System

Abstract

An object of a system according to an embodiment is to appropriately handle an incoming call from an unknown telephone number.SOLUTION: A system includes an automatic response unit, a caller information confirmation unit, a task confirmation unit, and a mediation determination unit. The automatic response unit automatically responds to an incoming call from an unknown telephone number. The caller information confirmation section confirms the information of the caller responded by the automatic response section. The business confirmation unit confirms the business of the caller confirmed by the caller information confirmation unit. The relay determination unit determines whether or not to relay to the called party based on the matter confirmed by the matter confirmation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to respond appropriately to calls from unknown phone numbers.

[0005] The system according to the embodiment aims to appropriately handle calls from unknown phone numbers. [Means for solving the problem]

[0006] The system according to the embodiment includes an automatic answering unit, a caller information confirmation unit, a message confirmation unit, and a forwarding determination unit. The automatic answering unit automatically answers calls from unknown telephone numbers. The caller information confirmation unit confirms the information of the caller answered by the automatic answering unit. The message confirmation unit confirms the message of the caller confirmed by the caller information confirmation unit. The forwarding determination unit determines whether to forward the call to the recipient based on the message confirmed by the message confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately handle an incoming call from an unknown phone number. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The call answering system according to the embodiment of the present invention uses AI to confirm the caller and purpose of calls from unknown phone numbers, and transfers the call to the callee as necessary. This allows the call answering system to efficiently and safely handle calls from unknown phone numbers.

[0029] An incoming call answering system according to an embodiment includes an automatic answering unit, a caller information confirmation unit, a message confirmation unit, and a transfer determination unit. The automatic answering unit automatically answers an incoming call from an unknown phone number. For example, the automatic answering unit uses voice recognition technology to understand what the caller is saying and confirm the caller's information and message. The automatic answering unit can also respond to the caller by saying, "Hello, this is the automatic answering system. May I have your name and your message?" The automatic answering unit can also acquire information by analyzing the caller's voice. The caller information confirmation unit confirms the caller's information answered by the automatic answering unit. For example, the caller information confirmation unit confirms the caller's name and phone number. The caller information confirmation unit can also confirm the caller's address and other information. The caller information confirmation unit can also acquire information by analyzing the caller's voice. The message confirmation unit confirms the caller's message confirmed by the caller information confirmation unit. For example, the message confirmation unit confirms the message the caller wants to convey. The message confirmation unit can also confirm questions or requests from the caller. The message confirmation unit can also confirm the message by analyzing the caller's voice. The transfer determination unit determines whether to transfer the call to the recipient based on the message confirmed by the message confirmation unit. For example, the transfer determination unit transfers the call to the recipient if the caller has an important matter. The transfer determination unit can also transfer the call to the recipient if the caller is a known person. The transfer determination unit does not transfer the call if it is determined to be a sales call or a nuisance call. This allows the call answering system according to the embodiment to efficiently and safely respond to calls from unknown phone numbers. For example, important messages can be received without being missed, and protection from nuisance calls and sales calls is also strengthened.

[0030] The caller information verification unit can provide a more personalized response by referring to the caller's past call history and message history. The caller information verification unit, for example, refers to the caller's past call history and provides a personalized response based on the content of previous conversations. For example, it may revisit topics discussed in previous calls. The caller information verification unit also refers to the caller's past message history and provides a personalized response based on the content of previous messages. For example, it may revisit topics discussed in previous messages. The caller information verification unit also refers to the caller's past call history and message history and provides a response based on the caller's preferences and interests. For example, it may bring up topics that the caller likes. This makes it possible to provide a more personalized response to the caller.

[0031] The caller information verification unit can verify the identity of a caller not only through the caller's voice but also through video calls using facial recognition technology. The caller information verification unit, for example, can verify the identity of a caller through video calls by recognizing the caller's face. For example, it can verify the identity of a caller using facial recognition technology. The caller information verification unit can also verify the identity of a caller by analyzing both the caller's voice and video calls. For example, it can verify the identity by combining voice recognition technology and facial recognition technology. The caller information verification unit can also verify the identity of a caller by referring to the caller's past video call history. For example, it can verify the identity based on identity information confirmed in past video calls. This makes it possible to verify the identity of a caller through a video call.

[0032] The caller information confirmation unit can automatically detect the caller's language and provide a multilingual response as needed. The caller information confirmation unit, for example, automatically detects the caller's language and provides a response in the corresponding language. For example, it supports English, Spanish, Chinese, etc. The caller information confirmation unit also automatically detects the caller's language and provides a response using a translation function as needed. For example, it detects the caller's language and provides a response using the translation function. The caller information confirmation unit also automatically detects the caller's language and provides a response using a template in the corresponding language. For example, it provides a response using a template according to the caller's language. This enables multilingual support according to the caller's language.

[0033] The message confirmation unit can classify the caller's messages in detail and set priorities based on specific keywords and phrases. The message confirmation unit, for example, classifies the caller's messages in detail and sets priorities based on specific keywords. For example, messages containing keywords such as "urgent" or "important" are set to a high priority. The message confirmation unit also classifies the caller's messages in detail and sets priorities based on specific phrases. For example, messages containing phrases such as "urgent" or "now" are set to a high priority. The message confirmation unit also classifies the caller's messages in detail and sets priorities based on specific keywords and phrases. For example, messages containing keywords or phrases such as "urgent" or "important" are set to a high priority. This makes it possible to set priorities based on the caller's messages and respond efficiently.

[0034] The message confirmation unit can analyze the caller's message using natural language processing technology and make a judgment by referring to related past data and cases. The message confirmation unit, for example, analyzes the caller's message using natural language processing technology and makes a judgment by referring to related past data. For example, it determines a response method based on similar past cases. The message confirmation unit can also analyze the caller's message using natural language processing technology and make a judgment by referring to related past cases. For example, it determines a response method based on past response cases. The message confirmation unit can also analyze the caller's message using natural language processing technology and make a judgment by referring to related past data and cases. For example, it determines a response method based on past data and cases. This makes it possible to appropriately judge the caller's message based on past data and cases.

[0035] The message confirmation unit can automatically generate the caller's message as a text message and send it to the recipient in advance. The message confirmation unit, for example, automatically generates the caller's message as a text message and sends it to the recipient in advance. For example, it sends a message that concisely summarizes the caller's requirements. The message confirmation unit can also automatically generate the caller's message as a text message and send it to the recipient in advance. For example, it can send a summary of the caller's requirements. The message confirmation unit can also automatically generate the caller's message as a text message and send it to the recipient in advance. For example, it can send a message that concisely summarizes the caller's requirements. This makes it possible to notify the recipient of the caller's message in advance.

[0036] The message confirmation unit can record the caller's message as a voice memo so that the recipient can check it later. The message confirmation unit, for example, records the caller's message as a voice memo so that the recipient can check it later. For example, it records and saves the caller's requirements as they are. The message confirmation unit also records the caller's message as a voice memo so that the recipient can check it later. For example, it records and saves the caller's requirements. The message confirmation unit also records the caller's message as a voice memo so that the recipient can check it later. For example, it records and saves the caller's requirements as they are. This allows the caller's message to be recorded as a voice memo so that the recipient can check it later.

[0037] The relay determination unit can refer to the recipient's current situation and make the notification at the optimal timing. The relay determination unit, for example, refers to the recipient's calendar information and makes the notification at the optimal timing. For example, the notification is delayed if the recipient is in a meeting or has an important appointment. The relay determination unit also refers to the recipient's location information and makes the notification at the optimal timing. For example, the notification is delayed if the recipient is on the move. The relay determination unit also refers to the recipient's current situation and makes the notification at the optimal timing. For example, the notification is delayed when the recipient is relaxing. This makes it possible to make the notification at the optimal timing depending on the recipient's current situation.

[0038] The call forwarding determination unit customizes the notification content based on the recipient's past response history, enabling more effective notification. The call forwarding determination unit, for example, references the recipient's past response history to customize the notification content. For example, it prioritizes notifications of incoming calls from callers who have made important calls in the past. The call forwarding determination unit also references the recipient's past response history to customize the notification content. For example, it does not notify incoming calls from callers who have been ignored in the past. The call forwarding determination unit also references the recipient's past response history to customize the notification content. For example, it prioritizes notifications of incoming calls from callers who have made important calls in the past. This allows the notification content to be customized based on the recipient's past response history, enabling more effective notification.

[0039] The relay determination unit can notify the called party through a device such as a smartwatch or a smart speaker. For example, the relay determination unit can notify the called party through a smartwatch. For example, the notification can be made using the vibration function of the smartwatch. The relay determination unit can also notify the called party through a smart speaker. For example, the notification can be made using the voice notification function of the smart speaker. The relay determination unit can also notify the called party through a device such as a smartwatch or a smart speaker. For example, the notification can be made using the vibration function of the smartwatch or the voice notification function of the smart speaker. This makes it possible to notify the called party through a device such as a smartwatch or a smart speaker.

[0040] The relay determination unit can add a function to automatically delay notifications under certain circumstances, such as when the recipient is driving or in a meeting. The relay determination unit adds a function to automatically delay notifications when the recipient is driving, for example. For example, the relay determination unit may detect a car's Bluetooth connection and delay the notification. The relay determination unit also adds a function to automatically delay notifications when the recipient is in a meeting, for example, by referring to calendar information. The relay determination unit also adds a function to automatically delay notifications under certain circumstances, such as when the recipient is driving or in a meeting. For example, the relay determination unit may delay notifications by referring to a car's Bluetooth connection or calendar information. This makes it possible to delay notifications when the recipient is in a certain situation.

[0041] In the forwarding judgment unit, when a caller leaves a message, the AI ​​can automatically summarize the main points and generate a concise message. In the forwarding judgment unit, for example, when a caller leaves a message, the AI ​​can automatically extract the main points and generate a concise message. For example, it can summarize the caller's long message. In addition, when a caller leaves a message, the AI ​​can automatically extract the main points and generate a concise message. For example, it can generate a message that concisely summarizes the caller's requirements. In addition, when a caller leaves a message, the AI ​​can automatically extract the main points and generate a concise message. For example, it can summarize the caller's long message. This makes it possible to concisely summarize the caller's message.

[0042] The relay determination unit can convert the caller's message from voice to text and save it in a format that is easy for the recipient to check later. The relay determination unit, for example, converts the caller's message from voice to text and saves it in a format that is easy for the recipient to check later. For example, it converts a voice message into text and saves it. The relay determination unit also converts the caller's message from voice to text and saves it in a format that is easy for the recipient to check later. For example, it converts a voice message into text and saves it. The relay determination unit also converts the caller's message from voice to text and saves it in a format that is easy for the recipient to check later. For example, it converts a voice message into text and saves it. This makes it possible to convert the caller's message from voice to text and save it in a format that is easy for the recipient to check later.

[0043] The relay determination unit can enable the caller to select a video message when leaving a message. The relay determination unit, for example, enables the caller to select a video message when leaving a message. For example, the caller records and leaves a video message. The relay determination unit also enables the caller to select a video message when leaving a message. For example, the caller records and leaves a video message. The relay determination unit also enables the caller to select a video message when leaving a message. For example, the caller records and leaves a video message. This makes it possible for the caller to leave a video message.

[0044] The relay determination unit can store the caller's message in the cloud, allowing the called party to access it from anywhere. The relay determination unit, for example, stores the caller's message in the cloud, allowing the called party to access it from anywhere. For example, it stores the message in cloud storage. The relay determination unit also stores the caller's message in the cloud, allowing the called party to access it from anywhere. For example, it stores the message in cloud storage. The relay determination unit also stores the caller's message in the cloud, allowing the called party to access it from anywhere. For example, it stores the message in cloud storage. This allows the caller's message to be stored in the cloud, allowing the called party to access it from anywhere.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The call answering system can also analyze the characteristics of the caller's voice to estimate the caller's age group and gender. For example, the pitch and tone of the voice can be analyzed to estimate the caller's age group. The voice characteristics can also be analyzed to estimate the caller's gender. This allows for an appropriate response based on the caller's age group and gender.

[0047] The call answering system can also refer to the caller's past call history and message history to provide a more personalized response. For example, it can revisit topics discussed in previous calls. It can also refer to the caller's past message history to provide a personalized response based on the content of previous messages. This allows for a more personalized response to the caller.

[0048] The call answering system can also respond to video calls in addition to the caller's voice and verify the caller's identity using facial recognition technology. For example, it can respond to video calls and verify the caller's identity by recognizing the caller's face. It can also verify the caller's identity by combining voice recognition technology and facial recognition technology. This makes it possible to verify the caller's identity through video calls.

[0049] The call response system can also automatically detect the caller's language and respond in multiple languages ​​as needed. For example, it can automatically detect the caller's language and respond in the appropriate language. It can also use a translation function to respond. This allows for multilingual support according to the caller's language.

[0050] The call answering system can further categorize the caller's message and set priorities based on specific keywords and phrases. For example, messages containing keywords such as "urgent" or "important" can be assigned a high priority. Messages containing phrases such as "urgent" or "right now" can also be assigned a high priority. This allows for prioritization based on the caller's message, enabling efficient responses.

[0051] The call response system can further analyze the caller's purpose using natural language processing technology and make a decision by referring to related past data and cases. For example, it can decide how to respond based on similar past cases. It can also decide how to respond based on past response cases. This allows it to appropriately determine the caller's purpose based on past data and cases.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The automated answering unit automatically answers calls from unknown phone numbers. For example, the automated answering unit uses voice recognition technology to understand what the caller is saying and confirm the caller's information and purpose. The automated answering unit can also respond to the caller by saying something like, "Hello, this is the automated answering system. Please let us know your name and purpose." The automated answering unit can also analyze the caller's voice to obtain information. Step 2: The caller information verification unit verifies the information of the caller answered by the automatic response unit. For example, the caller information verification unit verifies the caller's name and phone number. The caller information verification unit can also verify the caller's address and other information. The caller information verification unit can also obtain information by analyzing the caller's voice. Step 3: The message confirmation unit confirms the message of the caller confirmed by the caller information confirmation unit. For example, the message confirmation unit confirms what the caller wants to communicate. The message confirmation unit can also confirm any questions or requests the caller may have. The message confirmation unit can also analyze the caller's voice to confirm the message. Step 4: The transfer determination unit determines whether to transfer the call to the recipient based on the message confirmed by the message confirmation unit. For example, the transfer determination unit may transfer the call to the recipient if the caller has an important matter to deal with. The transfer determination unit may also transfer the call to the recipient if the caller is someone the caller knows. The transfer determination unit may not transfer the call if it determines that the call is a sales call or a nuisance call.

[0054] (Example 2) The call answering system according to the embodiment of the present invention uses AI to confirm the caller and purpose of calls from unknown phone numbers, and transfers the call to the callee as necessary. This allows the call answering system to efficiently and safely handle calls from unknown phone numbers.

[0055] An incoming call answering system according to an embodiment includes an automatic answering unit, a caller information confirmation unit, a message confirmation unit, and a transfer determination unit. The automatic answering unit automatically answers an incoming call from an unknown phone number. For example, the automatic answering unit uses voice recognition technology to understand what the caller is saying and confirm the caller's information and message. The automatic answering unit can also respond to the caller by saying, "Hello, this is the automatic answering system. May I have your name and your message?" The automatic answering unit can also acquire information by analyzing the caller's voice. The caller information confirmation unit confirms the caller's information answered by the automatic answering unit. For example, the caller information confirmation unit confirms the caller's name and phone number. The caller information confirmation unit can also confirm the caller's address and other information. The caller information confirmation unit can also acquire information by analyzing the caller's voice. The message confirmation unit confirms the caller's message confirmed by the caller information confirmation unit. For example, the message confirmation unit confirms the message the caller wants to convey. The message confirmation unit can also confirm questions or requests from the caller. The message confirmation unit can also confirm the message by analyzing the caller's voice. The transfer determination unit determines whether to transfer the call to the recipient based on the message confirmed by the message confirmation unit. For example, the transfer determination unit transfers the call to the recipient if the caller has an important matter. The transfer determination unit can also transfer the call to the recipient if the caller is a known person. The transfer determination unit does not transfer the call if it is determined to be a sales call or a nuisance call. This allows the call answering system according to the embodiment to efficiently and safely respond to calls from unknown phone numbers. For example, important messages can be received without being missed, and protection from nuisance calls and sales calls is also strengthened.

[0056] The automatic response unit can analyze the tone and speed of the caller's voice to estimate the urgency and emotional state and adjust the content of the response. The automatic response unit, for example, analyzes the tone and speed of the caller's voice in real time to estimate the urgency. For example, if the voice is high and fast, it determines that the call is urgent and responds quickly. The automatic response unit also analyzes the tone and speed of the caller's voice to estimate the emotional state. For example, if the voice is low and slow, it determines that the caller is relaxed and responds calmly. The automatic response unit also analyzes the tone and speed of the caller's voice to adjust the content of the response. For example, if the caller is angry, it responds calmly. This makes it possible to provide an appropriate response according to the caller's urgency and emotional state.

[0057] The caller information verification unit can provide a more personalized response by referring to the caller's past call history and message history. The caller information verification unit, for example, refers to the caller's past call history and provides a personalized response based on the content of previous conversations. For example, it may revisit topics discussed in previous calls. The caller information verification unit also refers to the caller's past message history and provides a personalized response based on the content of previous messages. For example, it may revisit topics discussed in previous messages. The caller information verification unit also refers to the caller's past call history and message history and provides a response based on the caller's preferences and interests. For example, it may bring up topics that the caller likes. This makes it possible to provide a more personalized response to the caller.

[0058] The caller information confirmation unit can use the emotion estimation function to analyze the caller's emotions in real time and generate an appropriate response. For example, the caller information confirmation unit uses the emotion estimation function to analyze the caller's tone of voice and language to estimate the emotion in real time. For example, it detects emotions such as anger and sadness. The caller information confirmation unit also uses the emotion estimation function to analyze the caller's facial expression to estimate the emotion in real time. For example, it detects smiles and tears. The caller information confirmation unit also uses the emotion estimation function to analyze the caller's text message to estimate the emotion in real time. For example, it detects exclamation marks and emoticons. This enables an appropriate response according to the caller's emotions.

[0059] The caller information verification unit can verify the identity of a caller not only through the caller's voice but also through video calls using facial recognition technology. The caller information verification unit, for example, can verify the identity of a caller through video calls by recognizing the caller's face. For example, it can verify the identity of a caller using facial recognition technology. The caller information verification unit can also verify the identity of a caller by analyzing both the caller's voice and video calls. For example, it can verify the identity by combining voice recognition technology and facial recognition technology. The caller information verification unit can also verify the identity of a caller by referring to the caller's past video call history. For example, it can verify the identity based on identity information confirmed in past video calls. This makes it possible to verify the identity of a caller through a video call.

[0060] The caller information confirmation unit can automatically detect the caller's language and provide a multilingual response as needed. The caller information confirmation unit, for example, automatically detects the caller's language and provides a response in the corresponding language. For example, it supports English, Spanish, Chinese, etc. The caller information confirmation unit also automatically detects the caller's language and provides a response using a translation function as needed. For example, it detects the caller's language and provides a response using the translation function. The caller information confirmation unit also automatically detects the caller's language and provides a response using a template in the corresponding language. For example, it provides a response using a template according to the caller's language. This enables multilingual support according to the caller's language.

[0061] The caller information confirmation unit uses the emotion estimation function to play music or sound effects in the background that correspond to the emotion of the caller, thereby relaxing the caller. The caller information confirmation unit, for example, uses the emotion estimation function to play music in the background that corresponds to the emotional state of the caller. For example, if the caller is nervous, relaxing music is played. The caller information confirmation unit also uses the emotion estimation function to play sound effects in the background that correspond to the emotional state of the caller. For example, relaxing sound effects are played. The caller information confirmation unit also uses the emotion estimation function to play music or sound effects in the background that correspond to the emotional state of the caller, thereby relaxing the caller. For example, if the caller is nervous, relaxing music or sound effects are played. This makes it possible to relax the caller with music or sound effects that correspond to the emotion of the caller.

[0062] The message confirmation unit can classify the caller's messages in detail and set priorities based on specific keywords and phrases. The message confirmation unit, for example, classifies the caller's messages in detail and sets priorities based on specific keywords. For example, messages containing keywords such as "urgent" or "important" are set to a high priority. The message confirmation unit also classifies the caller's messages in detail and sets priorities based on specific phrases. For example, messages containing phrases such as "urgent" or "now" are set to a high priority. The message confirmation unit also classifies the caller's messages in detail and sets priorities based on specific keywords and phrases. For example, messages containing keywords or phrases such as "urgent" or "important" are set to a high priority. This makes it possible to set priorities based on the caller's messages and respond efficiently.

[0063] The message confirmation unit can analyze the caller's message using natural language processing technology and make a judgment by referring to related past data and cases. The message confirmation unit, for example, analyzes the caller's message using natural language processing technology and makes a judgment by referring to related past data. For example, it determines a response method based on similar past cases. The message confirmation unit can also analyze the caller's message using natural language processing technology and make a judgment by referring to related past cases. For example, it determines a response method based on past response cases. The message confirmation unit can also analyze the caller's message using natural language processing technology and make a judgment by referring to related past data and cases. For example, it determines a response method based on past data and cases. This makes it possible to appropriately judge the caller's message based on past data and cases.

[0064] The message confirmation unit uses the emotion estimation function to consider the emotional state of the caller and can prioritize urgent messages. The message confirmation unit, for example, uses the emotion estimation function to analyze the emotional state of the caller and prioritize urgent messages. For example, if the caller is feeling strong anxiety, it determines that the call is urgent. The message confirmation unit also uses the emotion estimation function to analyze the emotional state of the caller and prioritize urgent messages. For example, if the caller is angry, it determines that the call is urgent. The message confirmation unit also uses the emotion estimation function to analyze the emotional state of the caller and prioritize urgent messages. For example, if the caller is sad, it determines that the call is urgent. This makes it possible to consider the emotional state of the caller and prioritize urgent messages.

[0065] The message confirmation unit can automatically generate the caller's message as a text message and send it to the recipient in advance. The message confirmation unit, for example, automatically generates the caller's message as a text message and sends it to the recipient in advance. For example, it sends a message that concisely summarizes the caller's requirements. The message confirmation unit can also automatically generate the caller's message as a text message and send it to the recipient in advance. For example, it can send a summary of the caller's requirements. The message confirmation unit can also automatically generate the caller's message as a text message and send it to the recipient in advance. For example, it can send a message that concisely summarizes the caller's requirements. This makes it possible to notify the recipient of the caller's message in advance.

[0066] The message confirmation unit can record the caller's message as a voice memo so that the recipient can check it later. The message confirmation unit, for example, records the caller's message as a voice memo so that the recipient can check it later. For example, it records and saves the caller's requirements as they are. The message confirmation unit also records the caller's message as a voice memo so that the recipient can check it later. For example, it records and saves the caller's requirements. The message confirmation unit also records the caller's message as a voice memo so that the recipient can check it later. For example, it records and saves the caller's requirements as they are. This allows the caller's message to be recorded as a voice memo so that the recipient can check it later.

[0067] The message confirmation unit uses the emotion estimation function to select a response template according to the emotion of the caller, allowing for a more appropriate response. The message confirmation unit, for example, uses the emotion estimation function to analyze the emotion of the caller and selects an appropriate response template. For example, if the caller is angry, a template that responds calmly is used. The message confirmation unit also uses the emotion estimation function to analyze the emotion of the caller and selects an appropriate response template. For example, if the caller is sad, a comforting template is used. The message confirmation unit also uses the emotion estimation function to analyze the emotion of the caller and selects an appropriate response template. For example, if the caller is happy, a template that shows empathy is used. This makes it possible to respond appropriately according to the emotion of the caller.

[0068] The relay determination unit can refer to the recipient's current situation and make the notification at the optimal timing. The relay determination unit, for example, refers to the recipient's calendar information and makes the notification at the optimal timing. For example, the notification is delayed if the recipient is in a meeting or has an important appointment. The relay determination unit also refers to the recipient's location information and makes the notification at the optimal timing. For example, the notification is delayed if the recipient is on the move. The relay determination unit also refers to the recipient's current situation and makes the notification at the optimal timing. For example, the notification is delayed when the recipient is relaxing. This makes it possible to make the notification at the optimal timing depending on the recipient's current situation.

[0069] The call forwarding determination unit customizes the notification content based on the recipient's past response history, enabling more effective notification. The call forwarding determination unit, for example, references the recipient's past response history to customize the notification content. For example, it prioritizes notifications of incoming calls from callers who have made important calls in the past. The call forwarding determination unit also references the recipient's past response history to customize the notification content. For example, it does not notify incoming calls from callers who have been ignored in the past. The call forwarding determination unit also references the recipient's past response history to customize the notification content. For example, it prioritizes notifications of incoming calls from callers who have made important calls in the past. This allows the notification content to be customized based on the recipient's past response history, enabling more effective notification.

[0070] The relay determination unit can use the emotion estimation function to consider the emotional state of the recipient and provide notification at an appropriate timing and in an appropriate manner. The relay determination unit, for example, uses the emotion estimation function to analyze the emotional state of the recipient and provide notification at an appropriate timing. For example, the notification is provided when the recipient is relaxed. The relay determination unit also uses the emotion estimation function to analyze the emotional state of the recipient and provide notification in an appropriate manner. For example, a gentle notification sound is used when the recipient is feeling stressed. The relay determination unit also uses the emotion estimation function to analyze the emotional state of the recipient and provide notification at an appropriate timing and in an appropriate manner. For example, a notification is provided when the recipient is relaxed and a gentle notification sound is used. This makes it possible to provide notification at an appropriate timing and in an appropriate manner depending on the emotional state of the recipient.

[0071] The relay determination unit can notify the called party through a device such as a smartwatch or a smart speaker. For example, the relay determination unit can notify the called party through a smartwatch. For example, the notification can be made using the vibration function of the smartwatch. The relay determination unit can also notify the called party through a smart speaker. For example, the notification can be made using the voice notification function of the smart speaker. The relay determination unit can also notify the called party through a device such as a smartwatch or a smart speaker. For example, the notification can be made using the vibration function of the smartwatch or the voice notification function of the smart speaker. This makes it possible to notify the called party through a device such as a smartwatch or a smart speaker.

[0072] The relay determination unit can add a function to automatically delay notifications under certain circumstances, such as when the recipient is driving or in a meeting. The relay determination unit adds a function to automatically delay notifications when the recipient is driving, for example. For example, the relay determination unit may detect a car's Bluetooth connection and delay the notification. The relay determination unit also adds a function to automatically delay notifications when the recipient is in a meeting, for example, by referring to calendar information. The relay determination unit also adds a function to automatically delay notifications under certain circumstances, such as when the recipient is driving or in a meeting. For example, the relay determination unit may delay notifications by referring to a car's Bluetooth connection or calendar information. This makes it possible to delay notifications when the recipient is in a certain situation.

[0073] The relay determination unit can use the emotion estimation function to select a notification sound or a vibration pattern according to the emotion of the call recipient. The relay determination unit, for example, uses the emotion estimation function to analyze the emotion of the call recipient and selects an appropriate notification sound. For example, a gentle notification sound is used when the call recipient is relaxed. The relay determination unit also uses the emotion estimation function to analyze the emotion of the call recipient and selects an appropriate vibration pattern. For example, a gentle vibration pattern is used when the call recipient is relaxed. The relay determination unit also uses the emotion estimation function to analyze the emotion of the call recipient and selects an appropriate notification sound or vibration pattern. For example, a gentle notification sound or vibration pattern is used when the call recipient is relaxed. This makes it possible to select a notification sound or vibration pattern according to the emotion of the call recipient.

[0074] In the forwarding judgment unit, when a caller leaves a message, the AI ​​can automatically summarize the main points and generate a concise message. In the forwarding judgment unit, for example, when a caller leaves a message, the AI ​​can automatically extract the main points and generate a concise message. For example, it can summarize the caller's long message. In addition, when a caller leaves a message, the AI ​​can automatically extract the main points and generate a concise message. For example, it can generate a message that concisely summarizes the caller's requirements. In addition, when a caller leaves a message, the AI ​​can automatically extract the main points and generate a concise message. For example, it can summarize the caller's long message. This makes it possible to concisely summarize the caller's message.

[0075] The relay determination unit can convert the caller's message from voice to text and save it in a format that is easy for the recipient to check later. The relay determination unit, for example, converts the caller's message from voice to text and saves it in a format that is easy for the recipient to check later. For example, it converts a voice message into text and saves it. The relay determination unit also converts the caller's message from voice to text and saves it in a format that is easy for the recipient to check later. For example, it converts a voice message into text and saves it. The relay determination unit also converts the caller's message from voice to text and saves it in a format that is easy for the recipient to check later. For example, it converts a voice message into text and saves it. This makes it possible to convert the caller's message from voice to text and save it in a format that is easy for the recipient to check later.

[0076] The relay determination unit can use the emotion estimation function to generate an appropriate response message according to the emotion of the caller. The relay determination unit, for example, uses the emotion estimation function to analyze the emotion of the caller and generate an appropriate response message. For example, if the caller is angry, it generates a message that responds calmly. The relay determination unit also uses the emotion estimation function to analyze the emotion of the caller and generate an appropriate response message. For example, if the caller is sad, it generates a comforting message. The relay determination unit also uses the emotion estimation function to analyze the emotion of the caller and generate an appropriate response message. For example, if the caller is happy, it generates a message that sympathizes. This makes it possible to generate an appropriate response message according to the emotion of the caller.

[0077] The relay determination unit can enable the caller to select a video message when leaving a message. The relay determination unit, for example, enables the caller to select a video message when leaving a message. For example, the caller records and leaves a video message. The relay determination unit also enables the caller to select a video message when leaving a message. For example, the caller records and leaves a video message. The relay determination unit also enables the caller to select a video message when leaving a message. For example, the caller records and leaves a video message. This makes it possible for the caller to leave a video message.

[0078] The relay determination unit can store the caller's message in the cloud, allowing the called party to access it from anywhere. The relay determination unit, for example, stores the caller's message in the cloud, allowing the called party to access it from anywhere. For example, it stores the message in cloud storage. The relay determination unit also stores the caller's message in the cloud, allowing the called party to access it from anywhere. For example, it stores the message in cloud storage. The relay determination unit also stores the caller's message in the cloud, allowing the called party to access it from anywhere. For example, it stores the message in cloud storage. This allows the caller's message to be stored in the cloud, allowing the called party to access it from anywhere.

[0079] The relay determination unit can use the emotion estimation function to add background music and sound effects to the message according to the emotion of the caller. The relay determination unit, for example, uses the emotion estimation function to analyze the emotion of the caller and add appropriate background music to the message. For example, if the caller is relaxed, calm music is added. The relay determination unit also uses the emotion estimation function to analyze the emotion of the caller and add appropriate sound effects to the message. For example, if the caller is nervous, relaxing sound effects are added. The relay determination unit also uses the emotion estimation function to analyze the emotion of the caller and add appropriate background music and sound effects to the message. For example, if the caller is relaxed, calm music and sound effects are added. This makes it possible to add background music and sound effects to the message according to the emotion of the caller.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The call answering system can also analyze the characteristics of the caller's voice to estimate the caller's age group and gender. For example, the pitch and tone of the voice can be analyzed to estimate the caller's age group. The voice characteristics can also be analyzed to estimate the caller's gender. This allows for an appropriate response based on the caller's age group and gender.

[0082] The call answering system can also analyze the tone and speed of the caller's voice to estimate the urgency and emotional state of the call and adjust the response accordingly. For example, if the voice is high-pitched and fast, it will be determined that the call is urgent and a prompt response will be made. On the other hand, if the voice is low and slow, it will be determined that the caller is relaxed and a gentler response will be made. This allows for an appropriate response depending on the caller's urgency and emotional state.

[0083] The call answering system can also refer to the caller's past call history and message history to provide a more personalized response. For example, it can revisit topics discussed in previous calls. It can also refer to the caller's past message history to provide a personalized response based on the content of previous messages. This allows for a more personalized response to the caller.

[0084] The call response system can also analyze the caller's emotions in real time and generate appropriate responses. For example, it can analyze the caller's tone of voice and choice of words to detect emotions such as anger or sadness. It can also analyze the caller's facial expressions to detect whether they are smiling or crying. This allows it to respond appropriately based on the caller's emotions.

[0085] The call answering system can also respond to video calls in addition to the caller's voice and verify the caller's identity using facial recognition technology. For example, it can respond to video calls and verify the caller's identity by recognizing the caller's face. It can also verify the caller's identity by combining voice recognition technology and facial recognition technology. This makes it possible to verify the caller's identity through video calls.

[0086] The call response system can also automatically detect the caller's language and respond in multiple languages ​​as needed. For example, it can automatically detect the caller's language and respond in the appropriate language. It can also use a translation function to respond. This allows for multilingual support according to the caller's language.

[0087] The call answering system can also play music or sound effects in the background that correspond to the caller's emotional state to help them relax. For example, if the caller is nervous, it can play relaxing music. It can also play relaxing sound effects. This makes it possible to relax the caller with music or sound effects that correspond to their emotions.

[0088] The call answering system can further categorize the caller's message and set priorities based on specific keywords and phrases. For example, messages containing keywords such as "urgent" or "important" can be assigned a high priority. Messages containing phrases such as "urgent" or "right now" can also be assigned a high priority. This allows for prioritization based on the caller's message, enabling efficient responses.

[0089] The call response system can further analyze the caller's purpose using natural language processing technology and make a decision by referring to related past data and cases. For example, it can decide how to respond based on similar past cases. It can also decide how to respond based on past response cases. This allows it to appropriately determine the caller's purpose based on past data and cases.

[0090] The call answering system can also take into account the caller's emotional state and prioritize urgent calls. For example, if the caller is feeling very anxious, it can determine that the call is urgent. It can also determine that the call is urgent if the caller is angry. This makes it possible to prioritize urgent calls by taking into account the caller's emotional state.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The automated answering unit automatically answers calls from unknown phone numbers. For example, the automated answering unit uses voice recognition technology to understand what the caller is saying and confirm the caller's information and purpose. The automated answering unit can also respond to the caller by saying something like, "Hello, this is the automated answering system. Please let us know your name and purpose." The automated answering unit can also analyze the caller's voice to obtain information. Step 2: The caller information verification unit verifies the information of the caller answered by the automatic response unit. For example, the caller information verification unit verifies the caller's name and phone number. The caller information verification unit can also verify the caller's address and other information. The caller information verification unit can also obtain information by analyzing the caller's voice. Step 3: The message confirmation unit confirms the message of the caller confirmed by the caller information confirmation unit. For example, the message confirmation unit confirms what the caller wants to communicate. The message confirmation unit can also confirm any questions or requests the caller may have. The message confirmation unit can also analyze the caller's voice to confirm the message. Step 4: The transfer determination unit determines whether to transfer the call to the recipient based on the message confirmed by the message confirmation unit. For example, the transfer determination unit may transfer the call to the recipient if the caller has an important matter to deal with. The transfer determination unit may also transfer the call to the recipient if the caller is someone the caller knows. The transfer determination unit may not transfer the call if it determines that the call is a sales call or a nuisance call.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an automatic answering unit that automatically answers calls from unknown telephone numbers; a caller information confirmation unit that confirms information of the caller responded to by the automatic response unit; a message confirmation unit that confirms the message of the sender confirmed by the sender information confirmation unit; and a transfer determination unit that determines whether to transfer the call to the recipient based on the business confirmed by the business confirmation unit. A system characterized by:

2. The automatic response unit Analyze the caller's tone and speed of voice to estimate urgency and emotional state and tailor the response accordingly.

2. The system of claim 1.

3. The caller information confirmation unit It supports not only voice calls but also video calls, and uses facial recognition technology to verify the caller's identity.

2. The system of claim 1.

4. The matter confirmation unit Break down the caller's needs and prioritize them based on specific keywords or phrases 2. The system of claim 1.

5. The relay determination unit Refer to the current status of the recipient and notify them at the most appropriate time.

2. The system of claim 1.

6. The caller information confirmation unit Using emotion estimation capabilities, the caller's emotions are analyzed in real time and an appropriate response is generated.

2. The system of claim 1.

7. The matter confirmation unit Using emotion estimation functionality, the system takes into account the caller's emotional state and prioritizes urgent matters.

2. The system of claim 1.

8. The relay determination unit Using an emotion estimation function, a notification sound or vibration pattern is selected according to the emotion of the recipient.

2. The system of claim 1.

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