system

Abstract

The system according to the embodiment aims to have AI respond in real time. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit answers an incoming call. The generation unit analyzes the content of the conversation received by the reception unit and generates a response. The provision unit conveys the response generated by the generation unit to the caller.

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] In the conventional technology, there was a problem that the answering machine service was mechanical and it was difficult to respond in real time.

[0005] The system according to the embodiment aims to have AI respond in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit answers an incoming call. The generation unit analyzes the content of the conversation received by the reception unit and generates a response. The provision unit communicates the response generated by the generation unit to the caller. [Effects of the Invention]

[0007] The system according to the embodiment allows AI to respond in real time. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) An answering system according to an embodiment of the present invention answers an incoming call, and a generation AI analyzes the conversation content, generates an appropriate response, and transmits it to the caller. In the answering system, a voice assistant answers an incoming call, and the generation AI analyzes the conversation content in real time and generates an appropriate response. This response is transmitted to the caller via the voice assistant. For example, in the answering system, when a call comes in, the voice assistant transmits a message to the caller such as, "Hello, this is your answering machine. How can I help you?" The voice assistant recognizes the caller's voice and begins a conversation. Next, the generation AI analyzes the conversation content in real time. The generation AI converts the caller's voice into text and analyzes the text. For example, if the caller says, "I'd like to leave a message," the generation AI understands the text and generates an appropriate response. The generated response is transmitted to the caller via the voice assistant. For example, a response such as, "I'll leave a message for you. Please tell us what you need." In this way, a conversation with the caller progresses in real time. This will put an end to the traditional mechanical answering machine messages and make it commonplace for AI to respond in real time. Users will be freed from the stress of having to speak alone and will be able to have more natural conversations. In addition, by using generative AI and voice assistants, it will be possible to respond quickly and appropriately to the caller's requirements. For example, even if a caller wants to convey an urgent matter, the generative AI will respond in real time, allowing for a quick response. This will enable the answering system to respond quickly and appropriately to the caller's requirements. For example, even if a caller wants to convey an urgent matter, the generative AI will respond in real time, allowing for a quick response. Users will be freed from the stress of having to speak alone and will be able to have more natural conversations.

[0029] An answering system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit answers an incoming call. For example, the reception unit uses a voice assistant to convey a message to the caller, such as "Hello, this is an answering machine. How can I help you?" The reception unit can also recognize the caller's voice and start a conversation. The generation unit uses a generation AI to analyze the content of the conversation received by the reception unit and generate an appropriate response. For example, the generation unit converts the caller's voice into text and analyzes the content. The generation AI uses a text generation AI (e.g., LLM) to generate an appropriate response based on the caller's requirements. The generation unit can also generate a response based on the caller's requirements. The provision unit communicates the response generated by the generation unit to the caller. For example, the provision unit communicates a response generated using a voice assistant to the caller. This enables the answering system according to an embodiment to use AI to respond to incoming calls in real time.

[0030] The reception unit can recognize the caller's voice and start a conversation. The reception unit recognizes the caller's voice using, for example, a voice recognition algorithm. For example, the reception unit collects the caller's voice using a microphone and converts it into text using a voice recognition algorithm. The reception unit can also analyze the caller's voice in real time and start a conversation. For example, the reception unit analyzes the tone and speed of the caller's voice and starts a conversation at an appropriate timing. This allows a natural response by recognizing the caller's voice and starting a conversation.

[0031] The generation unit can convert the caller's voice into text and analyze the content of the text. The generation unit can convert the caller's voice into text, for example, using a voice recognition engine. For example, the generation unit can convert the caller's voice into text in real time and analyze the content of the text. The generation unit can also convert the caller's voice into text with high accuracy using a conversion algorithm. For example, the generation unit can analyze the characteristics of the caller's voice and perform appropriate text conversion. As a result, the caller's voice is converted into text and the content is analyzed, thereby generating an appropriate response.

[0032] The generation unit can generate a response based on the requirements of the caller. For example, the generation unit analyzes the requirements of the caller and generates an appropriate response. For example, the generation unit generates a response based on the question or request of the caller. The generation unit can also use generation AI to generate a response based on the requirements of the caller. For example, the generation unit uses an algorithm that understands the requirements of the caller and generates an appropriate response. This enables a quick and appropriate response by generating an appropriate response based on the requirements of the caller.

[0033] The providing unit can communicate the generated response to the caller. For example, the providing unit communicates the generated response to the caller using speech synthesis technology. For example, the providing unit communicates the generated response to the caller through a voice assistant. The providing unit can also communicate the generated response to the caller using a text message. For example, the providing unit sends the generated response to the caller as a text message. This allows the generated response to be communicated to the caller in real time.

[0034] The reception unit can analyze the caller's past call history and select a response method. For example, the reception unit analyzes the content of past conversations and selects a response method based on the caller's call history. For example, the reception unit customizes the response of the voice assistant based on phrases frequently used by the caller in the past. In addition, if the caller has previously communicated a specific requirement, the reception unit can prioritize a response related to that requirement. Furthermore, if the caller has previously expressed dissatisfaction, the reception unit provides a response to resolve that dissatisfaction. For example, the reception unit uses an algorithm that analyzes the caller's past call history and selects the optimal response method. As a result, analyzing the caller's past call history enables a more appropriate response.

[0035] The reception unit can analyze the characteristics of the caller's voice and generate an individual response. The reception unit can analyze the characteristics of the caller's voice using, for example, voice waveform analysis technology. For example, the reception unit can analyze the tone and pitch of the caller's voice and generate an optimal response. The reception unit can also analyze the characteristics of the caller's voice using voiceprint recognition technology and generate an individually customized response. For example, the reception unit can analyze the speed of the caller's voice and adjust the speed of the response. The reception unit can also analyze the emotion in the caller's voice and generate a response according to that emotion. For example, the reception unit can use an algorithm that analyzes the emotion in the caller's voice and generates an appropriate response. This makes it possible to customize a response according to the characteristics of the caller's voice.

[0036] The reception unit can analyze the caller's background sound and provide a response environment. The reception unit can analyze the caller's background sound using, for example, noise filtering technology. For example, if the caller's background sound is noisy, the reception unit can have the voice assistant respond in a louder voice. The reception unit can also analyze the caller's background sound using sound source separation technology and provide an appropriate response environment. For example, if the caller's background sound is quiet, the reception unit can have the voice assistant respond in a softer voice. The reception unit can also provide a response appropriate for a specific environment (e.g., in a car or in a cafe) if the caller's background sound is in a specific environment. For example, the reception unit can analyze the caller's background sound and use an algorithm to provide an appropriate response environment. As a result, an appropriate response environment is provided according to the caller's background sound.

[0037] The reception unit can provide a response tailored to a region by taking into account the geographical location information of the caller. The reception unit, for example, uses GPS data to obtain the geographical location information of the caller. For example, if the caller is in a specific region, the reception unit can provide information related to that region. Furthermore, if the caller is traveling, the reception unit can provide information about the caller's travel destination. For example, if the caller is at home, the reception unit can provide information about the area around the caller's home. Furthermore, the reception unit can customize a response based on region-specific information. For example, the reception unit uses an algorithm that takes into account the geographical location information of the caller to provide a response tailored to the region. This enables a response tailored to the region based on the caller's geographical location information.

[0038] The reception unit can analyze the caller's social media activity and reflect related information in the response. The reception unit, for example, analyzes the content of social media posts to understand the caller's activity. For example, the reception unit customizes the response based on information shared by the caller on social media. The reception unit can also analyze the caller's social media activity and include related topics in the response. For example, the reception unit customizes the response based on the activity of the caller's friends on social media. Furthermore, the reception unit can adjust the response based on the reactions of social media followers. For example, the reception unit uses an algorithm that analyzes the caller's social media activity and reflects related information in the response. This makes it possible to provide a response based on the caller's social media activity.

[0039] The reception unit can adjust the response method by reflecting the caller's past feedback. The reception unit, for example, analyzes the caller's past feedback and improves the response method. For example, the reception unit improves the response based on feedback provided by the caller in the past. Furthermore, if the caller has expressed dissatisfaction in the past, the reception unit can provide a response to resolve that dissatisfaction. Furthermore, the reception unit can provide a similar response based on a response method that satisfied the caller in the past. For example, the reception unit uses an algorithm that adjusts the response method by reflecting the caller's past feedback. This makes it possible to provide a customized response based on the caller's past feedback.

[0040] The generation unit can adjust the level of detail of the response based on the importance of the conversation content. For example, the generation unit evaluates the importance of the conversation content and adjusts the level of detail of the response. For example, the generation unit generates a detailed response for important conversation content. The generation unit can also generate a concise response for general conversation content. Furthermore, the generation unit can generate a quick and to-the-point response for urgent conversation content. For example, the generation unit uses an algorithm that evaluates the importance of the conversation content and adjusts the level of detail of the response. In this way, an appropriate response is generated according to the importance of the conversation content.

[0041] The generation unit can apply different response algorithms depending on the category of the conversation. For example, the generation unit classifies the category of the conversation and applies an appropriate response algorithm. For example, the generation unit generates a formal response for a business conversation. The generation unit can also generate a casual response for a private conversation. Furthermore, the generation unit can generate a quick and to-the-point response for an urgent conversation. For example, the generation unit uses an algorithm that classifies the category of the conversation and applies an appropriate response algorithm. In this way, an appropriate response is generated according to the category of the conversation.

[0042] The generation unit can improve the accuracy of the response by referring to the content of the caller's past conversations. The generation unit, for example, analyzes the content of the caller's past conversations to improve the accuracy of the response. For example, the generation unit customizes the response based on requirements previously communicated by the caller. The generation unit can also analyze the content of the caller's past conversations to generate an optimal response. Furthermore, the generation unit can improve the response based on feedback previously provided by the caller. For example, the generation unit uses an algorithm that improves the accuracy of the response by referring to the content of the caller's past conversations. This allows for the generation of a highly accurate response based on the content of the caller's past conversations.

[0043] The generation unit can determine the priority of responses based on the time when the conversation content was submitted. For example, the generation unit evaluates the time when the conversation content was submitted and determines the priority of responses. For example, the generation unit generates a response with the highest priority for urgent conversation content. The generation unit can also generate a response with normal priority for general conversation content. Furthermore, the generation unit can adjust the priority of responses based on requirements communicated by the caller during a specific time period. For example, the generation unit uses an algorithm that evaluates the time when the conversation content was submitted and determines the priority of responses. This allows an appropriate response to be generated according to the time when the conversation content was submitted.

[0044] The generation unit can adjust the order of responses based on the relevance of the conversation content. For example, the generation unit evaluates the relevance of the conversation content and adjusts the order of responses. For example, the generation unit generates a response first for important conversation content. The generation unit can also generate responses in a normal order for general conversation content. Furthermore, the generation unit can prioritize generating responses for highly relevant conversation content. For example, the generation unit uses an algorithm that evaluates the relevance of the conversation content and adjusts the order of responses. This allows appropriate responses to be generated according to the relevance of the conversation content.

[0045] The generation unit can adjust the use of technical terms in the response depending on the caller's level of expertise. For example, the generation unit evaluates the caller's level of expertise and adjusts the use of technical terms in the response. For example, if the caller has technical knowledge, the generation unit generates a response using technical terms. Furthermore, if the caller has general knowledge, the generation unit can generate a response in simple language. Furthermore, if the caller is a beginner, the generation unit can generate a response in easy-to-understand language. For example, the generation unit uses an algorithm that evaluates the caller's level of expertise and adjusts the use of technical terms in the response. As a result, an appropriate response is generated according to the caller's level of expertise.

[0046] When providing a response, the providing unit can select the way of conveying the response by referring to the caller's past response history. For example, the providing unit analyzes the caller's past response history and selects the optimal way of conveying the response. For example, the providing unit causes the voice assistant to convey the response based on the caller's preferred response methods in the past. The providing unit can also cause the voice assistant to convey the response while avoiding response methods that the caller has previously expressed dissatisfaction with. Furthermore, the providing unit uses an algorithm that analyzes the caller's past response history and selects the optimal way of conveying the response. For example, the providing unit customizes the response method by referring to the caller's past response history. This makes it possible to provide the optimal way of conveying the response based on the caller's past response history.

[0047] When providing a response, the providing unit can adjust the response content based on the caller's current situation. The providing unit, for example, evaluates the caller's location information and current activity status and customizes the response content. For example, the providing unit may cause the voice assistant to provide a concise response if the caller is on the move. The providing unit may also cause the voice assistant to provide a detailed response if the caller is at home. Furthermore, the providing unit may cause the voice assistant to provide a response in a quieter voice if the caller is in a meeting. For example, the providing unit may use an algorithm that evaluates the caller's current situation and adjusts the response content. This enables a customized response to be provided according to the caller's current situation.

[0048] When providing a response, the providing unit can improve the response method by reflecting the caller's feedback. For example, the providing unit analyzes the caller's past feedback and improves the response method. For example, the providing unit improves the response based on feedback provided by the caller in the past. Furthermore, if the caller has expressed dissatisfaction in the past, the providing unit can provide a response to resolve that dissatisfaction. Furthermore, the providing unit can provide a similar response based on a response method that satisfied the caller in the past. For example, the providing unit uses an algorithm that improves the response method by reflecting the caller's past feedback. This makes it possible to provide an improved response based on the caller's feedback.

[0049] When providing a response, the providing unit can select a response method taking into account the geographical location information of the caller. The providing unit, for example, acquires the geographical location information of the caller using GPS data. For example, if the caller is in a specific area, the providing unit can provide information related to that area. Furthermore, if the caller is traveling, the providing unit can also provide information about the caller's travel destination. Furthermore, if the caller is at home, the providing unit can also provide information about the area around the caller's home. For example, the providing unit uses an algorithm that selects the optimal response method taking into account the geographical location information of the caller. This makes it possible to select the optimal response method based on the caller's geographical location information.

[0050] When providing a response, the providing unit can analyze the sender's social media activity and provide related information. The providing unit, for example, analyzes the content of social media posts to understand the sender's activities. For example, the providing unit can provide information about places where the sender has checked in on social media. The providing unit can also analyze the content of the sender's social media posts to provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the sender's friends on social media. For example, the providing unit uses an algorithm that analyzes the sender's social media activity and provides related information. This makes it possible to provide related information based on the sender's social media activity.

[0051] When providing a response, the providing unit can adjust the response method by reflecting the caller's past feedback. The providing unit, for example, analyzes the caller's past feedback and improves the response method. For example, the providing unit improves the response based on feedback provided by the caller in the past. Furthermore, if the caller has expressed dissatisfaction in the past, the providing unit can provide a response to resolve that dissatisfaction. Furthermore, the providing unit can provide a similar response based on a response method that the caller was satisfied with in the past. For example, the providing unit uses an algorithm that adjusts the response method by reflecting the caller's past feedback. This makes it possible to provide a customized response based on the caller's past feedback.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The reception unit can analyze the characteristics of the caller's voice and estimate the caller's age group. For example, the reception unit can use voice waveform analysis technology to analyze the tone and pitch of the caller's voice and estimate the caller's age group. The reception unit can also analyze the speed and pronunciation characteristics of the caller's voice to estimate the caller's age group. Furthermore, the reception unit adjusts the content and tone of the response based on the estimated age group. For example, the reception unit can respond in a casual tone to younger people and in a polite tone to older people. This makes it possible to provide an appropriate response according to the caller's age group.

[0054] The generation unit can analyze the characteristics of the caller's voice and estimate the caller's health condition. For example, the generation unit can use voice analysis technology to analyze the caller's tone of voice and breathing sounds to estimate the caller's health condition. The generation unit can also analyze the caller's voice tremor and cough frequency to estimate the caller's health condition. Furthermore, the generation unit adjusts the content of the response based on the estimated health condition. For example, if the caller is tired, the generation AI can generate a relaxing response. If the caller is healthy, a normal response can be generated. This makes it possible to provide an appropriate response according to the caller's health condition.

[0055] The reception unit can analyze the characteristics of the caller's voice and estimate the caller's cultural background. For example, the reception unit can use voice waveform analysis technology to analyze the caller's accent and pronunciation characteristics and estimate the caller's cultural background. The reception unit can also analyze the caller's language usage patterns and estimate the caller's cultural background. Furthermore, the reception unit adjusts the content and tone of the response based on the estimated cultural background. For example, by using greetings and expressions unique to a particular culture, it is possible to provide a response that is friendly to the caller. This makes it possible to provide an appropriate response according to the caller's cultural background.

[0056] The providing unit can analyze the characteristics of the caller's voice and estimate the caller's language ability. For example, the providing unit can use voice waveform analysis technology to analyze the accuracy and fluency of the caller's pronunciation and estimate the caller's language ability. The providing unit can also analyze the caller's vocabulary usage patterns and estimate the caller's language ability. Furthermore, the providing unit adjusts the content and tone of the response based on the estimated language ability. For example, if the caller is unfamiliar with the language, the providing unit can respond in simple terms, and if the caller is fluent in the language, the providing unit can provide a response using technical terms. This makes it possible to provide an appropriate response according to the caller's language ability.

[0057] The reception unit can analyze the characteristics of the caller's voice and estimate the caller's gender. For example, the reception unit can use voice waveform analysis technology to analyze the pitch and tone of the caller's voice and estimate the caller's gender. The reception unit can also analyze the speed and pronunciation characteristics of the caller's voice to estimate the caller's gender. Furthermore, the reception unit adjusts the content and tone of the response based on the estimated gender. For example, if the caller is female, the voice assistant can respond in a softer tone, and if the caller is male, the voice assistant can respond in a stronger tone. This enables an appropriate response according to the caller's gender.

[0058] The providing unit can analyze the characteristics of the caller's voice and estimate the caller's occupation. For example, the providing unit can use voice waveform analysis technology to analyze the tone and pitch of the caller's voice and estimate the caller's occupation. The providing unit can also analyze the caller's language usage patterns to estimate the caller's occupation. Furthermore, the providing unit can adjust the content and tone of the response based on the estimated occupation. For example, if the caller is a business person, the response can be made in a formal tone, and if the caller is a student, the response can be made in a casual tone. This makes it possible to provide an appropriate response according to the caller's occupation.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The receptionist answers the incoming call. For example, the receptionist uses a voice assistant to convey a message to the caller, such as "Hello, this is your answering machine. How can I help you?" The receptionist can also recognize the caller's voice and start a conversation. Step 2: The generation unit analyzes the conversation content received by the reception unit and generates an appropriate response. For example, the generation unit converts the caller's voice into text and analyzes the content. The generation AI uses a text generation AI (e.g., LLM) to generate an appropriate response based on the caller's requirements. Step 3: The providing unit communicates the response generated by the generating unit to the caller. For example, the providing unit communicates the response generated using a voice assistant to the caller.

[0061] (Example 2) An answering system according to an embodiment of the present invention answers an incoming call, and a generation AI analyzes the conversation content, generates an appropriate response, and transmits it to the caller. In the answering system, a voice assistant answers an incoming call, and the generation AI analyzes the conversation content in real time and generates an appropriate response. This response is transmitted to the caller via the voice assistant. For example, in the answering system, when a call comes in, the voice assistant transmits a message to the caller such as, "Hello, this is your answering machine. How can I help you?" The voice assistant recognizes the caller's voice and begins a conversation. Next, the generation AI analyzes the conversation content in real time. The generation AI converts the caller's voice into text and analyzes the text. For example, if the caller says, "I'd like to leave a message," the generation AI understands the text and generates an appropriate response. The generated response is transmitted to the caller via the voice assistant. For example, a response such as, "I'll leave a message for you. Please tell us what you need." In this way, a conversation with the caller progresses in real time. This will put an end to the traditional mechanical answering machine messages and make it commonplace for AI to respond in real time. Users will be freed from the stress of having to speak alone and will be able to have more natural conversations. In addition, by using generative AI and voice assistants, it will be possible to respond quickly and appropriately to the caller's requirements. For example, even if a caller wants to convey an urgent matter, the generative AI will respond in real time, allowing for a quick response. This will enable the answering system to respond quickly and appropriately to the caller's requirements. For example, even if a caller wants to convey an urgent matter, the generative AI will respond in real time, allowing for a quick response. Users will be freed from the stress of having to speak alone and will be able to have more natural conversations.

[0062] An answering system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit answers an incoming call. For example, the reception unit uses a voice assistant to convey a message to the caller, such as "Hello, this is an answering machine. How can I help you?" The reception unit can also recognize the caller's voice and start a conversation. The generation unit uses a generation AI to analyze the content of the conversation received by the reception unit and generate an appropriate response. For example, the generation unit converts the caller's voice into text and analyzes the content. The generation AI uses a text generation AI (e.g., LLM) to generate an appropriate response based on the caller's requirements. The generation unit can also generate a response based on the caller's requirements. The provision unit communicates the response generated by the generation unit to the caller. For example, the provision unit communicates a response generated using a voice assistant to the caller. This enables the answering system according to an embodiment to use AI to respond to incoming calls in real time.

[0063] The reception unit can recognize the caller's voice and start a conversation. The reception unit recognizes the caller's voice using, for example, a voice recognition algorithm. For example, the reception unit collects the caller's voice using a microphone and converts it into text using a voice recognition algorithm. The reception unit can also analyze the caller's voice in real time and start a conversation. For example, the reception unit analyzes the tone and speed of the caller's voice and starts a conversation at an appropriate timing. This allows a natural response by recognizing the caller's voice and starting a conversation.

[0064] The generation unit can convert the caller's voice into text and analyze the content of the text. The generation unit can convert the caller's voice into text, for example, using a voice recognition engine. For example, the generation unit can convert the caller's voice into text in real time and analyze the content of the text. The generation unit can also convert the caller's voice into text with high accuracy using a conversion algorithm. For example, the generation unit can analyze the characteristics of the caller's voice and perform appropriate text conversion. As a result, the caller's voice is converted into text and the content is analyzed, thereby generating an appropriate response.

[0065] The generation unit can generate a response based on the requirements of the caller. For example, the generation unit analyzes the requirements of the caller and generates an appropriate response. For example, the generation unit generates a response based on the question or request of the caller. The generation unit can also use generation AI to generate a response based on the requirements of the caller. For example, the generation unit uses an algorithm that understands the requirements of the caller and generates an appropriate response. This enables a quick and appropriate response by generating an appropriate response based on the requirements of the caller.

[0066] The providing unit can communicate the generated response to the caller. For example, the providing unit communicates the generated response to the caller using speech synthesis technology. For example, the providing unit communicates the generated response to the caller through a voice assistant. The providing unit can also communicate the generated response to the caller using a text message. For example, the providing unit sends the generated response to the caller as a text message. This allows the generated response to be communicated to the caller in real time.

[0067] The reception unit can estimate the caller's emotion and adjust the tone of the response based on the estimated emotion. The reception unit estimates the caller's emotion using, for example, voice analysis technology. For example, the reception unit analyzes the tone and speed of the caller's voice and calculates an emotion score. The reception unit can also estimate the caller's emotion using facial expression recognition technology. For example, the reception unit captures the caller's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit then adjusts the tone of the response based on the estimated emotion of the caller. For example, if the caller is nervous, the voice assistant can respond in a calm tone. Alternatively, if the caller is angry, the voice assistant can respond in a calm and gentle tone. This allows for a more appropriate response by adjusting the tone of the response according to the caller's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] The reception unit can analyze the caller's past call history and select a response method. For example, the reception unit analyzes the content of past conversations and selects a response method based on the caller's call history. For example, the reception unit customizes the response of the voice assistant based on phrases frequently used by the caller in the past. In addition, if the caller has previously communicated a specific requirement, the reception unit can prioritize a response related to that requirement. Furthermore, if the caller has previously expressed dissatisfaction, the reception unit provides a response to resolve that dissatisfaction. For example, the reception unit uses an algorithm that analyzes the caller's past call history and selects the optimal response method. As a result, analyzing the caller's past call history enables a more appropriate response.

[0069] The reception unit can analyze the characteristics of the caller's voice and generate an individual response. The reception unit can analyze the characteristics of the caller's voice using, for example, voice waveform analysis technology. For example, the reception unit can analyze the tone and pitch of the caller's voice and generate an optimal response. The reception unit can also analyze the characteristics of the caller's voice using voiceprint recognition technology and generate an individually customized response. For example, the reception unit can analyze the speed of the caller's voice and adjust the speed of the response. The reception unit can also analyze the emotion in the caller's voice and generate a response according to that emotion. For example, the reception unit can use an algorithm that analyzes the emotion in the caller's voice and generates an appropriate response. This makes it possible to customize a response according to the characteristics of the caller's voice.

[0070] The reception unit can analyze the caller's background sound and provide a response environment. The reception unit can analyze the caller's background sound using, for example, noise filtering technology. For example, if the caller's background sound is noisy, the reception unit can have the voice assistant respond in a louder voice. The reception unit can also analyze the caller's background sound using sound source separation technology and provide an appropriate response environment. For example, if the caller's background sound is quiet, the reception unit can have the voice assistant respond in a softer voice. The reception unit can also provide a response appropriate for a specific environment (e.g., in a car or in a cafe) if the caller's background sound is in a specific environment. For example, the reception unit can analyze the caller's background sound and use an algorithm to provide an appropriate response environment. As a result, an appropriate response environment is provided according to the caller's background sound.

[0071] The reception unit can estimate the caller's emotion and prioritize responses based on the estimated emotion. The reception unit estimates the caller's emotion using, for example, voice analysis technology. For example, the reception unit analyzes the caller's tone and speed of voice and calculates an emotion score. The reception unit can also estimate the caller's emotion using facial expression recognition technology. For example, the reception unit captures the caller's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit then prioritizes responses based on the estimated emotion. For example, if the caller is communicating an urgent matter, the reception unit prioritizes the response. If the caller is relaxed, the reception unit responds in the normal order. If the caller is angry, the reception unit responds quickly to resolve the problem. This enables a prompt and appropriate response by prioritizing responses based on the caller's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The reception unit can provide a response tailored to a region by taking into account the geographical location information of the caller. The reception unit, for example, uses GPS data to obtain the geographical location information of the caller. For example, if the caller is in a specific region, the reception unit can provide information related to that region. Furthermore, if the caller is traveling, the reception unit can provide information about the caller's travel destination. For example, if the caller is at home, the reception unit can provide information about the area around the caller's home. Furthermore, the reception unit can customize a response based on region-specific information. For example, the reception unit uses an algorithm that takes into account the geographical location information of the caller to provide a response tailored to the region. This enables a response tailored to the region based on the caller's geographical location information.

[0073] The reception unit can analyze the caller's social media activity and reflect related information in the response. The reception unit, for example, analyzes the content of social media posts to understand the caller's activity. For example, the reception unit customizes the response based on information shared by the caller on social media. The reception unit can also analyze the caller's social media activity and include related topics in the response. For example, the reception unit customizes the response based on the activity of the caller's friends on social media. Furthermore, the reception unit can adjust the response based on the reactions of social media followers. For example, the reception unit uses an algorithm that analyzes the caller's social media activity and reflects related information in the response. This makes it possible to provide a response based on the caller's social media activity.

[0074] The reception unit can adjust the response method by reflecting the caller's past feedback. The reception unit, for example, analyzes the caller's past feedback and improves the response method. For example, the reception unit improves the response based on feedback provided by the caller in the past. Furthermore, if the caller has expressed dissatisfaction in the past, the reception unit can provide a response to resolve that dissatisfaction. Furthermore, the reception unit can provide a similar response based on a response method that satisfied the caller in the past. For example, the reception unit uses an algorithm that adjusts the response method by reflecting the caller's past feedback. This makes it possible to provide a customized response based on the caller's past feedback.

[0075] The generation unit can estimate the caller's emotions and adjust the content of the response based on the estimated caller's emotions. The generation unit estimates the caller's emotions using, for example, voice analysis technology. For example, the generation unit analyzes the caller's tone and speed of voice and calculates an emotion score. The generation unit can also estimate the caller's emotions using facial expression recognition technology. For example, the generation unit captures the caller's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit then adjusts the content of the response based on the estimated caller's emotions. For example, if the caller is nervous, the generation AI can generate a response with relaxing content. Also, if the caller is angry, the generation AI can generate a calm and gentle response. This generates an appropriate response content according to the caller's emotions. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The generation unit can adjust the level of detail of the response based on the importance of the conversation content. For example, the generation unit evaluates the importance of the conversation content and adjusts the level of detail of the response. For example, the generation unit generates a detailed response for important conversation content. The generation unit can also generate a concise response for general conversation content. Furthermore, the generation unit can generate a quick and to-the-point response for urgent conversation content. For example, the generation unit uses an algorithm that evaluates the importance of the conversation content and adjusts the level of detail of the response. In this way, an appropriate response is generated according to the importance of the conversation content.

[0077] The generation unit can apply different response algorithms depending on the category of the conversation. For example, the generation unit classifies the category of the conversation and applies an appropriate response algorithm. For example, the generation unit generates a formal response for a business conversation. The generation unit can also generate a casual response for a private conversation. Furthermore, the generation unit can generate a quick and to-the-point response for an urgent conversation. For example, the generation unit uses an algorithm that classifies the category of the conversation and applies an appropriate response algorithm. In this way, an appropriate response is generated according to the category of the conversation.

[0078] The generation unit can improve the accuracy of the response by referring to the content of the caller's past conversations. The generation unit, for example, analyzes the content of the caller's past conversations to improve the accuracy of the response. For example, the generation unit customizes the response based on requirements previously communicated by the caller. The generation unit can also analyze the content of the caller's past conversations to generate an optimal response. Furthermore, the generation unit can improve the response based on feedback previously provided by the caller. For example, the generation unit uses an algorithm that improves the accuracy of the response by referring to the content of the caller's past conversations. This allows for the generation of a highly accurate response based on the content of the caller's past conversations.

[0079] The generation unit can estimate the caller's emotion and adjust the length of the response based on the estimated caller's emotion. The generation unit estimates the caller's emotion using, for example, voice analysis technology. For example, the generation unit analyzes the caller's tone and speed of voice and calculates an emotion score. The generation unit can also estimate the caller's emotion using facial expression recognition technology. For example, the generation unit captures the caller's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit then adjusts the length of the response based on the estimated caller's emotion. For example, if the caller is nervous, the generation AI can generate a short, to-the-point response. On the other hand, if the caller is relaxed, the generation AI can generate a detailed response. This generates an appropriate response length according to the caller's emotion. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The generation unit can determine the priority of responses based on the time when the conversation content was submitted. For example, the generation unit evaluates the time when the conversation content was submitted and determines the priority of responses. For example, the generation unit generates a response with the highest priority for urgent conversation content. The generation unit can also generate a response with normal priority for general conversation content. Furthermore, the generation unit can adjust the priority of responses based on requirements communicated by the caller during a specific time period. For example, the generation unit uses an algorithm that evaluates the time when the conversation content was submitted and determines the priority of responses. This allows an appropriate response to be generated according to the time when the conversation content was submitted.

[0081] The generation unit can adjust the order of responses based on the relevance of the conversation content. For example, the generation unit evaluates the relevance of the conversation content and adjusts the order of responses. For example, the generation unit generates a response first for important conversation content. The generation unit can also generate responses in a normal order for general conversation content. Furthermore, the generation unit can prioritize generating responses for highly relevant conversation content. For example, the generation unit uses an algorithm that evaluates the relevance of the conversation content and adjusts the order of responses. This allows appropriate responses to be generated according to the relevance of the conversation content.

[0082] The generation unit can adjust the use of technical terms in the response depending on the caller's level of expertise. For example, the generation unit evaluates the caller's level of expertise and adjusts the use of technical terms in the response. For example, if the caller has technical knowledge, the generation unit generates a response using technical terms. Furthermore, if the caller has general knowledge, the generation unit can generate a response in simple language. Furthermore, if the caller is a beginner, the generation unit can generate a response in easy-to-understand language. For example, the generation unit uses an algorithm that evaluates the caller's level of expertise and adjusts the use of technical terms in the response. As a result, an appropriate response is generated according to the caller's level of expertise.

[0083] The providing unit can estimate the caller's emotions and adjust the way a response is delivered based on the estimated caller's emotions. The providing unit, for example, estimates the caller's emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the caller's voice and calculates an emotion score. The providing unit can also estimate the caller's emotions using facial expression recognition technology. For example, the providing unit captures the caller's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit further adjusts the way a response is delivered based on the estimated caller's emotions. For example, if the caller is nervous, the voice assistant can deliver the response in a calm voice. On the other hand, if the caller is angry, the voice assistant can deliver the response in a calm and gentle voice. This enables an appropriate response to be delivered based on the caller's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When providing a response, the providing unit can select the way of conveying the response by referring to the caller's past response history. For example, the providing unit analyzes the caller's past response history and selects the optimal way of conveying the response. For example, the providing unit causes the voice assistant to convey the response based on the caller's preferred response methods in the past. The providing unit can also cause the voice assistant to convey the response while avoiding response methods that the caller has previously expressed dissatisfaction with. Furthermore, the providing unit uses an algorithm that analyzes the caller's past response history and selects the optimal way of conveying the response. For example, the providing unit customizes the response method by referring to the caller's past response history. This makes it possible to provide the optimal way of conveying the response based on the caller's past response history.

[0085] When providing a response, the providing unit can adjust the response content based on the caller's current situation. The providing unit, for example, evaluates the caller's location information and current activity status and customizes the response content. For example, the providing unit may cause the voice assistant to provide a concise response if the caller is on the move. The providing unit may also cause the voice assistant to provide a detailed response if the caller is at home. Furthermore, the providing unit may cause the voice assistant to provide a response in a quieter voice if the caller is in a meeting. For example, the providing unit may use an algorithm that evaluates the caller's current situation and adjusts the response content. This enables a customized response to be provided according to the caller's current situation.

[0086] When providing a response, the providing unit can improve the response method by reflecting the caller's feedback. For example, the providing unit analyzes the caller's past feedback and improves the response method. For example, the providing unit improves the response based on feedback provided by the caller in the past. Furthermore, if the caller has expressed dissatisfaction in the past, the providing unit can provide a response to resolve that dissatisfaction. Furthermore, the providing unit can provide a similar response based on a response method that satisfied the caller in the past. For example, the providing unit uses an algorithm that improves the response method by reflecting the caller's past feedback. This makes it possible to provide an improved response based on the caller's feedback.

[0087] The providing unit can estimate the caller's emotions and determine the priority of responses based on the estimated caller's emotions. The providing unit, for example, estimates the caller's emotions using voice analysis technology. For example, the providing unit analyzes the caller's tone and speed of voice and calculates an emotion score. The providing unit can also estimate the caller's emotions using facial expression recognition technology. For example, the providing unit captures the caller's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit then determines the priority of responses based on the estimated caller's emotions. For example, if the caller is communicating an urgent matter, the response can be given top priority. If the caller is relaxed, the response can be handled in the normal order. If the caller is angry, the response can be handled quickly to resolve the problem. This enables appropriate response prioritization according to the caller's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] When providing a response, the providing unit can select a response method taking into account the geographical location information of the caller. The providing unit, for example, acquires the geographical location information of the caller using GPS data. For example, if the caller is in a specific area, the providing unit can provide information related to that area. Furthermore, if the caller is traveling, the providing unit can also provide information about the caller's travel destination. Furthermore, if the caller is at home, the providing unit can also provide information about the area around the caller's home. For example, the providing unit uses an algorithm that selects the optimal response method taking into account the geographical location information of the caller. This makes it possible to select the optimal response method based on the caller's geographical location information.

[0089] When providing a response, the providing unit can analyze the sender's social media activity and provide related information. The providing unit, for example, analyzes the content of social media posts to understand the sender's activities. For example, the providing unit can provide information about places where the sender has checked in on social media. The providing unit can also analyze the content of the sender's social media posts to provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the sender's friends on social media. For example, the providing unit uses an algorithm that analyzes the sender's social media activity and provides related information. This makes it possible to provide related information based on the sender's social media activity.

[0090] When providing a response, the providing unit can adjust the response method by reflecting the caller's past feedback. The providing unit, for example, analyzes the caller's past feedback and improves the response method. For example, the providing unit improves the response based on feedback provided by the caller in the past. Furthermore, if the caller has expressed dissatisfaction in the past, the providing unit can provide a response to resolve that dissatisfaction. Furthermore, the providing unit can provide a similar response based on a response method that the caller was satisfied with in the past. For example, the providing unit uses an algorithm that adjusts the response method by reflecting the caller's past feedback. This makes it possible to provide a customized response based on the caller's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit recognizes the caller's voice using the voice assistant of the smart device 14 and starts a conversation. The generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the content of the conversation using a generation AI, and generates an appropriate response. The provision unit conveys the response generated using the voice assistant of the smart device 14 to the caller. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit recognizes the caller's voice using the voice assistant of the smart glasses 214 and starts a conversation. The generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the content of the conversation using a generation AI, and generates an appropriate response. The provision unit conveys the response generated using the voice assistant of the smart glasses 214 to the caller. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit recognizes the caller's voice using the voice assistant of the headset type terminal 314 and starts a conversation. The generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the content of the conversation using a generation AI, and generates an appropriate response. The provision unit conveys the response generated using the voice assistant of the headset type terminal 314 to the caller. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit recognizes the caller's voice using the voice assistant of the robot 414 and starts a conversation. The generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the content of the conversation using a generation AI, and generates an appropriate response. The provision unit conveys the response generated using the voice assistant of the robot 414 to the caller.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The reception unit can analyze the characteristics of the caller's voice and estimate the caller's age group. For example, the reception unit can use voice waveform analysis technology to analyze the tone and pitch of the caller's voice and estimate the caller's age group. The reception unit can also analyze the speed and pronunciation characteristics of the caller's voice to estimate the caller's age group. Furthermore, the reception unit adjusts the content and tone of the response based on the estimated age group. For example, the reception unit can respond in a casual tone to younger people and in a polite tone to older people. This makes it possible to provide an appropriate response according to the caller's age group.

[0093] The generation unit can analyze the characteristics of the caller's voice and estimate the caller's health condition. For example, the generation unit can use voice analysis technology to analyze the caller's tone of voice and breathing sounds to estimate the caller's health condition. The generation unit can also analyze the caller's voice tremor and cough frequency to estimate the caller's health condition. Furthermore, the generation unit adjusts the content of the response based on the estimated health condition. For example, if the caller is tired, the generation AI can generate a relaxing response. If the caller is healthy, a normal response can be generated. This makes it possible to provide an appropriate response according to the caller's health condition.

[0094] The providing unit can estimate the emotion of the caller and adjust the content of the response based on the estimated emotion of the caller. For example, the providing unit estimates the emotion of the caller using voice analysis technology. For example, the providing unit analyzes the tone and speed of the caller's voice and calculates an emotion score. The providing unit can also estimate the emotion of the caller using facial expression recognition technology. For example, the providing unit captures the expression of the caller with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, the providing unit adjusts the content of the response based on the estimated emotion of the caller. For example, if the caller is nervous, the voice assistant can provide a response with relaxing content. Also, if the caller is angry, the voice assistant can provide a response with calm and gentle content. This enables an appropriate response according to the emotion of the caller.

[0095] The reception unit can analyze the characteristics of the caller's voice and estimate the caller's cultural background. For example, the reception unit can use voice waveform analysis technology to analyze the caller's accent and pronunciation characteristics and estimate the caller's cultural background. The reception unit can also analyze the caller's language usage patterns and estimate the caller's cultural background. Furthermore, the reception unit adjusts the content and tone of the response based on the estimated cultural background. For example, by using greetings and expressions unique to a particular culture, it is possible to provide a response that is friendly to the caller. This makes it possible to provide an appropriate response according to the caller's cultural background.

[0096] The generation unit can estimate the caller's emotions and adjust the level of detail in the response based on the estimated caller's emotions. For example, the generation unit can estimate the caller's emotions using voice analysis technology. For example, the generation unit can analyze the caller's tone and speed of voice and calculate an emotion score. The generation unit can also estimate the caller's emotions using facial expression recognition technology. For example, the generation unit can capture the caller's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the generation unit adjusts the level of detail in the response based on the estimated caller's emotions. For example, if the caller is nervous, the generation AI can generate a concise and to-the-point response. On the other hand, if the caller is relaxed, the generation AI can generate a detailed response. This enables an appropriate response according to the caller's emotions.

[0097] The providing unit can analyze the characteristics of the caller's voice and estimate the caller's language ability. For example, the providing unit can use voice waveform analysis technology to analyze the accuracy and fluency of the caller's pronunciation and estimate the caller's language ability. The providing unit can also analyze the caller's vocabulary usage patterns and estimate the caller's language ability. Furthermore, the providing unit adjusts the content and tone of the response based on the estimated language ability. For example, if the caller is unfamiliar with the language, the providing unit can respond in simple terms, and if the caller is fluent in the language, the providing unit can provide a response using technical terms. This makes it possible to provide an appropriate response according to the caller's language ability.

[0098] The reception unit can analyze the characteristics of the caller's voice and estimate the caller's gender. For example, the reception unit can use voice waveform analysis technology to analyze the pitch and tone of the caller's voice and estimate the caller's gender. The reception unit can also analyze the speed and pronunciation characteristics of the caller's voice to estimate the caller's gender. Furthermore, the reception unit adjusts the content and tone of the response based on the estimated gender. For example, if the caller is female, the voice assistant can respond in a softer tone, and if the caller is male, the voice assistant can respond in a stronger tone. This enables an appropriate response according to the caller's gender.

[0099] The generation unit can estimate the emotion of the caller and adjust the order of responses based on the estimated emotion of the caller. For example, the generation unit estimates the emotion of the caller using voice analysis technology. For example, the generation unit analyzes the tone and speed of the caller's voice and calculates an emotion score. The generation unit can also estimate the emotion of the caller using facial expression recognition technology. For example, the generation unit captures the expression of the caller with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, the generation unit adjusts the order of responses based on the estimated emotion of the caller. For example, if the caller is communicating an urgent matter, the generation unit generates responses with the highest priority. On the other hand, if the caller is relaxed, the generation unit can generate responses in the normal order. This enables appropriate responses according to the emotion of the caller.

[0100] The providing unit can analyze the characteristics of the caller's voice and estimate the caller's occupation. For example, the providing unit can use voice waveform analysis technology to analyze the tone and pitch of the caller's voice and estimate the caller's occupation. The providing unit can also analyze the caller's language usage patterns to estimate the caller's occupation. Furthermore, the providing unit can adjust the content and tone of the response based on the estimated occupation. For example, if the caller is a business person, the response can be made in a formal tone, and if the caller is a student, the response can be made in a casual tone. This makes it possible to provide an appropriate response according to the caller's occupation.

[0101] The generation unit can estimate the caller's emotions and adjust the timing of the response based on the estimated caller's emotions. For example, the generation unit estimates the caller's emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the caller's voice and calculates an emotion score. The generation unit can also estimate the caller's emotions using facial expression recognition technology. For example, the generation unit captures the caller's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, the generation unit adjusts the timing of the response based on the estimated caller's emotions. For example, if the caller is nervous, the generation AI can generate a response at a slower pace. On the other hand, if the caller is relaxed, the generation AI can generate a response quickly. This makes it possible to provide an appropriate response according to the caller's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The receptionist answers the incoming call. For example, the receptionist uses a voice assistant to convey a message to the caller, such as "Hello, this is your answering machine. How can I help you?" The receptionist can also recognize the caller's voice and start a conversation. Step 2: The generation unit analyzes the conversation content received by the reception unit and generates an appropriate response. For example, the generation unit converts the caller's voice into text and analyzes the content. The generation AI uses a text generation AI (e.g., LLM) to generate an appropriate response based on the caller's requirements. Step 3: The providing unit communicates the response generated by the generating unit to the caller. For example, the providing unit communicates the response generated using a voice assistant to the caller.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> 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.

[0106] 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, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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, in order to avoid confusion and to 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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. A receptionist who answers calls, a generation unit that analyzes the conversation content received by the reception unit and generates a response; a providing unit that notifies the sender of the response generated by the generating unit. A system characterized by:

2. The reception unit Recognizes the caller's voice and starts the conversation 2. The system of claim 1.

3. The generation unit Convert the caller's voice into text and analyze the content 2. The system of claim 1.

4. The generation unit Generate a response based on the caller's requirements 2. The system of claim 1.

5. The providing unit Communicate the generated response to the caller 2. The system of claim 1.

6. The reception unit Estimate the caller's sentiment and adjust the tone of the response based on the estimated sentiment 2. The system of claim 1.

7. The reception unit Analyze the caller's past call history and decide how to respond 2. The system of claim 1.

8. The reception unit Analyzes the caller's voice characteristics and generates personalized responses 2. The system of claim 1.

9. The reception unit Analyzes the caller's background sounds and provides a response environment 2. The system of claim 1.

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