248] Terminal: Detects an incoming call and displays a notification on the screen. If the user does not respond, the terminal sends a no-response signal to the server.

[0249] Server: If no response is received within a certain period of time, it will automatically switch to voicemail mode.

[0250] 2. Switch to voicemail mode

[0251] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[0252] Server: Records the caller's voice message.

[0253] 3. Recording and converting voice messages

[0254] Server: Stores recorded voice messages as digital data.

[0255] Server: Uses a speech recognition API to convert audio data into text format.

[0256] 4. Recognition and Analysis of Emotions

[0257] Server: Inputs recorded audio data into the emotion engine to recognize the speaker's emotions.

[0258] Server: Stores emotional data analyzed by the emotion engine.

[0259] 5. Delivery of text messages and sentiment analysis results

[0260] Server: Sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[0261] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[0262] Specific example

[0263] Examples of how to handle voicemails:

[0264] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." Simultaneously, it uses an emotion engine to recognize the emotion "nervousness" from the caller's voice. The server sends the transcribed message and the emotion analysis results to User A's device via push notification, and the message and the "nervousness" notification are displayed on User A's device.

[0265] Examples of how to handle call waiting:

[0266] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts User B to leave a message for the new caller (family member). The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records this audio and uses a speech recognition API to transcribe it into text. Simultaneously, it uses an emotion engine to recognize the emotion "anxiety" from the caller's voice. The server sends the transcribed message and the emotion analysis results as a push notification to User B's device, displaying the message and the "anxiety" notification on User B's device.

[0267] This invention allows users to quickly check voicemail and call waiting messages, and also understand the caller's emotions, enabling them to respond more quickly and appropriately.

[0268] The following describes the processing flow.

[0269] Handling voicemails

[0270] Step 1:

[0271] Device: Detects incoming calls and displays a notification on the screen.

[0272] User: I don't answer incoming calls.

[0273] Step 2:

[0274] Server: If no response is received within a certain period of time (e.g., 30 seconds), it will automatically switch to voicemail mode.

[0275] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[0276] Step 3:

[0277] Server: Records the caller's voice message.

[0278] Server: Save the recorded voice message as digital data.

[0279] Step 4:

[0280] Server: Use a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the recorded voice data into text format.

[0281] Step 5:

[0282] Server: Use an emotion engine to recognize and analyze the emotion of the caller from the recorded voice data.

[0283] Server: Save the emotion data analyzed by the emotion engine.

[0284] Step 6:

[0285] Server: Push notify or send an email to the user's terminal with the converted text data and the emotion analysis result.

[0286] Terminal: Receive the notification and display the text message and the emotion analysis result on the screen.

[0287] Handling of call waiting

[0288] Step 1:

[0289] Terminal: Detect that there is a new incoming call while the user is on a call.

[0290] Terminal: Notify the user that they are on a call.

[0291] User: Cannot respond to the new incoming call because they are on a call.

[0292] Step 2:

[0293] Server: Automatically notifies new callers that the server is currently on another call and will switch them to call waiting mode.

[0294] Step 3:

[0295] Server: Records a voice message from a new caller.

[0296] Server: Stores recorded voice messages as digital data.

[0297] Step 4:

[0298] Server: Uses a speech recognition API to convert recorded audio data into text format.

[0299] Step 5:

[0300] Server: Uses an emotion engine to recognize and analyze the emotions of new callers from recorded audio data.

[0301] Server: Stores the sentiment data analyzed by the sentiment engine.

[0302] Step 6:

[0303] Server: Sends the transcribed message and sentiment analysis results to the user's device via push notification or email.

[0304] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[0305] (Example 2)

[0306] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0307] In conventional answering machine systems and answering machine functions, although it is possible to record and convert the voice message of the caller into text, it is not possible to grasp the emotional state of the caller. Therefore, when the user checks the content of the message, it is difficult to read the emotions of the caller and it may be difficult to take appropriate actions. The purpose of the present invention is to solve such problems and provide a next-generation communication system that not only allows the user to quickly check the voice messages of the answering machine and the answering machine, but also enables the user to grasp the emotions of the caller.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0309] In this invention, the server includes means for detecting an incoming call, means for switching to the answering machine mode when there is no response within a certain period of time, means for recording the voice message of the caller, means for converting the recorded voice data into text, means for recognizing and analyzing emotions from the voice data, and means for notifying the user's terminal of the converted text data and the emotion analysis result. Thereby, not only can the user check the content of the voice message, but it is also possible to grasp the emotional state of the caller.

[0310] The "means for detecting an incoming call" is a device or software for a communication terminal to receive a telephone signal from the outside and recognize that the signal is an incoming call.

[0311] The "means for switching to the answering machine mode when there is no response within a certain period of time" is a device or software for automatically switching the communication terminal to the message recording mode when there is no response from the user within a certain period of time after an incoming call.

[0312] The "means for recording the voice message of the caller" is a device or software for recording the voice message left by the call partner as digital data.

[0313] "Means for converting recorded audio data into text" refers to a device or software that utilizes speech recognition technology to analyze recorded audio data and convert it into corresponding text data.

[0314] "Means for recognizing and analyzing emotions from audio data" refers to a device or software that analyzes audio data to recognize the emotions of the speaker contained within it, and further analyzes those emotions.

[0315] "Means for notifying the user's terminal of converted text data and sentiment analysis results" refers to a device or software that transmits text data generated from speech and the results of sentiment analysis to the user's communication terminal via push notifications, email, or the like.

[0316] "Means for detecting incoming calls while a call is in progress" refers to a device or software that notifies a user of a new incoming call, even if the user is already on another call.

[0317] "Means of switching to call waiting mode when the user is unable to answer" refers to a device or software that automatically switches to message recording mode when a new incoming call occurs and the user is unable to answer it.

[0318] "Means for recording a new caller's voice message" refers to a device or software that records a voice message from a newly called party as digital data when the phone switches to call waiting mode.

[0319] "Methods for using speech recognition APIs to convert audio data to text" refers to devices or software that use external speech recognition services to perform analysis when converting audio data to text data.

[0320] "Means of utilizing an emotion engine for recognizing and analyzing emotions from voice data" refers to a device or software that uses an external emotion analysis service to recognize emotions based on voice data and analyze the results.

[0321] "Means for sending text messages and sentiment analysis results to the user via push notification or email" refers to a device or software that sends converted text data and sentiment analysis results to the user's communication terminal via email or push notification.

[0322] This invention is a next-generation communication system that can understand not only the content of a user's voice message but also their emotional state. This system operates through the coordinated efforts of the server, terminal, and user.

[0323] System Configuration

[0324] 1. Server

[0325] The server detects incoming calls and switches to voicemail mode and call waiting mode. When an incoming call occurs, the server checks if there is no response within a certain period of time and automatically switches to voicemail mode.

[0326] The server records the caller's voice message and saves the audio data to cloud storage. Then, it uses a speech recognition API (e.g., Google Cloud Speech-to-Text, Microsoft® Azure® Speech API) to convert the audio data into text format.

[0327] The server inputs the converted text data into an emotion engine (e.g., IBM Watson® Tone Analyzer, Affectiva SDK) to recognize and analyze the sender's emotions.

[0328] The server sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[0329] 2. Terminal

[0330] The device displays notifications for incoming calls and call waiting. If the user does not answer, the device sends a "no answer" signal to the server.

[0331] The system receives text messages and sentiment analysis results sent from the server and displays them to the user on the screen.

[0332] 3. User

[0333] The user checks incoming calls and call waiting notifications on their device screen.

[0334] Receive and review text messages and sentiment analysis results sent from the server.

[0335] Specific examples of the system

[0336] Examples of how to handle voicemails:

[0337] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." Simultaneously, it uses an emotion engine to recognize the emotion "tension" from the caller's voice. The server sends the transcribed message and the emotion analysis results as a push notification to User A's device, and the message and the "tension" notification are displayed on User A's device.

[0338] Examples of how to handle call waiting:

[0339] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records this audio and uses a speech recognition API to transcribe it into text. Simultaneously, it uses an emotion engine to recognize the emotion "anxiety" from the caller's voice. The server sends the transcribed message and the emotion analysis results as push notifications to User B's device, and the message and the "anxiety" notification are displayed on User B's device.

[0340] Example of a prompt

[0341] Entering the following prompt statements into the generated AI model will generate specific scenarios for this system.

[0342] "The system records the user's voice message and converts it to text using a speech recognition API. Then, it uses an emotion engine to analyze the caller's emotions and notifies the user of the results. As a concrete example, imagine a scenario where user A does not answer a call and the server switches to voicemail mode. Please describe the process in this scenario."

[0343] This system allows users to quickly check voicemail and call waiting messages, and also understand the caller's emotions, enabling them to respond more appropriately.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1: Incoming Call Detection

[0346] Terminal: The terminal receives incoming signals from the communication network. The input is the incoming signal, and the output is the incoming notification. The terminal displays a notification on the screen saying "You have a new incoming call" and waits for the user to respond. If the user does not respond, it sends a "no response" signal to the server after a certain period of time (e.g., 30 seconds).

[0347] Specific actions: The device's speaker sounds, and an incoming call notification is displayed on the screen.

[0348] Step 2: Switch to home security mode

[0349] Server: The server receives a "no response" signal from the terminal and switches to answering machine mode. The input is the "no response" signal, and the output is a notification that answering machine mode has started. The server then notifies the caller with an automated voice message saying, "Your phone has been switched to answering machine mode. Please leave a message."

[0350] Specific action: The server plays an automated voice message prompting the caller to record a message.

[0351] Step 3: Record and convert voice messages

[0352] Server: The server records the caller's voice message and saves it as audio data to cloud storage. Next, it sends the recorded audio data to a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert it into text format. The input is the recorded audio data, and the output is text data.

[0353] Specific operation: The server generates an audio data file and sends it to a speech recognition API for text conversion.

[0354] Step 4: Recognition and Analysis of Emotions

[0355] Server: The server inputs text data obtained from the speech recognition API and the original audio data into an emotion engine (e.g., IBM Watson Tone Analyzer) to recognize and analyze the speaker's emotions. The analysis results are stored in a database. The input is audio data and text data, and the output is the emotion analysis result.

[0356] Specific operation: The server inputs data into the emotion engine and retrieves emotion labels such as "tension," "joy," and "anger," along with their intensity.

[0357] Step 5: Delivery of text messages and sentiment analysis results

[0358] Server: The server sends text data and sentiment analysis results to the user's device via push notification or email. The input is text data and sentiment analysis results, and the output is a notification to the user's device.

[0359] Specific operation: The server uses the notification API to generate a push notification and sends it to the user's device saying, "Tomorrow's meeting has been changed to 2 PM. Emotion: Nervous."

[0360] Terminal: The terminal displays received text messages and sentiment analysis results on its screen. Input is notification data, and output is the screen display.

[0361] Specific action: The device displays the following message on the screen: "New voicemail: Tomorrow's meeting has been changed to 2 PM. Emotion: Nervous."

[0362] In this way, the system performs a series of processes that allow the user not only to confirm the content of the voice message but also to understand the emotions of the caller.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0365] Traditional communication systems could convert voicemail and call waiting messages into text, but they could not capture the caller's emotional state. Therefore, it was difficult for users to quickly and accurately assess not only the message content but also the caller's emotional state and take appropriate action. Furthermore, there was no means to analyze user emotions during high-risk transactions or logins to enhance security.

[0366] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for detecting incoming calls, means for switching to answering machine mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for analyzing emotions from the recorded voice data, means for notifying the user's terminal of the emotion analysis results, and means for notifying the user's terminal of the converted text data and the emotion analysis results. As a result, the user can understand not only the content of the message but also the emotions of the caller, enabling a quicker and more appropriate response. Furthermore, by analyzing the user's emotional state during high-risk transactions or logins, security can be enhanced.

[0367] "Incoming call" means receiving a phone call.

[0368] "Answering mode" is a mode that records a voice message from the caller if there is no response within a certain period of time.

[0369] A "voice message" refers to a message that is conveyed through voice.

[0370] "Recording" means saving sound as digital data.

[0371] "Audio data" refers to the digital representation of recorded sound.

[0372] Converting to "text" is the process of converting audio data into written characters.

[0373] "Notification" refers to sending and displaying information on a user's device.

[0374] "Sentiment analysis" is the process of recognizing and evaluating the emotional state of the caller from audio data.

[0375] An "emotion engine" is a technology for analyzing and recognizing emotions.

[0376] A "device" is a device that the user directly operates. Specific examples include smartphones and smart glasses.

[0377] "Push notifications" are a method of sending information to a user's device in real time.

[0378] "Security" refers to the means of protecting systems and users from unauthorized access and high-risk transactions.

[0379] This invention combines an emotion engine with conventional communication systems to achieve more advanced message communication and enhanced security. The specific configuration and operation of the system are described below.

[0380] System Configuration

[0381] The system primarily consists of server, terminal, and user elements.

[0382] server

[0383] The server is responsible for the following functions:

[0384] 1. Incoming Call Detection: The server detects incoming calls and switches to voicemail mode if there is no response within a certain period of time.

[0385] 2. Recording voice messages: Record voice messages left by the caller as digital data.

[0386] 3. Converting audio data to text: Convert recorded audio data to text using a speech recognition API (e.g., Google Speech-to-Text API).

[0387] 4. Emotional Analysis: The converted audio data is input into an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotions.

[0388] 5. Notification of Results: The text message and sentiment analysis results will be sent to the user's device via push notification or email.

[0389] terminal

[0390] The device is responsible for the following functions:

[0391] 1. Displaying notifications: Receive text messages and sentiment analysis results sent from the server and display them to the user.

[0392] 2. Incoming call and call waiting notifications: Receive and display notifications for incoming calls and call waiting.

[0393] User

[0394] The user performs the following actions:

[0395] 1. Check the message: Check the text message and sentiment analysis results on your device screen.

[0396] 2. Appropriate response: Take necessary actions promptly based on the message and emotional state.

[0397] Hardware and software

[0398] The hardware and software used to implement this system are as follows:

[0399] Hardware: Smartphones, smart glasses

[0400] Speech recognition API: Example: Google Speech-to-Text API

[0401] Emotion engine: Example: IBM Watson Tone Analyzer

[0402] Specific example

[0403] Examples of home security features

[0404] User A receives a call on their smartphone, and since there is no answer within a certain time, it switches to voicemail mode. The caller leaves a message saying, "An important meeting will be delayed until 2 PM." The server records this message and uses the Google Speech-to-Text API to transcribe it as "An important meeting will be delayed until 2 PM." Simultaneously, IBM Watson Tone Analyzer analyzes the sentiment and determines it to be "confused." The server pushes this text message and the sentiment analysis results to User A's smartphone, allowing User A to review the message and their emotional state.

[0405] Examples of security enhancements

[0406] User B is attempting a large transaction via internet banking. The server records User B's voice commands and converts them to text using the Google Speech-to-Text API. Simultaneously, it analyzes User B's emotions using IBM Watson Tone Analyzer and detects "anxiety." Based on these results, the server puts the transaction on hold and sends a confirmation notification to User B's smartphone.

[0407] Example of a prompt

[0408] Examples of prompts to input into a generative AI model are as follows:

[0409] Please input the audio data and perform sentiment analysis. If the results show an anomaly (abnormality), please issue an alert.

[0410] In this way, this invention enables users to understand not only the content of voice messages but also the emotional state of the caller, and further contributes to enhanced security.

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] The user's device detects an incoming call. The device receives the incoming call signal and notifies the user. If the user does not respond within a certain time, the device sends a no-response signal to the server. The input is the incoming call signal, and the output is the no-response signal.

[0414] Step 2:

[0415] The server receives a no-response signal from the terminal and switches to answering machine mode. At this point, the server automatically notifies the caller that the device has entered answering machine mode and records the caller's message. The input is the no-response signal, and the output is the recorded voice data.

[0416] Step 3:

[0417] The server stores the recorded audio data and uses the Google Speech-to-Text API to convert the audio data to text. The server inputs the audio data into the API and retrieves the text data. In this step, the input is the audio data and the output is the transcribed data.

[0418] Step 4:

[0419] The server performs sentiment analysis on the transcribed audio data using IBM Watson Tone Analyzer. The text data is input to the sentiment analysis engine, and the sentiment analysis results are obtained. The input is text data, and the output is the sentiment analysis results.

[0420] Step 5:

[0421] The server sends the converted text data and sentiment analysis results to the user's device via push notification or email. Here, the server formats the text data and sentiment analysis results and sends them to the device. The input is the text data and sentiment analysis results, and the output is the notification sent to the user's device.

[0422] Step 6:

[0423] The user's device receives notifications sent from the server and displays the text message and sentiment analysis results on the screen. Here, the device parses the notification to generate data to display to the user and displays it on the screen. The input is the notification from the server, and the output is the text and sentiment analysis results displayed to the user.

[0424] Step 7:

[0425] The user reviews the received text message and sentiment analysis results on the device screen. Based on this information, the user decides on the necessary actions. The input is the displayed text and sentiment analysis results, and the output is the user's actions and decisions.

[0426] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0427] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0428] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0429] [Second Embodiment]

[0430] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0431] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0432] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0433] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0434] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0435] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0436] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0437] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0438] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0439] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0440] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0441] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0442] This invention provides a next-generation answering machine system and call waiting function, enabling users to efficiently manage messages received on their answering machine or during calls. The system operates through the coordinated efforts of a server, terminal, and user.

[0443] System Configuration

[0444] 1. Server

[0445] It detects incoming calls and switches to voicemail mode and call waiting mode.

[0446] The system records the caller's voice message and utilizes a speech recognition API to convert the audio data into text.

[0447] The converted text data is sent to the user's device.

[0448] 2. Terminal

[0449] Displays notifications for incoming calls and call waiting.

[0450] Receive text messages sent from the server and display them to the user.

[0451] 3. User

[0452] Check the incoming call and call waiting notifications on your device's screen.

[0453] Receive and verify the text message sent from the server.

[0454] Program processing

[0455] 1. Incoming call detection

[0456] Terminal: When an incoming call is received, a notification is displayed on the screen. If the user does not answer, the terminal sends a no-response signal to the server.

[0457] Server: If no response is received within a certain period of time, it will automatically switch to voicemail mode.

[0458] 2. Switch to voicemail mode

[0459] Server: Notifies the caller via an automated voice message that the call has switched to voicemail mode.

[0460] Server: Records the caller's voice message.

[0461] 3. Recording and converting voice messages

[0462] Server: Stores recorded voice messages as digital data.

[0463] Server: Uses a speech recognition API to convert speech data into text.

[0464] 4. Text message delivery

[0465] Server: Sends the converted text data to the user's device via push notification or email.

[0466] Device: Receives notifications and displays text messages on the screen.

[0467] Specific example

[0468] Examples of how to handle voicemails:

[0469] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text: "Tomorrow's meeting has been changed to 2 PM." The server then sends a push notification to User A's device, and the message is displayed on User A's device.

[0470] Examples of how to handle call waiting:

[0471] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records the audio and uses a speech recognition API to transcribe "I forgot something at lunchtime, could you please bring it to me" into text. The server then sends a push notification to User B's device, and the message is displayed on User B's device.

[0472] This invention allows users to quickly check voicemail and call waiting messages as text, ensuring they receive important messages even while on a call.

[0473] The following describes the processing flow.

[0474] Handling voicemails

[0475] Step 1:

[0476] Device: Detects incoming calls and displays a notification on the screen.

[0477] User: I don't answer incoming calls.

[0478] Step 2:

[0479] Server: If there is no response within a certain period of time (e.g., 30 seconds), it will automatically switch to voicemail mode.

[0480] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[0481] Step 3:

[0482] Server: Records the caller's voice message.

[0483] Server: Stores recorded voice messages as digital data.

[0484] Step 4:

[0485] Server: Uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert recorded audio data into text format.

[0486] Step 5:

[0487] Server: Sends the converted text message to the user's device via push notification or email.

[0488] Device: Receives notifications and displays messages on the screen.

[0489] Call waiting

[0490] Step 1:

[0491] Terminal: Detects when a new call is coming in while the user is on a call.

[0492] Terminal: Notifies the user that a call is in progress.

[0493] User: I am currently on another call and cannot answer any new incoming calls.

[0494] Step 2:

[0495] Server: Automatically notifies new callers that the server is currently on another call and switches to call waiting mode.

[0496] Step 3:

[0497] Server: Records a voice message from a new caller.

[0498] Server: Stores recorded voice messages as digital data.

[0499] Step 4:

[0500] Server: Converts recorded audio data into text format using a speech recognition API.

[0501] Step 5:

[0502] Server: Sends a text-based message to the user's device via push notification or email.

[0503] Device: Receives notifications and displays text messages on the screen.

[0504] (Example 1)

[0505] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0506] Traditional voicemail systems and call waiting functions struggled to efficiently manage incoming messages, making it easy to miss important messages, especially when the user was away or on another call. Furthermore, the process of directly reviewing recorded messages was cumbersome, making quick responses difficult.

[0507] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0508] In this invention, the server includes means for detecting an incoming call, means for switching to voicemail mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for notifying the user's terminal of the converted text data, means for displaying an incoming call notification on the terminal, means for notifying the caller that voicemail mode is in effect, means for sending a text message via push notification or email, and means for displaying a text message on the terminal screen. This allows the user to quickly check important messages in text while away or on a call, enabling efficient management.

[0509] "Incoming call" refers to a state where there is a new call or notification to a communication device.

[0510] "Answering machine mode" is a feature that records incoming calls and allows users to play them back later if they are unable to answer the phone.

[0511] "Call waiting mode" is a feature that notifies you of an incoming call and allows you to record a message when a new call comes in while you are on a call.

[0512] A "voice message" is a recorded voice data left by the sender.

[0513] "Audio data" refers to data in which recorded audio is stored in digital format.

[0514] "Converting to text" refers to the process of analyzing audio data and converting it into written information.

[0515] "Push notifications" are a function that instantly sends specific information to a user's device to notify them.

[0516] A "terminal" refers to a communication device or equipment used by a user.

[0517] A "server" is a central processing unit that manages the entire system and handles data processing and transmission.

[0518] A "speech recognition API" is an application interface for converting speech data into text.

[0519] The "sender" is the person or device that initiates communication and leaves a message.

[0520] "Within a certain time period" refers to a specific time frame that has been set in advance.

[0521] "Recording" refers to the act of saving an audio message as digital data.

[0522] "To notify" refers to the act of informing a user of specific information.

[0523] "On a call" means that a communication connection is established and voice communication is taking place.

[0524] This invention provides a next-generation answering machine system and call waiting function to efficiently manage messages received by users while they are away or on a call. The system is configured in which the server, terminal, and user elements work together in coordination.

[0525] System Configuration

[0526] 1. Server

[0527] The server is the central processing unit of the system and is responsible for detecting incoming calls, switching to answering machine mode and call waiting mode, recording voice messages, converting voice data to text, and notifying users of the converted text data.

[0528] When a call comes in, the server receives the signal sent from the terminal, and if there is no response from the user within a certain period of time, it switches to voicemail mode.

[0529] 2. Terminal

[0530] A terminal is a communication device or equipment used by a user, and it displays incoming call notifications, communicates data with a server, and displays text messages based on recorded voice data.

[0531] When a call comes in to the device, a notification is displayed on the screen, and if the user does not answer, a no-response signal is sent to the server.

[0532] 3. User

[0533] The user uses their device to check incoming calls and call waiting notifications, and to receive and confirm text messages sent from the server.

[0534] Users view text messages on their device screen and respond or take action as needed.

[0535] Hardware and software to be used

[0536] Hardware:

[0537] The server will use standard server equipment.

[0538] The terminals used will be communication devices such as smartphones, tablets, and personal computers.

[0539] software:

[0540] The server uses the Google Cloud Speech-to-Text API as its speech recognition API to convert speech data into text.

[0541] The device uses dedicated applications or standard OS functions for incoming call notifications and text message display.

[0542] Specific example

[0543] Examples of how to handle voicemails:

[0544] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses the Google Cloud Speech-to-Text API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." The server then sends a push notification to User A's device, and the message is displayed on User A's device.

[0545] Examples of how to handle call waiting:

[0546] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records the audio and uses the Google Cloud Speech-to-Text API to transcribe the message into text. The server then sends a push notification to User B's device, and the message is displayed on User B's device.

[0547] Example of a prompt

[0548] Examples of prompts to input into a generative AI model:

[0549] Please explain the specific processing steps involved in recording voice data in an answering machine system, from recording to text conversion.

[0550] "Please explain, with specific examples, how the call waiting function transcribes and displays incoming messages while a call is in progress."

[0551] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0552] Step 1: Incoming Call Detection

[0553] Terminal: When a call comes in to the terminal, a notification pops up on the screen based on hardware signals (telephone line or internet calling protocol). The screen displays the caller's name and number.

[0554] Input: Incoming signal

[0555] Output: Incoming call notification pop-up display

[0556] Specific operation: The terminal analyzes the incoming signal received by the network module and displays a notification on the user interface.

[0557] Step 2: Check for response status

[0558] Terminal: If the user does not respond to an incoming call notification, a timer is started for a set period of time (e.g., 30 seconds). When the timer expires, a no-response signal is sent to the server.

[0559] Input: No response status and timer

[0560] Output: No response to the server.

[0561] Specific operation: The terminal's timer function activates for 30 seconds, and if there is no response, it sends a "no response" signal to the server via the network module.

[0562] Step 3: Switch to answering machine mode

[0563] Server: After receiving a no-response signal, the server plays an automated voice message to the caller informing them that the call has switched to voicemail mode.

[0564] Input: No response signal

[0565] Output: Voicemail mode notification to the caller

[0566] Specific operation: The server plays an automated voice message and notifies the caller via the voice transmission module that the call has switched to voicemail mode.

[0567] Step 4: Record a voice message

[0568] Server: Records the caller's voice message as digital data. Recording is limited to a certain time (e.g., 3 minutes).

[0569] Input: Caller's voice

[0570] Output: Digital audio data

[0571] Specific operation: The server's voice input module receives the caller's voice, converts it to a digital format, and records it.

[0572] Step 5: Convert speech to text

[0573] Server: Converts recorded audio data into text using a speech recognition API (e.g., Google Cloud Speech-to-Text API).

[0574] Input: Digital audio data

[0575] Output: Text data

[0576] Specific operation: The server sends digital audio data to the API, where it is parsed and converted before being received as text data.

[0577] Step 6: Sending Text Messages

[0578] Server: Sends the converted text data to the user's device via push notification or email.

[0579] Input: Text data

[0580] Output: Push notification or email

[0581] Specific operation: The server's communication module packets the text data and sends it to the terminal over the network.

[0582] Step 7: Displaying the message on the device

[0583] Device: The device displays the translated text message to the user via the device's notification center or a dedicated app. Example: "You have a new message: Tomorrow's meeting has been changed to 2 PM."

[0584] Input: Notification data from the server

[0585] Output: Text message on the screen

[0586] Specific operation: The terminal's user interface receives data from the server and displays it on the screen as a complete text message.

[0587] (Application Example 1)

[0588] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0589] Traditional answering machines and call waiting features require users to listen to incoming messages while on another call or away from their desk, making quick content review difficult. Furthermore, the lack of a function to convert voice messages to text makes it challenging for store employees, in particular, to efficiently manage messages while assisting customers. Additionally, there's a risk of missing important messages from callers while performing store duties.

[0590] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0591] In this invention, the server includes means for detecting incoming calls, means for switching to voicemail mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for notifying the user's mobile device of the converted text data, means for automatically switching to voicemail mode if a store employee is unable to answer, and means for displaying the notified text message on the user's mobile device. This enables efficient management of messages when employees are absent or busy in physical stores, and allows for quick confirmation of important messages.

[0592] "Means for detecting incoming calls" refers to a function that recognizes that an incoming call has been received on the user's terminal via a communication network.

[0593] "A means of switching to voicemail mode when there is no response within a certain period of time" refers to a function that automatically switches to voicemail mode if the user does not answer within a certain period of time after an incoming call.

[0594] "Means for recording a caller's voice message" refers to a function that records a voice message left by a caller as digital data.

[0595] "Means for converting recorded audio data into text" refers to a function that converts recorded audio messages into text data using speech recognition technology.

[0596] "Means of notifying the user of converted text data on their mobile device" refers to a function that sends the converted text message to the user's mobile device via push notification or email.

[0597] "A mechanism to automatically switch to answering machine mode when a store employee is unavailable" refers to a function that automatically switches to answering machine mode when a store employee is busy or absent.

[0598] "Means for displaying text messages notified to the user's mobile device" refers to a function that displays text messages sent to the user's mobile device on the screen.

[0599] To implement this invention, the following system is required. This system consists of a server, a user terminal, and a communication network connecting them.

[0600] server

[0601] The server has the following functions:

[0602] 1. Means for detecting incoming calls: This is a function that recognizes that an incoming call has been received on the user terminal via the communication network.

[0603] 2. Means of switching to voicemail mode when there is no response within a certain period of time: This function automatically switches to voicemail mode when the user does not answer within a certain period of time after an incoming call.

[0604] 3. Means for recording the caller's voice message: This is a function that records the voice message left by the caller as digital data.

[0605] 4. Means for converting recorded audio data to text: This function converts recorded audio messages into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text API).

[0606] 5. Means of notifying the user of the converted text data on their mobile device: This function sends the converted text message to the user's mobile device via push notification or email.

[0607] 6. A mechanism to automatically switch to answering machine mode when store employees are unavailable: This function automatically switches to answering machine mode when store employees are busy or absent.

[0608] User terminal

[0609] User devices (for example, smartphones) have the following functions:

[0610] 1. Means for displaying incoming call, voicemail, and call waiting notifications: This is a function that receives notifications sent from the server and displays them on the screen.

[0611] 2. Means for receiving and displaying text messages sent from the server: This is a function that receives text messages sent from the server and displays them visually to the user.

[0612] User actions

[0613] The user performs the following actions:

[0614] 1. Check notifications: Check incoming call and message notifications displayed on the user's device.

[0615] 2. Check text messages: Check text messages sent from the server and take action as necessary.

[0616] Specific example

[0617] For example, consider a scenario where a store employee is assisting a customer when a phone call comes in. Since the employee cannot answer the phone, the system automatically switches to voicemail mode, and the caller leaves a message. The caller's voice message, an inquiry about inventory, is recorded and converted to text using the Google Cloud Speech-to-Text API. The text message is then sent as a push notification to the store employee's smartphone, allowing them to immediately check the message.

[0618] Example of a prompt

[0619] The following is an example of a prompt statement to input into a generative AI model:

[0620] "I'd like to practice a next-generation voicemail system that converts phone voice messages written in Japanese into text. Please describe the following scenario where a call is received but not answered. Also, please include how the caller's message is converted to text and how it is notified to the user."

[0621] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0622] Step 1:

[0623] The server detects incoming calls to user terminals via the communication network. The input is the incoming signal from the communication network, and the server analyzes this signal to determine if an incoming call has occurred. The output is an internal flag indicating that an incoming call has been detected.

[0624] Step 2:

[0625] The server automatically switches to answering machine mode if the user does not respond within a certain period of time. The inputs are the output from step 1 (incoming call detection flag) and the user's response status (no response). As data processing, a timer is started, and if there is no response within a certain period of time, the answering machine mode flag is set. The output is the signal to switch to answering machine mode.

[0626] Step 3:

[0627] After switching to answering machine mode, the server records the voice message left by the caller. The inputs are the output from step 2 (answering machine mode signal) and the caller's voice signal. As a data processing step, the voice data is recorded in digital format. The output is the recorded voice data.

[0628] Step 4:

[0629] The server converts the recorded audio data into text. The input is the output from step 3 (recorded data). As a data operation, a speech recognition API (e.g., Google Cloud Speech-to-Text) is used to convert the audio data into text data. The output is the transcribed message.

[0630] Step 5:

[0631] The server notifies the user's mobile device of the text-based message. The input is the output (text message) from step 4. As data processing, the text message is sent in the form of a push notification or email. The output is a flag indicating that the notification has been successfully sent to the user's device.

[0632] Step 6:

[0633] The user terminal receives text messages notified from the server and displays them on the screen. The input is the notification signal from the server. As part of data processing, the notification is analyzed and the content to be displayed is extracted. The output is the text message displayed on the screen.

[0634] Step 7:

[0635] The user checks the text message displayed on the device and takes action as needed. The input is the output of step 6 (the text message displayed on the screen). The user reads the displayed content and takes appropriate action based on it. The output is the user's response.

[0636] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0637] This invention provides a next-generation communication system that combines a conventional answering machine system and call waiting function with an emotion engine, enabling the system to understand not only the content of the user's voice message but also their emotional state. The system operates through the coordinated efforts of the server, terminal, and user elements.

[0638] System Configuration

[0639] 1. Server

[0640] It detects incoming calls and switches to voicemail mode and call waiting mode.

[0641] The system records the caller's voice message and utilizes a speech recognition API to convert the audio data into text.

[0642] The system uses an emotion engine to recognize and analyze emotions from recorded audio data.

[0643] The converted text data and sentiment analysis results are sent to the user's device.

[0644] 2. Terminal

[0645] Displays notifications for incoming calls and call waiting.

[0646] Receives text messages and sentiment analysis results sent from the server and displays them to the user.

[0647] 3. User

[0648] Check the incoming call and call waiting notifications on your device's screen.

[0649] Receive and review text messages and sentiment analysis results sent from the server.

[0650] Program processing

[0651] 1. Incoming call detection

[0652] Terminal: Detects an incoming call and displays a notification on the screen. If the user does not respond, the terminal sends a no-response signal to the server.

[0653] Server: If no response is received within a certain period of time, it will automatically switch to voicemail mode.

[0654] 2. Switch to voicemail mode

[0655] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[0656] Server: Records the caller's voice message.

[0657] 3. Recording and converting voice messages

[0658] Server: Stores recorded voice messages as digital data.

[0659] Server: Uses a speech recognition API to convert audio data into text format.

[0660] 4. Recognition and Analysis of Emotions

[0661] Server: Inputs recorded audio data into the emotion engine to recognize the speaker's emotions.

[0662] Server: Stores emotional data analyzed by the emotion engine.

[0663] 5. Delivery of text messages and sentiment analysis results

[0664] Server: Sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[0665] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[0666] Specific example

[0667] Examples of how to handle voicemails:

[0668] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." Simultaneously, it uses an emotion engine to recognize the emotion "nervousness" from the caller's voice. The server sends the transcribed message and the emotion analysis results to User A's device via push notification, and the message and the "nervousness" notification are displayed on User A's device.

[0669] Examples of how to handle call waiting:

[0670] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts User B to leave a message for the new caller (family member). The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records this audio and uses a speech recognition API to transcribe it into text. Simultaneously, it uses an emotion engine to recognize the emotion "anxiety" from the caller's voice. The server sends the transcribed message and the emotion analysis results as a push notification to User B's device, displaying the message and the "anxiety" notification on User B's device.

[0671] This invention allows users to quickly check voicemail and call waiting messages, and also understand the caller's emotions, enabling them to respond more quickly and appropriately.

[0672] The following describes the processing flow.

[0673] Handling voicemails

[0674] Step 1:

[0675] Device: Detects incoming calls and displays a notification on the screen.

[0676] User: I don't answer incoming calls.

[0677] Step 2:

[0678] Server: If no response is received within a certain period of time (e.g., 30 seconds), it will automatically switch to voicemail mode.

[0679] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[0680] Step 3:

[0681] Server: Records the caller's voice message.

[0682] Server: Stores recorded voice messages as digital data.

[0683] Step 4:

[0684] Server: Uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert recorded audio data into text format.

[0685] Step 5:

[0686] Server: Uses an emotion engine to recognize and analyze the emotions of the caller from recorded audio data.

[0687] Server: Stores the sentiment data analyzed by the sentiment engine.

[0688] Step 6:

[0689] Server: Sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[0690] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[0691] Call waiting

[0692] Step 1:

[0693] Terminal: Detects when a new call is coming in while the user is on a call.

[0694] Terminal: Notifies the user that a call is in progress.

[0695] User: I am currently on another call and cannot answer any new incoming calls.

[0696] Step 2:

[0697] Server: Automatically notifies new callers that the server is currently on another call and will switch them to call waiting mode.

[0698] Step 3:

[0699] Server: Records a voice message from a new caller.

[0700] Server: Stores recorded voice messages as digital data.

[0701] Step 4:

[0702] Server: Uses a speech recognition API to convert recorded audio data into text format.

[0703] Step 5:

[0704] Server: Uses an emotion engine to recognize and analyze the emotions of new callers from recorded audio data.

[0705] Server: Stores the sentiment data analyzed by the sentiment engine.

[0706] Step 6:

[0707] Server: Sends the transcribed message and sentiment analysis results to the user's device via push notification or email.

[0708] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[0709] (Example 2)

[0710] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0711] Conventional voicemail systems and call waiting functions can record and transcribe caller voice messages, but they cannot capture the caller's emotional state. Therefore, when users check the message content, they may not be able to understand the caller's feelings, making it difficult to respond appropriately. This invention aims to solve these problems and provide a next-generation communication system that not only allows users to quickly check voicemail and call waiting messages, but also allows them to understand the caller's emotions.

[0712] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0713] In this invention, the server includes means for detecting an incoming call, means for switching to answering machine mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for recognizing and analyzing emotions from the voice data, and means for notifying the user's terminal of the converted text data and the emotion analysis results. This makes it possible for the user not only to check the content of the voice message but also to understand the caller's emotional state.

[0714] "Means for detecting incoming calls" refers to a device or software that allows a communication terminal to receive an external telephone signal and recognize that the signal is an incoming call.

[0715] "Means of switching to answering machine mode when there is no response within a certain period of time" refers to a device or software that automatically switches a communication terminal to message recording mode if there is no response from the user within a certain period of time after an incoming call.

[0716] "Means for recording a caller's voice message" refers to a device or software for recording a voice message left by the person on the other end of a call as digital data.

[0717] "Means for converting recorded audio data into text" refers to a device or software that utilizes speech recognition technology to analyze recorded audio data and convert it into corresponding text data.

[0718] "Means for recognizing and analyzing emotions from audio data" refers to a device or software that analyzes audio data to recognize the emotions of the speaker contained within it, and further analyzes those emotions.

[0719] "Means for notifying the user's terminal of converted text data and sentiment analysis results" refers to a device or software that transmits text data generated from speech and the results of sentiment analysis to the user's communication terminal via push notifications, email, or the like.

[0720] "Means for detecting incoming calls while a call is in progress" refers to a device or software that notifies a user of a new incoming call, even if the user is already on another call.

[0721] "Means of switching to call waiting mode when the user is unable to answer" refers to a device or software that automatically switches to message recording mode when a new incoming call occurs and the user is unable to answer it.

[0722] "Means for recording a new caller's voice message" refers to a device or software that records a voice message from a newly called party as digital data when the phone switches to call waiting mode.

[0723] "Methods for using speech recognition APIs to convert audio data to text" refers to devices or software that use external speech recognition services to perform analysis when converting audio data to text data.

[0724] "Means of utilizing an emotion engine for recognizing and analyzing emotions from voice data" refers to a device or software that uses an external emotion analysis service to recognize emotions based on voice data and analyze the results.

[0725] "Means for sending text messages and sentiment analysis results to the user via push notification or email" refers to a device or software that sends converted text data and sentiment analysis results to the user's communication terminal via email or push notification.

[0726] This invention is a next-generation communication system that can understand not only the content of a user's voice message but also their emotional state. This system operates through the coordinated efforts of the server, terminal, and user.

[0727] System Configuration

[0728] 1. Server

[0729] The server detects incoming calls and switches to voicemail mode and call waiting mode. When an incoming call occurs, the server checks if there is no response within a certain period of time and automatically switches to voicemail mode.

[0730] The server records the caller's voice message and saves the audio data to cloud storage. Then, it uses a speech recognition API (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech API) to convert the audio data into text format.

[0731] The server inputs the converted text data into a sentiment engine (e.g., IBM Watson Tone Analyzer, Affectiva SDK) to recognize and analyze the sender's emotions.

[0732] The server sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[0733] 2. Terminal

[0734] The device displays notifications for incoming calls and call waiting. If the user does not answer, the device sends a "no answer" signal to the server.

[0735] The system receives text messages and sentiment analysis results sent from the server and displays them to the user on the screen.

[0736] 3. User

[0737] The user checks incoming calls and call waiting notifications on their device screen.

[0738] Receive and review text messages and sentiment analysis results sent from the server.

[0739] Specific examples of the system

[0740] Examples of how to handle voicemails:

[0741] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." Simultaneously, it uses an emotion engine to recognize the emotion "tension" from the caller's voice. The server sends the transcribed message and the emotion analysis results as a push notification to User A's device, and the message and the "tension" notification are displayed on User A's device.

[0742] Examples of how to handle call waiting:

[0743] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records this audio and uses a speech recognition API to transcribe it into text. Simultaneously, it uses an emotion engine to recognize the emotion "anxiety" from the caller's voice. The server sends the transcribed message and the emotion analysis results as push notifications to User B's device, and the message and the "anxiety" notification are displayed on User B's device.

[0744] Example of a prompt

[0745] Entering the following prompt statements into the generated AI model will generate specific scenarios for this system.

[0746] "The system records the user's voice message and converts it to text using a speech recognition API. Then, it uses an emotion engine to analyze the caller's emotions and notifies the user of the results. As a concrete example, imagine a scenario where user A does not answer a call and the server switches to voicemail mode. Please describe the process in this scenario."

[0747] This system allows users to quickly check voicemail and call waiting messages, and also understand the caller's emotions, enabling them to respond more appropriately.

[0748] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0749] Step 1: Incoming Call Detection

[0750] Terminal: The terminal receives incoming signals from the communication network. The input is the incoming signal, and the output is the incoming notification. The terminal displays a notification on the screen saying "You have a new incoming call" and waits for the user to respond. If the user does not respond, it sends a "no response" signal to the server after a certain period of time (e.g., 30 seconds).

[0751] Specific actions: The device's speaker sounds, and an incoming call notification is displayed on the screen.

[0752] Step 2: Switch to home security mode

[0753] Server: The server receives a "no response" signal from the terminal and switches to answering machine mode. The input is the "no response" signal, and the output is a notification that answering machine mode has started. The server then notifies the caller with an automated voice message saying, "Your phone has been switched to answering machine mode. Please leave a message."

[0754] Specific action: The server plays an automated voice message prompting the caller to record a message.

[0755] Step 3: Record and convert voice messages

[0756] Server: The server records the caller's voice message and saves it as audio data to cloud storage. Next, it sends the recorded audio data to a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert it into text format. The input is the recorded audio data, and the output is text data.

[0757] Specific operation: The server generates an audio data file and sends it to a speech recognition API for text conversion.

[0758] Step 4: Recognition and Analysis of Emotions

[0759] Server: The server inputs text data obtained from the speech recognition API and the original audio data into an emotion engine (e.g., IBM Watson Tone Analyzer) to recognize and analyze the speaker's emotions. The analysis results are stored in a database. The input is audio data and text data, and the output is the emotion analysis result.

[0760] Specific operation: The server inputs data into the emotion engine and retrieves emotion labels such as "tension," "joy," and "anger," along with their intensity.

[0761] Step 5: Delivery of text messages and sentiment analysis results

[0762] Server: The server sends text data and sentiment analysis results to the user's device via push notification or email. The input is text data and sentiment analysis results, and the output is a notification to the user's device.

[0763] Specific operation: The server uses the notification API to generate a push notification and sends it to the user's device saying, "Tomorrow's meeting has been changed to 2 PM. Emotion: Nervous."

[0764] Terminal: The terminal displays received text messages and sentiment analysis results on its screen. Input is notification data, and output is the screen display.

[0765] Specific action: The device displays the following message on the screen: "New voicemail: Tomorrow's meeting has been changed to 2 PM. Emotion: Nervous."

[0766] In this way, the system performs a series of processes that allow the user not only to confirm the content of the voice message but also to understand the emotions of the caller.

[0767] (Application Example 2)

[0768] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0769] Traditional communication systems could convert voicemail and call waiting messages into text, but they could not capture the caller's emotional state. Therefore, it was difficult for users to quickly and accurately assess not only the message content but also the caller's emotional state and take appropriate action. Furthermore, there was no means to analyze user emotions during high-risk transactions or logins to enhance security.

[0770] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for detecting incoming calls, means for switching to answering machine mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for analyzing emotions from the recorded voice data, means for notifying the user's terminal of the emotion analysis results, and means for notifying the user's terminal of the converted text data and the emotion analysis results. As a result, the user can understand not only the content of the message but also the emotions of the caller, enabling a quicker and more appropriate response. Furthermore, by analyzing the user's emotional state during high-risk transactions or logins, security can be enhanced.

[0771] "Incoming call" means receiving a phone call.

[0772] "Answering mode" is a mode that records a voice message from the caller if there is no response within a certain period of time.

[0773] A "voice message" refers to a message that is conveyed through voice.

[0774] "Recording" means saving sound as digital data.

[0775] "Audio data" refers to the digital representation of recorded sound.

[0776] Converting to "text" is the process of converting audio data into written characters.

[0777] "Notification" refers to sending and displaying information on a user's device.

[0778] "Sentiment analysis" is the process of recognizing and evaluating the emotional state of the caller from audio data.

[0779] An "emotion engine" is a technology for analyzing and recognizing emotions.

[0780] A "device" is a device that the user directly operates. Specific examples include smartphones and smart glasses.

[0781] "Push notifications" are a method of sending information to a user's device in real time.

[0782] "Security" refers to the means of protecting systems and users from unauthorized access and high-risk transactions.

[0783] This invention combines an emotion engine with conventional communication systems to achieve more advanced message communication and enhanced security. The specific configuration and operation of the system are described below.

[0784] System Configuration

[0785] The system primarily consists of server, terminal, and user elements.

[0786] server

[0787] The server is responsible for the following functions:

[0788] 1. Incoming Call Detection: The server detects incoming calls and switches to voicemail mode if there is no response within a certain period of time.

[0789] 2. Recording voice messages: Record voice messages left by the caller as digital data.

[0790] 3. Converting audio data to text: Convert recorded audio data to text using a speech recognition API (e.g., Google Speech-to-Text API).

[0791] 4. Emotional Analysis: The converted audio data is input into an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotions.

[0792] 5. Notification of Results: The text message and sentiment analysis results will be sent to the user's device via push notification or email.

[0793] terminal

[0794] The device is responsible for the following functions:

[0795] 1. Displaying notifications: Receive text messages and sentiment analysis results sent from the server and display them to the user.

[0796] 2. Incoming call and call waiting notifications: Receive and display notifications for incoming calls and call waiting.

[0797] User

[0798] The user performs the following actions:

[0799] 1. Check the message: Check the text message and sentiment analysis results on your device screen.

[0800] 2. Appropriate response: Take necessary actions promptly based on the message and emotional state.

[0801] Hardware and software

[0802] The hardware and software used to implement this system are as follows:

[0803] Hardware: Smartphones, smart glasses

[0804] Speech recognition API: Example: Google Speech-to-Text API

[0805] Emotion engine: Example: IBM Watson Tone Analyzer

[0806] Specific example

[0807] Examples of home security features

[0808] User A receives a call on their smartphone, and since there is no answer within a certain time, it switches to voicemail mode. The caller leaves a message saying, "An important meeting will be delayed until 2 PM." The server records this message and uses the Google Speech-to-Text API to transcribe it as "An important meeting will be delayed until 2 PM." Simultaneously, IBM Watson Tone Analyzer analyzes the sentiment and determines it to be "confused." The server pushes this text message and the sentiment analysis results to User A's smartphone, allowing User A to review the message and their emotional state.

[0809] Examples of security enhancements

[0810] User B is attempting a large transaction via internet banking. The server records User B's voice commands and converts them to text using the Google Speech-to-Text API. Simultaneously, it analyzes User B's emotions using IBM Watson Tone Analyzer and detects "anxiety." Based on these results, the server puts the transaction on hold and sends a confirmation notification to User B's smartphone.

[0811] Example of a prompt

[0812] Examples of prompts to input into a generative AI model are as follows:

[0813] Please input the audio data and perform sentiment analysis. If the results show an anomaly (abnormality), please issue an alert.

[0814] In this way, this invention enables users to understand not only the content of voice messages but also the emotional state of the caller, and further contributes to enhanced security.

[0815] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0816] Step 1:

[0817] The user's device detects an incoming call. The device receives the incoming call signal and notifies the user. If the user does not respond within a certain time, the device sends a no-response signal to the server. The input is the incoming call signal, and the output is the no-response signal.

[0818] Step 2:

[0819] The server receives a no-response signal from the terminal and switches to answering machine mode. At this point, the server automatically notifies the caller that the device has entered answering machine mode and records the caller's message. The input is the no-response signal, and the output is the recorded voice data.

[0820] Step 3:

[0821] The server stores the recorded audio data and uses the Google Speech-to-Text API to convert the audio data to text. The server inputs the audio data into the API and retrieves the text data. In this step, the input is the audio data and the output is the transcribed data.

[0822] Step 4:

[0823] The server performs sentiment analysis on the transcribed audio data using IBM Watson Tone Analyzer. The text data is input to the sentiment analysis engine, and the sentiment analysis results are obtained. The input is text data, and the output is the sentiment analysis results.

[0824] Step 5:

[0825] The server sends the converted text data and sentiment analysis results to the user's device via push notification or email. Here, the server formats the text data and sentiment analysis results and sends them to the device. The input is the text data and sentiment analysis results, and the output is the notification sent to the user's device.

[0826] Step 6:

[0827] The user's device receives notifications sent from the server and displays the text message and sentiment analysis results on the screen. Here, the device parses the notification to generate data to display to the user and displays it on the screen. The input is the notification from the server, and the output is the text and sentiment analysis results displayed to the user.

[0828] Step 7:

[0829] The user reviews the received text message and sentiment analysis results on the device screen. Based on this information, the user decides on the necessary actions. The input is the displayed text and sentiment analysis results, and the output is the user's actions and decisions.

[0830] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0831] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0832] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0833] [Third Embodiment]

[0834] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0835] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0836] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0837] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0838] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0839] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0840] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0841] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0842] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0843] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0844] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0845] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0846] This invention provides a next-generation answering machine system and call waiting function, enabling users to efficiently manage messages received on their answering machine or during calls. The system operates through the coordinated efforts of a server, terminal, and user.

[0847] System Configuration

[0848] 1. Server

[0849] It detects incoming calls and switches to voicemail mode and call waiting mode.

[0850] The system records the caller's voice message and utilizes a speech recognition API to convert the audio data into text.

[0851] The converted text data is sent to the user's device.

[0852] 2. Terminal

[0853] Displays notifications for incoming calls and call waiting.

[0854] Receive text messages sent from the server and display them to the user.

[0855] 3. User

[0856] Check the incoming call and call waiting notifications on your device's screen.

[0857] Receive and verify the text message sent from the server.

[0858] Program processing

[0859] 1. Incoming call detection

[0860] Terminal: When an incoming call is received, a notification is displayed on the screen. If the user does not answer, the terminal sends a no-response signal to the server.

[0861] Server: If no response is received within a certain period of time, it will automatically switch to voicemail mode.

[0862] 2. Switch to voicemail mode

[0863] Server: Notifies the caller via an automated voice message that the call has switched to voicemail mode.

[0864] Server: Records the caller's voice message.

[0865] 3. Recording and converting voice messages

[0866] Server: Stores recorded voice messages as digital data.

[0867] Server: Uses a speech recognition API to convert speech data into text.

[0868] 4. Text message delivery

[0869] Server: Sends the converted text data to the user's device via push notification or email.

[0870] Device: Receives notifications and displays text messages on the screen.

[0871] Specific example

[0872] Examples of how to handle voicemails:

[0873] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text: "Tomorrow's meeting has been changed to 2 PM." The server then sends a push notification to User A's device, and the message is displayed on User A's device.

[0874] Examples of how to handle call waiting:

[0875] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records the audio and uses a speech recognition API to transcribe "I forgot something at lunchtime, could you please bring it to me" into text. The server then sends a push notification to User B's device, and the message is displayed on User B's device.

[0876] This invention allows users to quickly check voicemail and call waiting messages as text, ensuring they receive important messages even while on a call.

[0877] The following describes the processing flow.

[0878] Handling voicemails

[0879] Step 1:

[0880] Device: Detects incoming calls and displays a notification on the screen.

[0881] User: I don't answer incoming calls.

[0882] Step 2:

[0883] Server: If there is no response within a certain period of time (e.g., 30 seconds), it will automatically switch to voicemail mode.

[0884] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[0885] Step 3:

[0886] Server: Records the caller's voice message.

[0887] Server: Stores recorded voice messages as digital data.

[0888] Step 4:

[0889] Server: Uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert recorded audio data into text format.

[0890] Step 5:

[0891] Server: Sends the converted text message to the user's device via push notification or email.

[0892] Device: Receives notifications and displays messages on the screen.

[0893] Call waiting

[0894] Step 1:

[0895] Terminal: Detects when a new call is coming in while the user is on a call.

[0896] Terminal: Notifies the user that a call is in progress.

[0897] User: I am currently on another call and cannot answer any new incoming calls.

[0898] Step 2:

[0899] Server: Automatically notifies new callers that the server is currently on another call and switches to call waiting mode.

[0900] Step 3:

[0901] Server: Records a voice message from a new caller.

[0902] Server: Stores recorded voice messages as digital data.

[0903] Step 4:

[0904] Server: Converts recorded audio data into text format using a speech recognition API.

[0905] Step 5:

[0906] Server: Sends a text-based message to the user's device via push notification or email.

[0907] Device: Receives notifications and displays text messages on the screen.

[0908] (Example 1)

[0909] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0910] Traditional voicemail systems and call waiting functions struggled to efficiently manage incoming messages, making it easy to miss important messages, especially when the user was away or on another call. Furthermore, the process of directly reviewing recorded messages was cumbersome, making quick responses difficult.

[0911] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0912] In this invention, the server includes means for detecting an incoming call, means for switching to voicemail mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for notifying the user's terminal of the converted text data, means for displaying an incoming call notification on the terminal, means for notifying the caller that voicemail mode is in effect, means for sending a text message via push notification or email, and means for displaying a text message on the terminal screen. This allows the user to quickly check important messages in text while away or on a call, enabling efficient management.

[0913] "Incoming call" refers to a state where there is a new call or notification to a communication device.

[0914] "Answering machine mode" is a feature that records incoming calls and allows users to play them back later if they are unable to answer the phone.

[0915] "Call waiting mode" is a feature that notifies you of an incoming call and allows you to record a message when a new call comes in while you are on a call.

[0916] A "voice message" is a recorded voice data left by the sender.

[0917] "Audio data" refers to data in which recorded audio is stored in digital format.

[0918] "Converting to text" refers to the process of analyzing audio data and converting it into written information.

[0919] "Push notifications" are a function that instantly sends specific information to a user's device to notify them.

[0920] A "terminal" refers to a communication device or equipment used by a user.

[0921] A "server" is a central processing unit that manages the entire system and handles data processing and transmission.

[0922] A "speech recognition API" is an application interface for converting speech data into text.

[0923] The "sender" is the person or device that initiates communication and leaves a message.

[0924] "Within a certain time period" refers to a specific time frame that has been set in advance.

[0925] "Recording" refers to the act of saving an audio message as digital data.

[0926] "To notify" refers to the act of informing a user of specific information.

[0927] "On a call" means that a communication connection is established and voice communication is taking place.

[0928] This invention provides a next-generation answering machine system and call waiting function to efficiently manage messages received by users while they are away or on a call. The system is configured in which the server, terminal, and user elements work together in coordination.

[0929] System Configuration

[0930] 1. Server

[0931] The server is the central processing unit of the system and is responsible for detecting incoming calls, switching to answering machine mode and call waiting mode, recording voice messages, converting voice data to text, and notifying users of the converted text data.

[0932] When a call comes in, the server receives the signal sent from the terminal, and if there is no response from the user within a certain period of time, it switches to voicemail mode.

[0933] 2. Terminal

[0934] A terminal is a communication device or equipment used by a user, and it displays incoming call notifications, communicates data with a server, and displays text messages based on recorded voice data.

[0935] When a call comes in to the device, a notification is displayed on the screen, and if the user does not answer, a no-response signal is sent to the server.

[0936] 3. User

[0937] The user uses their device to check incoming calls and call waiting notifications, and to receive and confirm text messages sent from the server.

[0938] Users view text messages on their device screen and respond or take action as needed.

[0939] Hardware and software to be used

[0940] Hardware:

[0941] The server will use standard server equipment.

[0942] The terminals used will be communication devices such as smartphones, tablets, and personal computers.

[0943] software:

[0944] The server uses the Google Cloud Speech-to-Text API as its speech recognition API to convert speech data into text.

[0945] The device uses dedicated applications or standard OS functions for incoming call notifications and text message display.

[0946] Specific example

[0947] Examples of how to handle voicemails:

[0948] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses the Google Cloud Speech-to-Text API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." The server then sends a push notification to User A's device, and the message is displayed on User A's device.

[0949] Examples of how to handle call waiting:

[0950] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records the audio and uses the Google Cloud Speech-to-Text API to transcribe the message into text. The server then sends a push notification to User B's device, and the message is displayed on User B's device.

[0951] Example of a prompt

[0952] Examples of prompts to input into a generative AI model:

[0953] Please explain the specific processing steps involved in recording voice data in an answering machine system, from recording to text conversion.

[0954] "Please explain, with specific examples, how the call waiting function transcribes and displays incoming messages while a call is in progress."

[0955] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0956] Step 1: Incoming Call Detection

[0957] Terminal: When a call comes in to the terminal, a notification pops up on the screen based on hardware signals (telephone line or internet calling protocol). The screen displays the caller's name and number.

[0958] Input: Incoming signal

[0959] Output: Incoming call notification pop-up display

[0960] Specific operation: The terminal analyzes the incoming signal received by the network module and displays a notification on the user interface.

[0961] Step 2: Check for response status

[0962] Terminal: If the user does not respond to an incoming call notification, a timer is started for a set period of time (e.g., 30 seconds). When the timer expires, a no-response signal is sent to the server.

[0963] Input: No response status and timer

[0964] Output: No response to the server.

[0965] Specific operation: The terminal's timer function activates for 30 seconds, and if there is no response, it sends a "no response" signal to the server via the network module.

[0966] Step 3: Switch to answering machine mode

[0967] Server: After receiving a no-response signal, the server plays an automated voice message to the caller informing them that the call has switched to voicemail mode.

[0968] Input: No response signal

[0969] Output: Voicemail mode notification to the caller

[0970] Specific operation: The server plays an automated voice message and notifies the caller via the voice transmission module that the call has switched to voicemail mode.

[0971] Step 4: Record a voice message

[0972] Server: Records the caller's voice message as digital data. Recording is limited to a certain time (e.g., 3 minutes).

[0973] Input: Caller's voice

[0974] Output: Digital audio data

[0975] Specific operation: The server's voice input module receives the caller's voice, converts it to a digital format, and records it.

[0976] Step 5: Convert speech to text

[0977] Server: Converts recorded audio data into text using a speech recognition API (e.g., Google Cloud Speech-to-Text API).

[0978] Input: Digital audio data

[0979] Output: Text data

[0980] Specific operation: The server sends digital audio data to the API, where it is parsed and converted before being received as text data.

[0981] Step 6: Sending Text Messages

[0982] Server: Sends the converted text data to the user's device via push notification or email.

[0983] Input: Text data

[0984] Output: Push notification or email

[0985] Specific operation: The server's communication module packets the text data and sends it to the terminal over the network.

[0986] Step 7: Displaying the message on the device

[0987] Device: The device displays the translated text message to the user via the device's notification center or a dedicated app. Example: "You have a new message: Tomorrow's meeting has been changed to 2 PM."

[0988] Input: Notification data from the server

[0989] Output: Text message on the screen

[0990] Specific operation: The terminal's user interface receives data from the server and displays it on the screen as a complete text message.

[0991] (Application Example 1)

[0992] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0993] Traditional answering machines and call waiting features require users to listen to incoming messages while on another call or away from their desk, making quick content review difficult. Furthermore, the lack of a function to convert voice messages to text makes it challenging for store employees, in particular, to efficiently manage messages while assisting customers. Additionally, there's a risk of missing important messages from callers while performing store duties.

[0994] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0995] In this invention, the server includes means for detecting incoming calls, means for switching to voicemail mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for notifying the user's mobile device of the converted text data, means for automatically switching to voicemail mode if a store employee is unable to answer, and means for displaying the notified text message on the user's mobile device. This enables efficient management of messages when employees are absent or busy in physical stores, and allows for quick confirmation of important messages.

[0996] "Means for detecting incoming calls" refers to a function that recognizes that an incoming call has been received on the user's terminal via a communication network.

[0997] "A means of switching to voicemail mode when there is no response within a certain period of time" refers to a function that automatically switches to voicemail mode if the user does not answer within a certain period of time after an incoming call.

[0998] "Means for recording a caller's voice message" refers to a function that records a voice message left by a caller as digital data.

[0999] "Means for converting recorded audio data into text" refers to a function that converts recorded audio messages into text data using speech recognition technology.

[1000] "Means of notifying the user of converted text data on their mobile device" refers to a function that sends the converted text message to the user's mobile device via push notification or email.

[1001] "A mechanism to automatically switch to answering machine mode when a store employee is unavailable" refers to a function that automatically switches to answering machine mode when a store employee is busy or absent.

[1002] "Means for displaying text messages notified to the user's mobile device" refers to a function that displays text messages sent to the user's mobile device on the screen.

[1003] To implement this invention, the following system is required. This system consists of a server, a user terminal, and a communication network connecting them.

[1004] server

[1005] The server has the following functions:

[1006] 1. Means for detecting incoming calls: This is a function that recognizes that an incoming call has been received on the user terminal via the communication network.

[1007] 2. Means of switching to voicemail mode when there is no response within a certain period of time: This function automatically switches to voicemail mode when the user does not answer within a certain period of time after an incoming call.

[1008] 3. Means for recording the caller's voice message: This is a function that records the voice message left by the caller as digital data.

[1009] 4. Means for converting recorded audio data to text: This function converts recorded audio messages into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text API).

[1010] 5. Means of notifying the user of the converted text data on their mobile device: This function sends the converted text message to the user's mobile device via push notification or email.

[1011] 6. A mechanism to automatically switch to answering machine mode when store employees are unavailable: This function automatically switches to answering machine mode when store employees are busy or absent.

[1012] User terminal

[1013] User devices (for example, smartphones) have the following functions:

[1014] 1. Means for displaying incoming call, voicemail, and call waiting notifications: This is a function that receives notifications sent from the server and displays them on the screen.

[1015] 2. Means for receiving and displaying text messages sent from the server: This is a function that receives text messages sent from the server and displays them visually to the user.

[1016] User actions

[1017] The user performs the following actions:

[1018] 1. Check notifications: Check incoming call and message notifications displayed on the user's device.

[1019] 2. Check text messages: Check text messages sent from the server and take action as necessary.

[1020] Specific example

[1021] For example, consider a scenario where a store employee is assisting a customer when a phone call comes in. Since the employee cannot answer the phone, the system automatically switches to voicemail mode, and the caller leaves a message. The caller's voice message, an inquiry about inventory, is recorded and converted to text using the Google Cloud Speech-to-Text API. The text message is then sent as a push notification to the store employee's smartphone, allowing them to immediately check the message.

[1022] Example of a prompt

[1023] The following is an example of a prompt statement to input into a generative AI model:

[1024] "I'd like to practice a next-generation voicemail system that converts phone voice messages written in Japanese into text. Please describe the following scenario where a call is received but not answered. Also, please include how the caller's message is converted to text and how it is notified to the user."

[1025] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1026] Step 1:

[1027] The server detects incoming calls to user terminals via the communication network. The input is the incoming signal from the communication network, and the server analyzes this signal to determine if an incoming call has occurred. The output is an internal flag indicating that an incoming call has been detected.

[1028] Step 2:

[1029] The server automatically switches to answering machine mode if the user does not respond within a certain period of time. The inputs are the output from step 1 (incoming call detection flag) and the user's response status (no response). As data processing, a timer is started, and if there is no response within a certain period of time, the answering machine mode flag is set. The output is the signal to switch to answering machine mode.

[1030] Step 3:

[1031] After switching to answering machine mode, the server records the voice message left by the caller. The inputs are the output from step 2 (answering machine mode signal) and the caller's voice signal. As a data processing step, the voice data is recorded in digital format. The output is the recorded voice data.

[1032] Step 4:

[1033] The server converts the recorded audio data into text. The input is the output from step 3 (recorded data). As a data operation, a speech recognition API (e.g., Google Cloud Speech-to-Text) is used to convert the audio data into text data. The output is the transcribed message.

[1034] Step 5:

[1035] The server notifies the user's mobile device of the text-based message. The input is the output (text message) from step 4. As data processing, the text message is sent in the form of a push notification or email. The output is a flag indicating that the notification has been successfully sent to the user's device.

[1036] Step 6:

[1037] The user terminal receives text messages notified from the server and displays them on the screen. The input is the notification signal from the server. As part of data processing, the notification is analyzed and the content to be displayed is extracted. The output is the text message displayed on the screen.

[1038] Step 7:

[1039] The user checks the text message displayed on the device and takes action as needed. The input is the output of step 6 (the text message displayed on the screen). The user reads the displayed content and takes appropriate action based on it. The output is the user's response.

[1040] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1041] This invention provides a next-generation communication system that combines a conventional answering machine system and call waiting function with an emotion engine, enabling the system to understand not only the content of the user's voice message but also their emotional state. The system operates through the coordinated efforts of the server, terminal, and user elements.

[1042] System Configuration

[1043] 1. Server

[1044] It detects incoming calls and switches to voicemail mode and call waiting mode.

[1045] The system records the caller's voice message and utilizes a speech recognition API to convert the audio data into text.

[1046] The system uses an emotion engine to recognize and analyze emotions from recorded audio data.

[1047] The converted text data and sentiment analysis results are sent to the user's device.

[1048] 2. Terminal

[1049] Displays notifications for incoming calls and call waiting.

[1050] Receives text messages and sentiment analysis results sent from the server and displays them to the user.

[1051] 3. User

[1052] Check the incoming call and call waiting notifications on your device's screen.

[1053] Receive and review text messages and sentiment analysis results sent from the server.

[1054] Program processing

[1055] 1. Incoming call detection

[1056] Terminal: Detects an incoming call and displays a notification on the screen. If the user does not respond, the terminal sends a no-response signal to the server.

[1057] Server: If no response is received within a certain period of time, it will automatically switch to voicemail mode.

[1058] 2. Switch to voicemail mode

[1059] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[1060] Server: Records the caller's voice message.

[1061] 3. Recording and converting voice messages

[1062] Server: Stores recorded voice messages as digital data.

[1063] Server: Uses a speech recognition API to convert audio data into text format.

[1064] 4. Recognition and Analysis of Emotions

[1065] Server: Inputs recorded audio data into the emotion engine to recognize the speaker's emotions.

[1066] Server: Stores emotional data analyzed by the emotion engine.

[1067] 5. Delivery of text messages and sentiment analysis results

[1068] Server: Sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[1069] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[1070] Specific example

[1071] Examples of how to handle voicemails:

[1072] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." Simultaneously, it uses an emotion engine to recognize the emotion "nervousness" from the caller's voice. The server sends the transcribed message and the emotion analysis results to User A's device via push notification, and the message and the "nervousness" notification are displayed on User A's device.

[1073] Examples of how to handle call waiting:

[1074] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts User B to leave a message for the new caller (family member). The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records this audio and uses a speech recognition API to transcribe it into text. Simultaneously, it uses an emotion engine to recognize the emotion "anxiety" from the caller's voice. The server sends the transcribed message and the emotion analysis results as a push notification to User B's device, displaying the message and the "anxiety" notification on User B's device.

[1075] This invention allows users to quickly check voicemail and call waiting messages, and also understand the caller's emotions, enabling them to respond more quickly and appropriately.

[1076] The following describes the processing flow.

[1077] Handling voicemails

[1078] Step 1:

[1079] Device: Detects incoming calls and displays a notification on the screen.

[1080] User: I don't answer incoming calls.

[1081] Step 2:

[1082] Server: If no response is received within a certain period of time (e.g., 30 seconds), it will automatically switch to voicemail mode.

[1083] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[1084] Step 3:

[1085] Server: Records the caller's voice message.

[1086] Server: Stores recorded voice messages as digital data.

[1087] Step 4:

[1088] Server: Uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert recorded audio data into text format.

[1089] Step 5:

[1090] Server: Uses an emotion engine to recognize and analyze the emotions of the caller from recorded audio data.

[1091] Server: Stores the sentiment data analyzed by the sentiment engine.

[1092] Step 6:

[1093] Server: Sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[1094] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[1095] Call waiting

[1096] Step 1:

[1097] Terminal: Detects when a new call is coming in while the user is on a call.

[1098] Terminal: Notifies the user that a call is in progress.

[1099] User: I am currently on another call and cannot answer any new incoming calls.

[1100] Step 2:

[1101] Server: Automatically notifies new callers that the server is currently on another call and will switch them to call waiting mode.

[1102] Step 3:

[1103] Server: Records a voice message from a new caller.

[1104] Server: Stores recorded voice messages as digital data.

[1105] Step 4:

[1106] Server: Uses a speech recognition API to convert recorded audio data into text format.

[1107] Step 5:

[1108] Server: Uses an emotion engine to recognize and analyze the emotions of new callers from recorded audio data.

[1109] Server: Stores the sentiment data analyzed by the sentiment engine.

[1110] Step 6:

[1111] Server: Sends the transcribed message and sentiment analysis results to the user's device via push notification or email.

[1112] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[1113] (Example 2)

[1114] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1115] Conventional voicemail systems and call waiting functions can record and transcribe caller voice messages, but they cannot capture the caller's emotional state. Therefore, when users check the message content, they may not be able to understand the caller's feelings, making it difficult to respond appropriately. This invention aims to solve these problems and provide a next-generation communication system that not only allows users to quickly check voicemail and call waiting messages, but also allows them to understand the caller's emotions.

[1116] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1117] In this invention, the server includes means for detecting an incoming call, means for switching to answering machine mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for recognizing and analyzing emotions from the voice data, and means for notifying the user's terminal of the converted text data and the emotion analysis results. This makes it possible for the user not only to check the content of the voice message but also to understand the caller's emotional state.

[1118] "Means for detecting incoming calls" refers to a device or software that allows a communication terminal to receive an external telephone signal and recognize that the signal is an incoming call.

[1119] "Means of switching to answering machine mode when there is no response within a certain period of time" refers to a device or software that automatically switches a communication terminal to message recording mode if there is no response from the user within a certain period of time after an incoming call.

[1120] "Means for recording a caller's voice message" refers to a device or software for recording a voice message left by the person on the other end of a call as digital data.

[1121] "Means for converting recorded audio data into text" refers to a device or software that utilizes speech recognition technology to analyze recorded audio data and convert it into corresponding text data.

[1122] "Means for recognizing and analyzing emotions from audio data" refers to a device or software that analyzes audio data to recognize the emotions of the speaker contained within it, and further analyzes those emotions.

[1123] "Means for notifying the user's terminal of converted text data and sentiment analysis results" refers to a device or software that transmits text data generated from speech and the results of sentiment analysis to the user's communication terminal via push notifications, email, or the like.

[1124] "Means for detecting incoming calls while a call is in progress" refers to a device or software that notifies a user of a new incoming call, even if the user is already on another call.

[1125] "Means of switching to call waiting mode when the user is unable to answer" refers to a device or software that automatically switches to message recording mode when a new incoming call occurs and the user is unable to answer it.

[1126] "Means for recording a new caller's voice message" refers to a device or software that records a voice message from a newly called party as digital data when the phone switches to call waiting mode.

[1127] "Methods for using speech recognition APIs to convert audio data to text" refers to devices or software that use external speech recognition services to perform analysis when converting audio data to text data.

[1128] "Means of utilizing an emotion engine for recognizing and analyzing emotions from voice data" refers to a device or software that uses an external emotion analysis service to recognize emotions based on voice data and analyze the results.

[1129] "Means for sending text messages and sentiment analysis results to the user via push notification or email" refers to a device or software that sends converted text data and sentiment analysis results to the user's communication terminal via email or push notification.

[1130] This invention is a next-generation communication system that can understand not only the content of a user's voice message but also their emotional state. This system operates through the coordinated efforts of the server, terminal, and user.

[1131] System Configuration

[1132] 1. Server

[1133] The server detects incoming calls and switches to voicemail mode and call waiting mode. When an incoming call occurs, the server checks if there is no response within a certain period of time and automatically switches to voicemail mode.

[1134] The server records the caller's voice message and saves the audio data to cloud storage. Then, it uses a speech recognition API (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech API) to convert the audio data into text format.

[1135] The server inputs the converted text data into a sentiment engine (e.g., IBM Watson Tone Analyzer, Affectiva SDK) to recognize and analyze the sender's emotions.

[1136] The server sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[1137] 2. Terminal

[1138] The device displays notifications for incoming calls and call waiting. If the user does not answer, the device sends a "no answer" signal to the server.

[1139] The system receives text messages and sentiment analysis results sent from the server and displays them to the user on the screen.

[1140] 3. User

[1141] The user checks incoming calls and call waiting notifications on their device screen.

[1142] Receive and review text messages and sentiment analysis results sent from the server.

[1143] Specific examples of the system

[1144] Examples of how to handle voicemails:

[1145] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." Simultaneously, it uses an emotion engine to recognize the emotion "tension" from the caller's voice. The server sends the transcribed message and the emotion analysis results as a push notification to User A's device, and the message and the "tension" notification are displayed on User A's device.

[1146] Examples of how to handle call waiting:

[1147] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records this audio and uses a speech recognition API to transcribe it into text. Simultaneously, it uses an emotion engine to recognize the emotion "anxiety" from the caller's voice. The server sends the transcribed message and the emotion analysis results as push notifications to User B's device, and the message and the "anxiety" notification are displayed on User B's device.

[1148] Example of a prompt

[1149] Entering the following prompt statements into the generated AI model will generate specific scenarios for this system.

[1150] "The system records the user's voice message and converts it to text using a speech recognition API. Then, it uses an emotion engine to analyze the caller's emotions and notifies the user of the results. As a concrete example, imagine a scenario where user A does not answer a call and the server switches to voicemail mode. Please describe the process in this scenario."

[1151] This system allows users to quickly check voicemail and call waiting messages, and also understand the caller's emotions, enabling them to respond more appropriately.

[1152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1153] Step 1: Incoming Call Detection

[1154] Terminal: The terminal receives incoming signals from the communication network. The input is the incoming signal, and the output is the incoming notification. The terminal displays a notification on the screen saying "You have a new incoming call" and waits for the user to respond. If the user does not respond, it sends a "no response" signal to the server after a certain period of time (e.g., 30 seconds).

[1155] Specific actions: The device's speaker sounds, and an incoming call notification is displayed on the screen.

[1156] Step 2: Switch to home security mode

[1157] Server: The server receives a "no response" signal from the terminal and switches to answering machine mode. The input is the "no response" signal, and the output is a notification that answering machine mode has started. The server then notifies the caller with an automated voice message saying, "Your phone has been switched to answering machine mode. Please leave a message."

[1158] Specific action: The server plays an automated voice message prompting the caller to record a message.

[1159] Step 3: Record and convert voice messages

[1160] Server: The server records the caller's voice message and saves it as audio data to cloud storage. Next, it sends the recorded audio data to a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert it into text format. The input is the recorded audio data, and the output is text data.

[1161] Specific operation: The server generates an audio data file and sends it to a speech recognition API for text conversion.

[1162] Step 4: Recognition and Analysis of Emotions

[1163] Server: The server inputs text data obtained from the speech recognition API and the original audio data into an emotion engine (e.g., IBM Watson Tone Analyzer) to recognize and analyze the speaker's emotions. The analysis results are stored in a database. The input is audio data and text data, and the output is the emotion analysis result.

[1164] Specific operation: The server inputs data into the emotion engine and retrieves emotion labels such as "tension," "joy," and "anger," along with their intensity.

[1165] Step 5: Delivery of text messages and sentiment analysis results

[1166] Server: The server sends text data and sentiment analysis results to the user's device via push notification or email. The input is text data and sentiment analysis results, and the output is a notification to the user's device.

[1167] Specific operation: The server uses the notification API to generate a push notification and sends it to the user's device saying, "Tomorrow's meeting has been changed to 2 PM. Emotion: Nervous."

[1168] Terminal: The terminal displays received text messages and sentiment analysis results on its screen. Input is notification data, and output is the screen display.

[1169] Specific action: The device displays the following message on the screen: "New voicemail: Tomorrow's meeting has been changed to 2 PM. Emotion: Nervous."

[1170] In this way, the system performs a series of processes that allow the user not only to confirm the content of the voice message but also to understand the emotions of the caller.

[1171] (Application Example 2)

[1172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1173] Traditional communication systems could convert voicemail and call waiting messages into text, but they could not capture the caller's emotional state. Therefore, it was difficult for users to quickly and accurately assess not only the message content but also the caller's emotional state and take appropriate action. Furthermore, there was no means to analyze user emotions during high-risk transactions or logins to enhance security.

[1174] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for detecting incoming calls, means for switching to answering machine mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for analyzing emotions from the recorded voice data, means for notifying the user's terminal of the emotion analysis results, and means for notifying the user's terminal of the converted text data and the emotion analysis results. As a result, the user can understand not only the content of the message but also the emotions of the caller, enabling a quicker and more appropriate response. Furthermore, by analyzing the user's emotional state during high-risk transactions or logins, security can be enhanced.

[1175] "Incoming call" means receiving a phone call.

[1176] "Answering mode" is a mode that records a voice message from the caller if there is no response within a certain period of time.

[1177] A "voice message" refers to a message that is conveyed through voice.

[1178] "Recording" means saving sound as digital data.

[1179] "Audio data" refers to the digital representation of recorded sound.

[1180] Converting to "text" is the process of converting audio data into written characters.

[1181] "Notification" refers to sending and displaying information on a user's device.

[1182] "Sentiment analysis" is the process of recognizing and evaluating the emotional state of the caller from audio data.

[1183] An "emotion engine" is a technology for analyzing and recognizing emotions.

[1184] A "device" is a device that the user directly operates. Specific examples include smartphones and smart glasses.

[1185] "Push notifications" are a method of sending information to a user's device in real time.

[1186] "Security" refers to the means of protecting systems and users from unauthorized access and high-risk transactions.

[1187] This invention combines an emotion engine with conventional communication systems to achieve more advanced message communication and enhanced security. The specific configuration and operation of the system are described below.

[1188] System Configuration

[1189] The system primarily consists of server, terminal, and user elements.

[1190] server

[1191] The server is responsible for the following functions:

[1192] 1. Incoming Call Detection: The server detects incoming calls and switches to voicemail mode if there is no response within a certain period of time.

[1193] 2. Recording voice messages: Record voice messages left by the caller as digital data.

[1194] 3. Converting audio data to text: Convert recorded audio data to text using a speech recognition API (e.g., Google Speech-to-Text API).

[1195] 4. Emotional Analysis: The converted audio data is input into an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotions.

[1196] 5. Notification of Results: The text message and sentiment analysis results will be sent to the user's device via push notification or email.

[1197] terminal

[1198] The device is responsible for the following functions:

[1199] 1. Displaying notifications: Receive text messages and sentiment analysis results sent from the server and display them to the user.

[1200] 2. Incoming call and call waiting notifications: Receive and display notifications for incoming calls and call waiting.

[1201] User

[1202] The user performs the following actions:

[1203] 1. Check the message: Check the text message and sentiment analysis results on your device screen.

[1204] 2. Appropriate response: Take necessary actions promptly based on the message and emotional state.

[1205] Hardware and software

[1206] The hardware and software used to implement this system are as follows:

[1207] Hardware: Smartphones, smart glasses

[1208] Speech recognition API: Example: Google Speech-to-Text API

[1209] Emotion engine: Example: IBM Watson Tone Analyzer

[1210] Specific example

[1211] Examples of home security features

[1212] User A receives a call on their smartphone, and since there is no answer within a certain time, it switches to voicemail mode. The caller leaves a message saying, "An important meeting will be delayed until 2 PM." The server records this message and uses the Google Speech-to-Text API to transcribe it as "An important meeting will be delayed until 2 PM." Simultaneously, IBM Watson Tone Analyzer analyzes the sentiment and determines it to be "confused." The server pushes this text message and the sentiment analysis results to User A's smartphone, allowing User A to review the message and their emotional state.

[1213] Examples of security enhancements

[1214] User B is attempting a large transaction via internet banking. The server records User B's voice commands and converts them to text using the Google Speech-to-Text API. Simultaneously, it analyzes User B's emotions using IBM Watson Tone Analyzer and detects "anxiety." Based on these results, the server puts the transaction on hold and sends a confirmation notification to User B's smartphone.

[1215] Example of a prompt

[1216] Examples of prompts to input into a generative AI model are as follows:

[1217] Please input the audio data and perform sentiment analysis. If the results show an anomaly (abnormality), please issue an alert.

[1218] In this way, this invention enables users to understand not only the content of voice messages but also the emotional state of the caller, and further contributes to enhanced security.

[1219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1220] Step 1:

[1221] The user's device detects an incoming call. The device receives the incoming call signal and notifies the user. If the user does not respond within a certain time, the device sends a no-response signal to the server. The input is the incoming call signal, and the output is the no-response signal.

[1222] Step 2:

[1223] The server receives a no-response signal from the terminal and switches to answering machine mode. At this point, the server automatically notifies the caller that the device has entered answering machine mode and records the caller's message. The input is the no-response signal, and the output is the recorded voice data.

[1224] Step 3:

[1225] The server stores the recorded audio data and uses the Google Speech-to-Text API to convert the audio data to text. The server inputs the audio data into the API and retrieves the text data. In this step, the input is the audio data and the output is the transcribed data.

[1226] Step 4:

[1227] The server performs sentiment analysis on the transcribed audio data using IBM Watson Tone Analyzer. The text data is input to the sentiment analysis engine, and the sentiment analysis results are obtained. The input is text data, and the output is the sentiment analysis results.

[1228] Step 5:

[1229] The server sends the converted text data and sentiment analysis results to the user's device via push notification or email. Here, the server formats the text data and sentiment analysis results and sends them to the device. The input is the text data and sentiment analysis results, and the output is the notification sent to the user's device.

[1230] Step 6:

[1231] The user's device receives notifications sent from the server and displays the text message and sentiment analysis results on the screen. Here, the device parses the notification to generate data to display to the user and displays it on the screen. The input is the notification from the server, and the output is the text and sentiment analysis results displayed to the user.

[1232] Step 7:

[1233] The user reviews the received text message and sentiment analysis results on the device screen. Based on this information, the user decides on the necessary actions. The input is the displayed text and sentiment analysis results, and the output is the user's actions and decisions.

[1234] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1235] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1236] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1237] [Fourth Embodiment]

[1238] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1239] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1240] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1241] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1242] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1243] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1244] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1245] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1246] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1247] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1248] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1249] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1250] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1251] This invention provides a next-generation answering machine system and call waiting function, enabling users to efficiently manage messages received on their answering machine or during calls. The system operates through the coordinated efforts of a server, terminal, and user.

[1252] System Configuration

[1253] 1. Server

[1254] It detects incoming calls and switches to voicemail mode and call waiting mode.

[1255] The system records the caller's voice message and utilizes a speech recognition API to convert the audio data into text.

[1256] The converted text data is sent to the user's device.

[1257] 2. Terminal

[1258] Displays notifications for incoming calls and call waiting.

[1259] Receive text messages sent from the server and display them to the user.

[1260] 3. User

[1261] Check the incoming call and call waiting notifications on your device's screen.

[1262] Receive and verify the text message sent from the server.

[1263] Program processing

[1264] 1. Incoming call detection

[1265] Terminal: When an incoming call is received, a notification is displayed on the screen. If the user does not answer, the terminal sends a no-response signal to the server.

[1266] Server: If no response is received within a certain period of time, it will automatically switch to voicemail mode.

[1267] 2. Switch to voicemail mode

[1268] Server: Notifies the caller via an automated voice message that the call has switched to voicemail mode.

[1269] Server: Records the caller's voice message.

[1270] 3. Recording and converting voice messages

[1271] Server: Stores recorded voice messages as digital data.

[1272] Server: Uses a speech recognition API to convert speech data into text.

[1273] 4. Text message delivery

[1274] Server: Sends the converted text data to the user's device via push notification or email.

[1275] Device: Receives notifications and displays text messages on the screen.

[1276] Specific example

[1277] Examples of how to handle voicemails:

[1278] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text: "Tomorrow's meeting has been changed to 2 PM." The server then sends a push notification to User A's device, and the message is displayed on User A's device.

[1279] Examples of how to handle call waiting:

[1280] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records the audio and uses a speech recognition API to transcribe "I forgot something at lunchtime, could you please bring it to me" into text. The server then sends a push notification to User B's device, and the message is displayed on User B's device.

[1281] This invention allows users to quickly check voicemail and call waiting messages as text, ensuring they receive important messages even while on a call.

[1282] The following describes the processing flow.

[1283] Handling voicemails

[1284] Step 1:

[1285] Device: Detects incoming calls and displays a notification on the screen.

[1286] User: I don't answer incoming calls.

[1287] Step 2:

[1288] Server: If there is no response within a certain period of time (e.g., 30 seconds), it will automatically switch to voicemail mode.

[1289] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[1290] Step 3:

[1291] Server: Records the caller's voice message.

[1292] Server: Stores recorded voice messages as digital data.

[1293] Step 4:

[1294] Server: Uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert recorded audio data into text format.

[1295] Step 5:

[1296] Server: Sends the converted text message to the user's device via push notification or email.

[1297] Device: Receives notifications and displays messages on the screen.

[1298] Call waiting

[1299] Step 1:

[1300] Terminal: Detects when a new call is coming in while the user is on a call.

[1301] Terminal: Notifies the user that a call is in progress.

[1302] User: I am currently on another call and cannot answer any new incoming calls.

[1303] Step 2:

[1304] Server: Automatically notifies new callers that the server is currently on another call and switches to call waiting mode.

[1305] Step 3:

[1306] Server: Records a voice message from a new caller.

[1307] Server: Stores recorded voice messages as digital data.

[1308] Step 4:

[1309] Server: Converts recorded audio data into text format using a speech recognition API.

[1310] Step 5:

[1311] Server: Sends a text-based message to the user's device via push notification or email.

[1312] Device: Receives notifications and displays text messages on the screen.

[1313] (Example 1)

[1314] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1315] Traditional voicemail systems and call waiting functions struggled to efficiently manage incoming messages, making it easy to miss important messages, especially when the user was away or on another call. Furthermore, the process of directly reviewing recorded messages was cumbersome, making quick responses difficult.

[1316] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1317] In this invention, the server includes means for detecting an incoming call, means for switching to voicemail mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for notifying the user's terminal of the converted text data, means for displaying an incoming call notification on the terminal, means for notifying the caller that voicemail mode is in effect, means for sending a text message via push notification or email, and means for displaying a text message on the terminal screen. This allows the user to quickly check important messages in text while away or on a call, enabling efficient management.

[1318] "Incoming call" refers to a state where there is a new call or notification to a communication device.

[1319] "Answering machine mode" is a feature that records incoming calls and allows users to play them back later if they are unable to answer the phone.

[1320] "Call waiting mode" is a feature that notifies you of an incoming call and allows you to record a message when a new call comes in while you are on a call.

[1321] A "voice message" is a recorded voice data left by the sender.

[1322] "Audio data" refers to data in which recorded audio is stored in digital format.

[1323] "Converting to text" refers to the process of analyzing audio data and converting it into written information.

[1324] "Push notifications" are a function that instantly sends specific information to a user's device to notify them.

[1325] A "terminal" refers to a communication device or equipment used by a user.

[1326] A "server" is a central processing unit that manages the entire system and handles data processing and transmission.

[1327] A "speech recognition API" is an application interface for converting speech data into text.

[1328] The "sender" is the person or device that initiates communication and leaves a message.

[1329] "Within a certain time period" refers to a specific time frame that has been set in advance.

[1330] "Recording" refers to the act of saving an audio message as digital data.

[1331] "To notify" refers to the act of informing a user of specific information.

[1332] "On a call" means that a communication connection is established and voice communication is taking place.

[1333] This invention provides a next-generation answering machine system and call waiting function to efficiently manage messages received by users while they are away or on a call. The system is configured in which the server, terminal, and user elements work together in coordination.

[1334] System Configuration

[1335] 1. Server

[1336] The server is the central processing unit of the system and is responsible for detecting incoming calls, switching to answering machine mode and call waiting mode, recording voice messages, converting voice data to text, and notifying users of the converted text data.

[1337] When a call comes in, the server receives the signal sent from the terminal, and if there is no response from the user within a certain period of time, it switches to voicemail mode.

[1338] 2. Terminal

[1339] A terminal is a communication device or equipment used by a user, and it displays incoming call notifications, communicates data with a server, and displays text messages based on recorded voice data.

[1340] When a call comes in to the device, a notification is displayed on the screen, and if the user does not answer, a no-response signal is sent to the server.

[1341] 3. User

[1342] The user uses their device to check incoming calls and call waiting notifications, and to receive and confirm text messages sent from the server.

[1343] Users view text messages on their device screen and respond or take action as needed.

[1344] Hardware and software to be used

[1345] Hardware:

[1346] The server will use standard server equipment.

[1347] The terminals used will be communication devices such as smartphones, tablets, and personal computers.

[1348] software:

[1349] The server uses the Google Cloud Speech-to-Text API as its speech recognition API to convert speech data into text.

[1350] The device uses dedicated applications or standard OS functions for incoming call notifications and text message display.

[1351] Specific example

[1352] Examples of how to handle voicemails:

[1353] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses the Google Cloud Speech-to-Text API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." The server then sends a push notification to User A's device, and the message is displayed on User A's device.

[1354] Examples of how to handle call waiting:

[1355] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records the audio and uses the Google Cloud Speech-to-Text API to transcribe the message into text. The server then sends a push notification to User B's device, and the message is displayed on User B's device.

[1356] Example of a prompt

[1357] Examples of prompts to input into a generative AI model:

[1358] Please explain the specific processing steps involved in recording voice data in an answering machine system, from recording to text conversion.

[1359] "Please explain, with specific examples, how the call waiting function transcribes and displays incoming messages while a call is in progress."

[1360] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1361] Step 1: Incoming Call Detection

[1362] Terminal: When a call comes in to the terminal, a notification pops up on the screen based on hardware signals (telephone line or internet calling protocol). The screen displays the caller's name and number.

[1363] Input: Incoming signal

[1364] Output: Incoming call notification pop-up display

[1365] Specific operation: The terminal analyzes the incoming signal received by the network module and displays a notification on the user interface.

[1366] Step 2: Check for response status

[1367] Terminal: If the user does not respond to an incoming call notification, a timer is started for a set period of time (e.g., 30 seconds). When the timer expires, a no-response signal is sent to the server.

[1368] Input: No response status and timer

[1369] Output: No response to the server.

[1370] Specific operation: The terminal's timer function activates for 30 seconds, and if there is no response, it sends a "no response" signal to the server via the network module.

[1371] Step 3: Switch to answering machine mode

[1372] Server: After receiving a no-response signal, the server plays an automated voice message to the caller informing them that the call has switched to voicemail mode.

[1373] Input: No response signal

[1374] Output: Voicemail mode notification to the caller

[1375] Specific operation: The server plays an automated voice message and notifies the caller via the voice transmission module that the call has switched to voicemail mode.

[1376] Step 4: Record a voice message

[1377] Server: Records the caller's voice message as digital data. Recording is limited to a certain time (e.g., 3 minutes).

[1378] Input: Caller's voice

[1379] Output: Digital audio data

[1380] Specific operation: The server's voice input module receives the caller's voice, converts it to a digital format, and records it.

[1381] Step 5: Convert speech to text

[1382] Server: Converts recorded audio data into text using a speech recognition API (e.g., Google Cloud Speech-to-Text API).

[1383] Input: Digital audio data

[1384] Output: Text data

[1385] Specific operation: The server sends digital audio data to the API, where it is parsed and converted before being received as text data.

[1386] Step 6: Sending Text Messages

[1387] Server: Sends the converted text data to the user's device via push notification or email.

[1388] Input: Text data

[1389] Output: Push notification or email

[1390] Specific operation: The server's communication module packets the text data and sends it to the terminal over the network.

[1391] Step 7: Displaying the message on the device

[1392] Device: The device displays the translated text message to the user via the device's notification center or a dedicated app. Example: "You have a new message: Tomorrow's meeting has been changed to 2 PM."

[1393] Input: Notification data from the server

[1394] Output: Text message on the screen

[1395] Specific operation: The terminal's user interface receives data from the server and displays it on the screen as a complete text message.

[1396] (Application Example 1)

[1397] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1398] Traditional answering machines and call waiting features require users to listen to incoming messages while on another call or away from their desk, making quick content review difficult. Furthermore, the lack of a function to convert voice messages to text makes it challenging for store employees, in particular, to efficiently manage messages while assisting customers. Additionally, there's a risk of missing important messages from callers while performing store duties.

[1399] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1400] In this invention, the server includes means for detecting incoming calls, means for switching to voicemail mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for notifying the user's mobile device of the converted text data, means for automatically switching to voicemail mode if a store employee is unable to answer, and means for displaying the notified text message on the user's mobile device. This enables efficient management of messages when employees are absent or busy in physical stores, and allows for quick confirmation of important messages.

[1401] "Means for detecting incoming calls" refers to a function that recognizes that an incoming call has been received on the user's terminal via a communication network.

[1402] "A means of switching to voicemail mode when there is no response within a certain period of time" refers to a function that automatically switches to voicemail mode if the user does not answer within a certain period of time after an incoming call.

[1403] "Means for recording a caller's voice message" refers to a function that records a voice message left by a caller as digital data.

[1404] "Means for converting recorded audio data into text" refers to a function that converts recorded audio messages into text data using speech recognition technology.

[1405] "Means of notifying the user of converted text data on their mobile device" refers to a function that sends the converted text message to the user's mobile device via push notification or email.

[1406] "A mechanism to automatically switch to answering machine mode when a store employee is unavailable" refers to a function that automatically switches to answering machine mode when a store employee is busy or absent.

[1407] "Means for displaying text messages notified to the user's mobile device" refers to a function that displays text messages sent to the user's mobile device on the screen.

[1408] To implement this invention, the following system is required. This system consists of a server, a user terminal, and a communication network connecting them.

[1409] server

[1410] The server has the following functions:

[1411] 1. Means for detecting incoming calls: This is a function that recognizes that an incoming call has been received on the user terminal via the communication network.

[1412] 2. Means of switching to voicemail mode when there is no response within a certain period of time: This function automatically switches to voicemail mode when the user does not answer within a certain period of time after an incoming call.

[1413] 3. Means for recording the caller's voice message: This is a function that records the voice message left by the caller as digital data.

[1414] 4. Means for converting recorded audio data to text: This function converts recorded audio messages into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text API).

[1415] 5. Means of notifying the user of the converted text data on their mobile device: This function sends the converted text message to the user's mobile device via push notification or email.

[1416] 6. A mechanism to automatically switch to answering machine mode when store employees are unavailable: This function automatically switches to answering machine mode when store employees are busy or absent.

[1417] User terminal

[1418] User devices (for example, smartphones) have the following functions:

[1419] 1. Means for displaying incoming call, voicemail, and call waiting notifications: This is a function that receives notifications sent from the server and displays them on the screen.

[1420] 2. Means for receiving and displaying text messages sent from the server: This is a function that receives text messages sent from the server and displays them visually to the user.

[1421] User actions

[1422] The user performs the following actions:

[1423] 1. Check notifications: Check incoming call and message notifications displayed on the user's device.

[1424] 2. Check text messages: Check text messages sent from the server and take action as necessary.

[1425] Specific example

[1426] For example, consider a scenario where a store employee is assisting a customer when a phone call comes in. Since the employee cannot answer the phone, the system automatically switches to voicemail mode, and the caller leaves a message. The caller's voice message, an inquiry about inventory, is recorded and converted to text using the Google Cloud Speech-to-Text API. The text message is then sent as a push notification to the store employee's smartphone, allowing them to immediately check the message.

[1427] Example of a prompt

[1428] The following is an example of a prompt statement to input into a generative AI model:

[1429] "I'd like to practice a next-generation voicemail system that converts phone voice messages written in Japanese into text. Please describe the following scenario where a call is received but not answered. Also, please include how the caller's message is converted to text and how it is notified to the user."

[1430] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1431] Step 1:

[1432] The server detects incoming calls to user terminals via the communication network. The input is the incoming signal from the communication network, and the server analyzes this signal to determine if an incoming call has occurred. The output is an internal flag indicating that an incoming call has been detected.

[1433] Step 2:

[1434] The server automatically switches to answering machine mode if the user does not respond within a certain period of time. The inputs are the output from step 1 (incoming call detection flag) and the user's response status (no response). As data processing, a timer is started, and if there is no response within a certain period of time, the answering machine mode flag is set. The output is the signal to switch to answering machine mode.

[1435] Step 3:

[1436] After switching to answering machine mode, the server records the voice message left by the caller. The inputs are the output from step 2 (answering machine mode signal) and the caller's voice signal. As a data processing step, the voice data is recorded in digital format. The output is the recorded voice data.

[1437] Step 4:

[1438] The server converts the recorded audio data into text. The input is the output from step 3 (recorded data). As a data operation, a speech recognition API (e.g., Google Cloud Speech-to-Text) is used to convert the audio data into text data. The output is the transcribed message.

[1439] Step 5:

[1440] The server notifies the user's mobile device of the text-based message. The input is the output (text message) from step 4. As data processing, the text message is sent in the form of a push notification or email. The output is a flag indicating that the notification has been successfully sent to the user's device.

[1441] Step 6:

[1442] The user terminal receives text messages notified from the server and displays them on the screen. The input is the notification signal from the server. As part of data processing, the notification is analyzed and the content to be displayed is extracted. The output is the text message displayed on the screen.

[1443] Step 7:

[1444] The user checks the text message displayed on the device and takes action as needed. The input is the output of step 6 (the text message displayed on the screen). The user reads the displayed content and takes appropriate action based on it. The output is the user's response.

[1445] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1446] This invention provides a next-generation communication system that combines a conventional answering machine system and call waiting function with an emotion engine, enabling the system to understand not only the content of the user's voice message but also their emotional state. The system operates through the coordinated efforts of the server, terminal, and user elements.

[1447] System Configuration

[1448] 1. Server

[1449] It detects incoming calls and switches to voicemail mode and call waiting mode.

[1450] The system records the caller's voice message and utilizes a speech recognition API to convert the audio data into text.

[1451] The system uses an emotion engine to recognize and analyze emotions from recorded audio data.

[1452] The converted text data and sentiment analysis results are sent to the user's device.

[1453] 2. Terminal

[1454] Displays notifications for incoming calls and call waiting.

[1455] Receives text messages and sentiment analysis results sent from the server and displays them to the user.

[1456] 3. User

[1457] Check the incoming call and call waiting notifications on your device's screen.

[1458] Receive and review text messages and sentiment analysis results sent from the server.

[1459] Program processing

[1460] 1. Incoming call detection

[1461] Terminal: Detects an incoming call and displays a notification on the screen. If the user does not respond, the terminal sends a no-response signal to the server.

[1462] Server: If no response is received within a certain period of time, it will automatically switch to voicemail mode.

[1463] 2. Switch to voicemail mode

[1464] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[1465] Server: Records the caller's voice message.

[1466] 3. Recording and converting voice messages

[1467] Server: Stores recorded voice messages as digital data.

[1468] Server: Uses a speech recognition API to convert audio data into text format.

[1469] 4. Recognition and Analysis of Emotions

[1470] Server: Inputs recorded audio data into the emotion engine to recognize the speaker's emotions.

[1471] Server: Stores emotional data analyzed by the emotion engine.

[1472] 5. Delivery of text messages and sentiment analysis results

[1473] Server: Sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[1474] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[1475] Specific example

[1476] Examples of how to handle voicemails:

[1477] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." Simultaneously, it uses an emotion engine to recognize the emotion "nervousness" from the caller's voice. The server sends the transcribed message and the emotion analysis results to User A's device via push notification, and the message and the "nervousness" notification are displayed on User A's device.

[1478] Examples of how to handle call waiting:

[1479] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts User B to leave a message for the new caller (family member). The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records this audio and uses a speech recognition API to transcribe it into text. Simultaneously, it uses an emotion engine to recognize the emotion "anxiety" from the caller's voice. The server sends the transcribed message and the emotion analysis results as a push notification to User B's device, displaying the message and the "anxiety" notification on User B's device.

[1480] This invention allows users to quickly check voicemail and call waiting messages, and also understand the caller's emotions, enabling them to respond more quickly and appropriately.

[1481] The following describes the processing flow.

[1482] Handling voicemails

[1483] Step 1:

[1484] Device: Detects incoming calls and displays a notification on the screen.

[1485] User: I don't answer incoming calls.

[1486] Step 2:

[1487] Server: If no response is received within a certain period of time (e.g., 30 seconds), it will automatically switch to voicemail mode.

[1488] Server: The server will notify the caller via automated voice message that the call has switched to voicemail mode.

[1489] Step 3:

[1490] Server: Records the caller's voice message.

[1491] Server: Stores recorded voice messages as digital data.

[1492] Step 4:

[1493] Server: Uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert recorded audio data into text format.

[1494] Step 5:

[1495] Server: Uses an emotion engine to recognize and analyze the emotions of the caller from recorded audio data.

[1496] Server: Stores the sentiment data analyzed by the sentiment engine.

[1497] Step 6:

[1498] Server: Sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[1499] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[1500] Call waiting

[1501] Step 1:

[1502] Terminal: Detects when a new call is coming in while the user is on a call.

[1503] Terminal: Notifies the user that a call is in progress.

[1504] User: I am currently on another call and cannot answer any new incoming calls.

[1505] Step 2:

[1506] Server: Automatically notifies new callers that the server is currently on another call and will switch them to call waiting mode.

[1507] Step 3:

[1508] Server: Records a voice message from a new caller.

[1509] Server: Stores recorded voice messages as digital data.

[1510] Step 4:

[1511] Server: Uses a speech recognition API to convert recorded audio data into text format.

[1512] Step 5:

[1513] Server: Uses an emotion engine to recognize and analyze the emotions of new callers from recorded audio data.

[1514] Server: Stores the sentiment data analyzed by the sentiment engine.

[1515] Step 6:

[1516] Server: Sends the transcribed message and sentiment analysis results to the user's device via push notification or email.

[1517] Device: Receives notifications and displays text messages and sentiment analysis results on the screen.

[1518] (Example 2)

[1519] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1520] Conventional voicemail systems and call waiting functions can record and transcribe caller voice messages, but they cannot capture the caller's emotional state. Therefore, when users check the message content, they may not be able to understand the caller's feelings, making it difficult to respond appropriately. This invention aims to solve these problems and provide a next-generation communication system that not only allows users to quickly check voicemail and call waiting messages, but also allows them to understand the caller's emotions.

[1521] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1522] In this invention, the server includes means for detecting an incoming call, means for switching to answering machine mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for recognizing and analyzing emotions from the voice data, and means for notifying the user's terminal of the converted text data and the emotion analysis results. This makes it possible for the user not only to check the content of the voice message but also to understand the caller's emotional state.

[1523] "Means for detecting incoming calls" refers to a device or software that allows a communication terminal to receive an external telephone signal and recognize that the signal is an incoming call.

[1524] "Means of switching to answering machine mode when there is no response within a certain period of time" refers to a device or software that automatically switches a communication terminal to message recording mode if there is no response from the user within a certain period of time after an incoming call.

[1525] "Means for recording a caller's voice message" refers to a device or software for recording a voice message left by the person on the other end of a call as digital data.

[1526] "Means for converting recorded audio data into text" refers to a device or software that utilizes speech recognition technology to analyze recorded audio data and convert it into corresponding text data.

[1527] "Means for recognizing and analyzing emotions from audio data" refers to a device or software that analyzes audio data to recognize the emotions of the speaker contained within it, and further analyzes those emotions.

[1528] "Means for notifying the user's terminal of converted text data and sentiment analysis results" refers to a device or software that transmits text data generated from speech and the results of sentiment analysis to the user's communication terminal via push notifications, email, or the like.

[1529] "Means for detecting incoming calls while a call is in progress" refers to a device or software that notifies a user of a new incoming call, even if the user is already on another call.

[1530] "Means of switching to call waiting mode when the user is unable to answer" refers to a device or software that automatically switches to message recording mode when a new incoming call occurs and the user is unable to answer it.

[1531] "Means for recording a new caller's voice message" refers to a device or software that records a voice message from a newly called party as digital data when the phone switches to call waiting mode.

[1532] "Methods for using speech recognition APIs to convert audio data to text" refers to devices or software that use external speech recognition services to perform analysis when converting audio data to text data.

[1533] "Means of utilizing an emotion engine for recognizing and analyzing emotions from voice data" refers to a device or software that uses an external emotion analysis service to recognize emotions based on voice data and analyze the results.

[1534] "Means for sending text messages and sentiment analysis results to the user via push notification or email" refers to a device or software that sends converted text data and sentiment analysis results to the user's communication terminal via email or push notification.

[1535] This invention is a next-generation communication system that can understand not only the content of a user's voice message but also their emotional state. This system operates through the coordinated efforts of the server, terminal, and user.

[1536] System Configuration

[1537] 1. Server

[1538] The server detects incoming calls and switches to voicemail mode and call waiting mode. When an incoming call occurs, the server checks if there is no response within a certain period of time and automatically switches to voicemail mode.

[1539] The server records the caller's voice message and saves the audio data to cloud storage. Then, it uses a speech recognition API (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech API) to convert the audio data into text format.

[1540] The server inputs the converted text data into a sentiment engine (e.g., IBM Watson Tone Analyzer, Affectiva SDK) to recognize and analyze the sender's emotions.

[1541] The server sends the converted text data and sentiment analysis results to the user's device via push notification or email.

[1542] 2. Terminal

[1543] The device displays notifications for incoming calls and call waiting. If the user does not answer, the device sends a "no answer" signal to the server.

[1544] The system receives text messages and sentiment analysis results sent from the server and displays them to the user on the screen.

[1545] 3. User

[1546] The user checks incoming calls and call waiting notifications on their device screen.

[1547] Receive and review text messages and sentiment analysis results sent from the server.

[1548] Specific examples of the system

[1549] Examples of how to handle voicemails:

[1550] User A receives a call on their device, and since the user does not answer, the server switches to voicemail mode after 30 seconds. The caller leaves a message saying, "Tomorrow's meeting has been changed to 2 PM." The server records this audio and uses a speech recognition API to transcribe it into text, saying, "Tomorrow's meeting has been changed to 2 PM." Simultaneously, it uses an emotion engine to recognize the emotion "tension" from the caller's voice. The server sends the transcribed message and the emotion analysis results as a push notification to User A's device, and the message and the "tension" notification are displayed on User A's device.

[1551] Examples of how to handle call waiting:

[1552] User B receives a new call from a family member while on a call with a client. The server detects that User B is already on a call, switches to call waiting mode, and prompts the new caller (family member) to leave a message. The family member leaves a message saying, "I forgot something at lunchtime, could you please bring it to me?" The server records this audio and uses a speech recognition API to transcribe it into text. Simultaneously, it uses an emotion engine to recognize the emotion "anxiety" from the caller's voice. The server sends the transcribed message and the emotion analysis results as push notifications to User B's device, and the message and the "anxiety" notification are displayed on User B's device.

[1553] Example of a prompt

[1554] Entering the following prompt statements into the generated AI model will generate specific scenarios for this system.

[1555] "The system records the user's voice message and converts it to text using a speech recognition API. Then, it uses an emotion engine to analyze the caller's emotions and notifies the user of the results. As a concrete example, imagine a scenario where user A does not answer a call and the server switches to voicemail mode. Please describe the process in this scenario."

[1556] This system allows users to quickly check voicemail and call waiting messages, and also understand the caller's emotions, enabling them to respond more appropriately.

[1557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1558] Step 1: Incoming Call Detection

[1559] Terminal: The terminal receives incoming signals from the communication network. The input is the incoming signal, and the output is the incoming notification. The terminal displays a notification on the screen saying "You have a new incoming call" and waits for the user to respond. If the user does not respond, it sends a "no response" signal to the server after a certain period of time (e.g., 30 seconds).

[1560] Specific actions: The device's speaker sounds, and an incoming call notification is displayed on the screen.

[1561] Step 2: Switch to home security mode

[1562] Server: The server receives a "no response" signal from the terminal and switches to answering machine mode. The input is the "no response" signal, and the output is a notification that answering machine mode has started. The server then notifies the caller with an automated voice message saying, "Your phone has been switched to answering machine mode. Please leave a message."

[1563] Specific action: The server plays an automated voice message prompting the caller to record a message.

[1564] Step 3: Record and convert voice messages

[1565] Server: The server records the caller's voice message and saves it as audio data to cloud storage. Next, it sends the recorded audio data to a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert it into text format. The input is the recorded audio data, and the output is text data.

[1566] Specific operation: The server generates an audio data file and sends it to a speech recognition API for text conversion.

[1567] Step 4: Recognition and Analysis of Emotions

[1568] Server: The server inputs text data obtained from the speech recognition API and the original audio data into an emotion engine (e.g., IBM Watson Tone Analyzer) to recognize and analyze the speaker's emotions. The analysis results are stored in a database. The input is audio data and text data, and the output is the emotion analysis result.

[1569] Specific operation: The server inputs data into the emotion engine and retrieves emotion labels such as "tension," "joy," and "anger," along with their intensity.

[1570] Step 5: Delivery of text messages and sentiment analysis results

[1571] Server: The server sends text data and sentiment analysis results to the user's device via push notification or email. The input is text data and sentiment analysis results, and the output is a notification to the user's device.

[1572] Specific operation: The server uses the notification API to generate a push notification and sends it to the user's device saying, "Tomorrow's meeting has been changed to 2 PM. Emotion: Nervous."

[1573] Terminal: The terminal displays received text messages and sentiment analysis results on its screen. Input is notification data, and output is the screen display.

[1574] Specific action: The device displays the following message on the screen: "New voicemail: Tomorrow's meeting has been changed to 2 PM. Emotion: Nervous."

[1575] In this way, the system performs a series of processes that allow the user not only to confirm the content of the voice message but also to understand the emotions of the caller.

[1576] (Application Example 2)

[1577] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1578] Traditional communication systems could convert voicemail and call waiting messages into text, but they could not capture the caller's emotional state. Therefore, it was difficult for users to quickly and accurately assess not only the message content but also the caller's emotional state and take appropriate action. Furthermore, there was no means to analyze user emotions during high-risk transactions or logins to enhance security.

[1579] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for detecting incoming calls, means for switching to answering machine mode if there is no response within a certain period of time, means for recording the caller's voice message, means for converting the recorded voice data into text, means for analyzing emotions from the recorded voice data, means for notifying the user's terminal of the emotion analysis results, and means for notifying the user's terminal of the converted text data and the emotion analysis results. As a result, the user can understand not only the content of the message but also the emotions of the caller, enabling a quicker and more appropriate response. Furthermore, by analyzing the user's emotional state during high-risk transactions or logins, security can be enhanced.

[1580] "Incoming call" means receiving a phone call.

[1581] "Answering mode" is a mode that records a voice message from the caller if there is no response within a certain period of time.

[1582] A "voice message" refers to a message that is conveyed through voice.

[1583] "Recording" means saving sound as digital data.

[1584] "Audio data" refers to the digital representation of recorded sound.

[1585] Converting to "text" is the process of converting audio data into written characters.

[1586] "Notification" refers to sending and displaying information on a user's device.

[1587] "Sentiment analysis" is the process of recognizing and evaluating the emotional state of the caller from audio data.

[1588] An "emotion engine" is a technology for analyzing and recognizing emotions.

[1589] A "device" is a device that the user directly operates. Specific examples include smartphones and smart glasses.

[1590] "Push notifications" are a method of sending information to a user's device in real time.

[1591] "Security" refers to the means of protecting systems and users from unauthorized access and high-risk transactions.

[1592] This invention combines an emotion engine with conventional communication systems to achieve more advanced message communication and enhanced security. The specific configuration and operation of the system are described below.

[1593] System Configuration

[1594] The system primarily consists of server, terminal, and user elements.

[1595] server

[1596] The server is responsible for the following functions:

[1597] 1. Incoming Call Detection: The server detects incoming calls and switches to voicemail mode if there is no response within a certain period of time.

[1598] 2. Recording voice messages: Record voice messages left by the caller as digital data.

[1599] 3. Converting audio data to text: Convert recorded audio data to text using a speech recognition API (e.g., Google Speech-to-Text API).

[1600] 4. Emotional Analysis: The converted audio data is input into an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotions.

[1601] 5. Notification of Results: The text message and sentiment analysis results will be sent to the user's device via push notification or email.

[1602] terminal

[1603] The device is responsible for the following functions:

[1604] 1. Displaying notifications: Receive text messages and sentiment analysis results sent from the server and display them to the user.

[1605] 2. Incoming call and call waiting notifications: Receive and display notifications for incoming calls and call waiting.

[1606] User

[1607] The user performs the following actions:

[1608] 1. Check the message: Check the text message and sentiment analysis results on your device screen.

[1609] 2. Appropriate response: Take necessary actions promptly based on the message and emotional state.

[1610] Hardware and software

[1611] The hardware and software used to implement this system are as follows:

[1612] Hardware: Smartphones, smart glasses

[1613] Speech recognition API: Example: Google Speech-to-Text API

[1614] Emotion engine: Example: IBM Watson Tone Analyzer

[1615] Specific example

[1616] Examples of home security features

[1617] User A receives a call on their smartphone, and since there is no answer within a certain time, it switches to voicemail mode. The caller leaves a message saying, "An important meeting will be delayed until 2 PM." The server records this message and uses the Google Speech-to-Text API to transcribe it as "An important meeting will be delayed until 2 PM." Simultaneously, IBM Watson Tone Analyzer analyzes the sentiment and determines it to be "confused." The server pushes this text message and the sentiment analysis results to User A's smartphone, allowing User A to review the message and their emotional state.

[1618] Examples of security enhancements

[1619] User B is attempting a large transaction via internet banking. The server records User B's voice commands and converts them to text using the Google Speech-to-Text API. Simultaneously, it analyzes User B's emotions using IBM Watson Tone Analyzer and detects "anxiety." Based on these results, the server puts the transaction on hold and sends a confirmation notification to User B's smartphone.

[1620] Example of a prompt

[1621] Examples of prompts to input into a generative AI model are as follows:

[1622] Please input the audio data and perform sentiment analysis. If the results show an anomaly (abnormality), please issue an alert.

[1623] In this way, this invention enables users to understand not only the content of voice messages but also the emotional state of the caller, and further contributes to enhanced security.

[1624] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1625] Step 1:

[1626] The user's device detects an incoming call. The device receives the incoming call signal and notifies the user. If the user does not respond within a certain time, the device sends a no-response signal to the server. The input is the incoming call signal, and the output is the no-response signal.

[1627] Step 2:

[1628] The server receives a no-response signal from the terminal and switches to answering machine mode. At this point, the server automatically notifies the caller that the device has entered answering machine mode and records the caller's message. The input is the no-response signal, and the output is the recorded voice data.

[1629] Step 3:

[1630] The server stores the recorded audio data and uses the Google Speech-to-Text API to convert the audio data to text. The server inputs the audio data into the API and retrieves the text data. In this step, the input is the audio data and the output is the transcribed data.

[1631] Step 4:

[1632] The server performs sentiment analysis on the transcribed audio data using IBM Watson Tone Analyzer. The text data is input to the sentiment analysis engine, and the sentiment analysis results are obtained. The input is text data, and the output is the sentiment analysis results.

[1633] Step 5:

[1634] The server sends the converted text data and sentiment analysis results to the user's device via push notification or email. Here, the server formats the text data and sentiment analysis results and sends them to the device. The input is the text data and sentiment analysis results, and the output is the notification sent to the user's device.

[1635] Step 6:

[1636] The user's device receives notifications sent from the server and displays the text message and sentiment analysis results on the screen. Here, the device parses the notification to generate data to display to the user and displays it on the screen. The input is the notification from the server, and the output is the text and sentiment analysis results displayed to the user.

[1637] Step 7:

[1638] The user reviews the received text message and sentiment analysis results on the device screen. Based on this information, the user decides on the necessary actions. The input is the displayed text and sentiment analysis results, and the output is the user's actions and decisions.

[1639] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1640] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1641] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1642] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1643] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1644] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1645] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1646] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1647] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1648] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1649] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1650] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1651] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1652] 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.

[1653] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1654] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1655] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1656] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1657] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1658] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1659] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1660] The following is further disclosed regarding the embodiments described above.

[1661] (Claim 1)

[1662] A means for detecting incoming calls,

[1663] A means to switch to answering machine mode if there is no response within a certain period of time,

[1664] A means of recording the caller's voice message,

[1665] A means of converting recorded audio data into text,

[1666] A means of notifying the user's device of the converted text data,

[1667] A system that includes this.

[1668] (Claim 2)

[1669] A means for detecting an incoming call while a call is in progress,

[1670] A means of switching to call waiting mode when the user is unable to answer,

[1671] A means of recording voice messages from new callers,

[1672] A means of converting recorded audio data into text,

[1673] A means of notifying the user's device of the converted text data,

[1674] The system according to claim 1, including the following:

[1675] (Claim 3)

[1676] Methods for using speech recognition APIs to convert audio data to text,

[1677] A means of sending text messages to users via push notifications or email,

[1678] The system according to claim 1, including the following:

[1679] "Example 1"

[1680] (Claim 1)

[1681] A means for detecting incoming calls,

[1682] A means to switch to answering machine mode if there is no response within a certain period of time,

[1683] A means of recording the caller's voice message,

[1684] A means of converting recorded audio data into text,

[1685] A means of notifying the user's device of the converted text data,

[1686] A means of displaying incoming call notifications on the device,

[1687] A means of notifying the caller that the phone is in voicemail mode,

[1688] Means of sending text messages via push notifications or email,

[1689] A means of displaying text messages on the device screen,

[1690] A system that includes this.

[1691] (Claim 2)

[1692] A means for detecting an incoming call while a call is in progress,

[1693] A means of switching to call waiting mode when the user is unable to answer,

[1694] A means of recording voice messages from new callers,

[1695] A means of converting recorded audio data into text,

[1696] A means of notifying the user's device of the converted text data,

[1697] A means of displaying text messages during a call on a device,

[1698] The system according to claim 1, including the following:

[1699] (Claim 3)

[1700] Methods for using speech recognition APIs to convert audio data to text,

[1701] A means of sending text messages to users via push notifications or email,

[1702] The system according to claim 1, including the following:

[1703] "Application Example 1"

[1704] (Claim 1)

[1705] A means for detecting incoming calls,

[1706] A means to switch to answering machine mode if there is no response within a certain period of time,

[1707] A means of recording the caller's voice message,

[1708] A means of converting recorded audio data into text,

[1709] A means of notifying the user of the converted text data on their mobile device,

[1710] A mechanism to automatically switch to answering machine mode if store employees are unable to respond,

[1711] A means of displaying text messages notified to the user's mobile device,

[1712] A system that includes this.

[1713] (Claim 2)

[1714] A means for detecting an incoming call while a call is in progress,

[1715] A means of switching to call waiting mode when the user is unable to answer,

[1716] A means of recording voice messages from new callers,

[1717] A means of converting recorded audio data into text,

[1718] A means of notifying the user of the converted text data on their mobile device,

[1719] The system according to claim 1, including the following:

[1720] (Claim 3)

[1721] Methods for using speech recognition APIs to convert audio data to text,

[1722] A means of sending text messages to users via push notifications or email,

[1723] A means of receiving customer calls as text messages when store employees are absent or busy,

[1724] The system according to claim 1, including the following:

[1725] "Example 2 of combining an emotion engine"

[1726] (Claim 1)

[1727] A means for detecting incoming calls,

[1728] A means to switch to answering machine mode if there is no response within a certain period of time,

[1729] A means of recording the caller's voice message,

[1730] A means of converting recorded audio data into text,

[1731] A means of recognizing and analyzing emotions from audio data,

[1732] A means for notifying the user's device of the converted text data and sentiment analysis results,

[1733] A system that includes this.

[1734] (Claim 2)

[1735] A means for detecting an incoming call while a call is in progress,

[1736] A means of switching to call waiting mode when the user is unable to answer,

[1737] A means of recording voice messages from new callers,

[1738] A means of converting recorded audio data into text,

[1739] A means of recognizing and analyzing emotions from audio data,

[1740] A means for notifying the user's device of the converted text data and sentiment analysis results,

[1741] The system according to claim 1, including the following:

[1742] (Claim 3)

[1743] Methods for using speech recognition APIs to convert audio data to text,

[1744] A means of utilizing an emotion engine to recognize and analyze emotions from voice data,

[1745] A means of sending text messages and sentiment analysis results to users via push notifications or email,

[1746] The system according to claim 1, including the following:

[1747] "Application example 2 when combining with an emotional engine"

[1748] (Claim 1)

[1749] A means for detecting incoming calls,

[1750] A means to switch to answering machine mode if there is no response within a certain period of time,

[1751] A means of recording the caller's voice message,

[1752] A means of converting recorded audio data into text,

[1753] A method for analyzing emotions from recorded audio data,

[1754] A means of notifying the user's device of the sentiment analysis results,

[1755] A means for notifying the user's device of the converted text data and sentiment analysis results,

[1756] A system that includes this.

[1757] (Claim 2)

[1758] A means for detecting an incoming call while a call is in progress,

[1759] A means of switching to call waiting mode when the user is unable to answer,

[1760] A means of recording voice messages from new callers,

[1761] A means of converting recorded audio data into text,

[1762] A method for analyzing emotions from recorded audio data,

[1763] A means of notifying the user's device of the sentiment analysis results,

[1764] A means for notifying the user's device of the converted text data and sentiment analysis results,

[1765] The system according to claim 1, including the following:

[1766] (Claim 3)

[1767] Methods for using speech recognition APIs to convert audio data to text,

[1768] A means of using an emotion engine for the recognition and analysis of emotions,

[1769] A means of sending text messages and sentiment analysis results to users via push notifications or email,

[1770] The system according to claim 1, including the following: [Explanation of Symbols]

[1771] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for detecting incoming calls, A means to switch to answering machine mode if there is no response within a certain period of time, A means of recording the caller's voice message, A means of converting recorded audio data into text, A means of notifying the user's device of the converted text data, A system that includes this.

2. A means for detecting an incoming call while a call is in progress, A means of switching to call waiting mode when the user is unable to answer, A means of recording voice messages from new callers, A means of converting recorded audio data into text, A means of notifying the user's device of the converted text data, The system according to claim 1, including the following:

3. Methods for using speech recognition APIs to convert audio data to text, A means of sending text messages to users via push notifications or email, The system according to claim 1, including the following:

Images