System

Abstract

An object of the system according to the embodiment is to receive a telephone call in a natural language, accurately record the content thereof, and report the content to a user.SOLUTION: A system includes a response unit, a text generation unit, and a report unit. The answering unit responds to a call in a natural language. The text generation unit generates text from the content of the call made by the response unit. The reporting unit reports, to the user, the content of the call converted into text by the text conversion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to accurately record the contents of telephone conversations and report them to users later.

[0005] The system according to the embodiment aims to answer telephone calls in natural language, accurately record the content of the calls, and report the results to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a response unit, a text conversion unit, and a reporting unit. The response unit answers telephone calls in natural language. The text conversion unit converts the contents of the call made by the response unit into text. The reporting unit reports the contents of the call converted into text by the text conversion unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can answer telephone calls in natural language, accurately record the content of the call, and report it to the user. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The telephone answering system according to the embodiment of the present invention is a system in which AI answers telephone calls in natural language, converts the content of the answering call into text, and reports it to the user. This allows the user to calmly answer the phone whenever they like.

[0029] A telephone answering system according to an embodiment includes a response unit, a text conversion unit, and a reporting unit. The response unit responds to a call in natural language. For example, the response unit automatically answers an incoming call, responding in a manner such as, "Hello, this is an automated response system. How can I help you?" The response unit also uses speech recognition technology to understand what the caller is saying and generate an appropriate response. For example, the response unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to respond to what the caller is saying in natural language. The text conversion unit converts the content of the call made by the response unit into text. For example, the text conversion unit converts the content of the call into text using speech recognition technology. The text conversion unit can also summarize and convert the content of the call into text. For example, the text conversion unit summarizes and converts the content of the call into text using the generation AI. The reporting unit reports the content of the call converted into text by the text conversion unit to a user. For example, the reporting unit sends the converted content of the call to the user by email. The reporting unit can also display the converted content of the call on the user's device. For example, the reporting unit displays the text of the call on the screen of a smartphone or a computer. This allows the telephone answering system according to the embodiment to calmly answer calls whenever the user wants. For example, even if a call comes in during a busy time or a meeting, the user can check the call content later and calmly take the necessary action.

[0030] The response unit refers to the other party's background information during a call to provide a more personalized response. For example, the response unit refers to the other party's past call history during a call to provide a personalized response based on the content of the previous conversation. For example, the response unit uses a generation AI to analyze the other party's past call history and generate a response based on the content of the previous conversation. The response unit also refers to the other party's profile information to provide a personalized response. For example, the response unit generates a response based on profile information such as the other party's name and occupation. This makes it possible to provide a more personalized response to the other party.

[0031] The response unit provides a more appropriate response by deeply understanding the other party's intention during a call and presenting multiple options to the other party to choose from. For example, the response unit analyzes the other party's intention during a call and presents multiple options. For example, the response unit uses generation AI to analyze the other party's intention and generate multiple options. The response unit also provides a more appropriate response by allowing the other party to choose from. For example, if the other party inquires about a product, the response unit suggests multiple related products and allows the other party to choose from. This allows a deeper understanding of the other party's intention and a more appropriate response.

[0032] The response unit analyzes the tone or speed of the other party's voice during a call, determines the other party's level of urgency, and determines the priority of the response. For example, the response unit analyzes the tone and speed of the other party's voice during a call to determine the level of urgency. For example, the response unit uses a generation AI to analyze the tone and speed of the other party's voice and estimate the other party's level of urgency. The response unit also determines the priority of the response according to the level of urgency. For example, if the other party's voice sounds urgent, the response unit determines that the level of urgency is high and responds with priority. This makes it possible to respond appropriately according to the other party's level of urgency.

[0033] The response unit automatically translates the other party's language during a call, allowing for smooth communication even with people who speak different languages. The response unit, for example, automatically translates the other party's language during a call, allowing for smooth communication even with people who speak different languages. For example, the response unit uses a generation AI to translate the other party's language in real time and responds to people who speak different languages ​​in a natural language. The response unit also understands the context of the other party's language to improve the accuracy of the translation. For example, the response unit uses a generation AI to analyze the context of the other party's language and perform an appropriate translation. This allows for smooth communication even with people who speak different languages.

[0034] The text conversion unit can automatically highlight important keywords and phrases when converting the contents of a call into text. For example, the text conversion unit automatically highlights important keywords and phrases when converting the contents of a call into text. For example, the text conversion unit uses a generation AI to analyze the contents of a call and highlight keywords such as "urgent," "important," and "deadline." The text conversion unit also automatically highlights important phrases. For example, the text conversion unit uses a generation AI to analyze the contents of a call and highlight important phrases. This allows the user to immediately grasp important information.

[0035] The text conversion unit can automatically attach related materials and links when converting the contents of a call into text. For example, the text conversion unit automatically attaches related materials and links when converting the contents of a call into text. For example, the text conversion unit uses a generation AI to analyze the contents of a call and attach links to documents and websites mentioned during the call. The text conversion unit also automatically attaches related materials. For example, the text conversion unit uses a generation AI to analyze the contents of a call and attaches related materials. This allows the user to quickly refer to the information they need.

[0036] The text conversion unit can simultaneously store the audio data when converting the contents of a call into text, allowing the audio and text to be compared later. For example, when converting the contents of a call into text, the text conversion unit can simultaneously store the audio data, allowing the audio and text to be compared later. For example, the text conversion unit uses a generation AI to analyze the contents of the call and link the converted content to the corresponding audio file. In addition, when saving the audio data, the text conversion unit uses technology to maintain the quality of the audio. For example, the text conversion unit uses a generation AI to compress the audio data and save it while maintaining the quality. This allows the audio and text to be compared later.

[0037] When converting the contents of a call into text, the text conversion unit can generate reports in different formats and provide them in a format that meets the needs of the user. For example, when converting the contents of a call into text, the text conversion unit can generate reports in different formats. For example, the text conversion unit can analyze the contents of the call using a generation AI and create reports in a summary format, a detailed format, or a bulleted format. The text conversion unit can also provide reports in a format that meets the needs of the user. For example, the text conversion unit can analyze the contents of the call using a generation AI and generate reports in a format selected by the user. This allows reports to be provided in a format that meets the needs of the user.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The telephone answering system further includes a call content analysis unit. The analysis unit can analyze the call content and classify it based on specific topics or themes. For example, the analysis unit can analyze the call content and classify customer inquiries into product-related, service-related, technical support-related, etc. The analysis unit can also analyze the call content and extract frequently occurring keywords and phrases. For example, the analysis unit can analyze the call content and extract keywords such as "returns," "complaints," and "new products." The analysis unit can also analyze the call content and evaluate customer satisfaction. For example, the analysis unit can analyze the call content and score satisfaction based on what the customer says. This allows the user to analyze the call content in detail and understand customer needs and problems.

[0040] The telephone answering system further includes a learning unit. The learning unit can learn from past call data and improve the accuracy of responses. For example, the learning unit analyzes past call data and stores frequently asked questions and their answers in a database. The learning unit can also improve the content of responses based on user feedback. For example, the learning unit analyzes feedback provided by the user and adjusts the content and tone of the responses. Furthermore, the learning unit can regularly update the database to respond to new topics and trends. This allows the telephone answering system to always respond based on the latest information.

[0041] The telephone answering system further includes a call content summarizing unit. The summarizing unit can summarize the call content and extract important points. For example, the summarizing unit analyzes the call content and concisely summarizes the customer's requests and problems. The summarizing unit can also highlight important keywords and phrases when summarizing the call content. For example, the summarizing unit highlights keywords such as "urgent," "important," and "deadline." Furthermore, the summarizing unit can automatically attach related materials and links when reporting the summarized content to the user. This allows the user to concisely understand the call content and quickly refer to the information they need.

[0042] The telephone answering system further includes a translation unit for the call content. The translation unit translates the call content in real time, allowing for smooth communication even with people who speak different languages. For example, the translation unit analyzes the call content and automatically detects and translates the language of the other party. The translation unit can also understand the context of the other party's language to improve the accuracy of the translation. For example, the translation unit analyzes the context of the other party's language and provides an appropriate translation. Furthermore, the translation unit can also handle technical terms and industry jargon when translating the call content. This allows for smooth communication even with people who speak different languages.

[0043] The telephone answering system further includes a call content storage unit. When converting the call content into text, the storage unit can simultaneously store the voice data, allowing the voice and text to be compared later. For example, the storage unit analyzes the call content and links the converted text to the corresponding voice file. The storage unit can also use technology to maintain the quality of the voice when storing the voice data. For example, the storage unit compresses the voice data and stores it while maintaining its quality. The storage unit can also make the stored voice data searchable. For example, the storage unit can analyze the voice data and enable searches based on specific keywords or phrases. This allows the voice and text to be compared later and needed information to be found quickly.

[0044] The telephone answering system further includes a call content format conversion unit. When converting the call content into text, the format conversion unit can generate reports in different formats and provide them in a format that meets the user's needs. For example, the format conversion unit analyzes the call content and creates reports in a summary format, a detailed format, or a bulleted list format. The format conversion unit can also provide the report in a format that meets the user's needs. For example, the format conversion unit generates the report in a format selected by the user. Furthermore, when generating the report, the format conversion unit can automatically attach related materials and links. This allows the report to be provided in a format that meets the user's needs, allowing the user to quickly refer to the information they need.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The response unit answers the phone in natural language. For example, when a call comes in, it automatically answers, saying something like, "Hello, this is our automated response system. How can I help you?" It also uses speech recognition technology to understand what the other person is saying, and uses generation AI to generate an appropriate response. Step 2: The text conversion unit converts the contents of the call made by the response unit into text. For example, the contents of the call can be converted into text using voice recognition technology, and the contents of the call can be summarized and converted into text using generation AI. Step 3: The reporting unit reports the contents of the call that have been converted into text by the text conversion unit to the user. For example, the converted contents of the call can be sent by email or displayed on the screen of a smartphone or computer.

[0047] (Example 2) The telephone answering system according to the embodiment of the present invention is a system in which AI answers telephone calls in natural language, converts the content of the answering call into text, and reports it to the user. This allows the user to calmly answer the phone whenever they like.

[0048] A telephone answering system according to an embodiment includes a response unit, a text conversion unit, and a reporting unit. The response unit responds to a call in natural language. For example, the response unit automatically answers an incoming call, responding in a manner such as, "Hello, this is an automated response system. How can I help you?" The response unit also uses speech recognition technology to understand what the caller is saying and generate an appropriate response. For example, the response unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to respond to what the caller is saying in natural language. The text conversion unit converts the content of the call made by the response unit into text. For example, the text conversion unit converts the content of the call into text using speech recognition technology. The text conversion unit can also summarize and convert the content of the call into text. For example, the text conversion unit summarizes and converts the content of the call into text using the generation AI. The reporting unit reports the content of the call converted into text by the text conversion unit to a user. For example, the reporting unit sends the converted content of the call to the user by email. The reporting unit can also display the converted content of the call on the user's device. For example, the reporting unit displays the text of the call on the screen of a smartphone or a computer. This allows the telephone answering system according to the embodiment to calmly answer calls whenever the user wants. For example, even if a call comes in during a busy time or a meeting, the user can check the call content later and calmly take the necessary action.

[0049] The response unit estimates the emotions of the other party in real time during a call and generates a response according to the emotions. For example, the response unit analyzes the tone of voice and language of the other party during a call to estimate the emotions in real time. For example, the response unit uses a generation AI to analyze the tone of voice and language of the other party to estimate the other party's emotions. The response unit also generates a response according to the other party's emotions. For example, the response unit responds calmly if the other party is angry, and generates a response that shows empathy if the other party is happy. This makes it possible to provide an appropriate response according to the other party's emotions.

[0050] The response unit refers to the other party's background information during a call to provide a more personalized response. For example, the response unit refers to the other party's past call history during a call to provide a personalized response based on the content of the previous conversation. For example, the response unit uses a generation AI to analyze the other party's past call history and generate a response based on the content of the previous conversation. The response unit also refers to the other party's profile information to provide a personalized response. For example, the response unit generates a response based on profile information such as the other party's name and occupation. This makes it possible to provide a more personalized response to the other party.

[0051] The response unit provides a more appropriate response by deeply understanding the other party's intention during a call and presenting multiple options to the other party to choose from. For example, the response unit analyzes the other party's intention during a call and presents multiple options. For example, the response unit uses generation AI to analyze the other party's intention and generate multiple options. The response unit also provides a more appropriate response by allowing the other party to choose from. For example, if the other party inquires about a product, the response unit suggests multiple related products and allows the other party to choose from. This allows a deeper understanding of the other party's intention and a more appropriate response.

[0052] The response unit analyzes the tone or speed of the other party's voice during a call, determines the other party's level of urgency, and determines the priority of the response. For example, the response unit analyzes the tone and speed of the other party's voice during a call to determine the level of urgency. For example, the response unit uses a generation AI to analyze the tone and speed of the other party's voice and estimate the other party's level of urgency. The response unit also determines the priority of the response according to the level of urgency. For example, if the other party's voice sounds urgent, the response unit determines that the level of urgency is high and responds with priority. This makes it possible to respond appropriately according to the other party's level of urgency.

[0053] The response unit automatically translates the other party's language during a call, allowing for smooth communication even with people who speak different languages. The response unit, for example, automatically translates the other party's language during a call, allowing for smooth communication even with people who speak different languages. For example, the response unit uses a generation AI to translate the other party's language in real time and responds to people who speak different languages ​​in a natural language. The response unit also understands the context of the other party's language to improve the accuracy of the translation. For example, the response unit uses a generation AI to analyze the context of the other party's language and perform an appropriate translation. This allows for smooth communication even with people who speak different languages.

[0054] The response unit can monitor the emotions of the other party in real time during a call and generate a response that corresponds to that emotion. For example, the response unit can use generation AI to analyze the other party's emotions, responding calmly if the other party is angry, and generating a response that shows empathy if the other party is happy. This makes it possible to respond appropriately according to the other party's emotions.

[0055] The text conversion unit can automatically highlight important keywords and phrases when converting the contents of a call into text. For example, the text conversion unit automatically highlights important keywords and phrases when converting the contents of a call into text. For example, the text conversion unit uses a generation AI to analyze the contents of a call and highlight keywords such as "urgent," "important," and "deadline." The text conversion unit also automatically highlights important phrases. For example, the text conversion unit uses a generation AI to analyze the contents of a call and highlight important phrases. This allows the user to immediately grasp important information.

[0056] The text conversion unit can automatically attach related materials and links when converting the contents of a call into text. For example, the text conversion unit automatically attaches related materials and links when converting the contents of a call into text. For example, the text conversion unit uses a generation AI to analyze the contents of a call and attach links to documents and websites mentioned during the call. The text conversion unit also automatically attaches related materials. For example, the text conversion unit uses a generation AI to analyze the contents of a call and attaches related materials. This allows the user to quickly refer to the information they need.

[0057] The text conversion unit can perform emotion analysis when converting the contents of a call into text and display changes in emotion during the call in a graph. For example, the text conversion unit can perform emotion analysis when converting the contents of a call into text and display changes in emotion during the call in a graph. For example, the text conversion unit can analyze the contents of a call using a generation AI and display changes in emotion from the start to the end of the call in a line graph. The text conversion unit can also display changes in emotion in real time. For example, the text conversion unit can analyze the contents of a call using a generation AI and display changes in emotion in real time. This allows users to visually grasp changes in emotion during a call.

[0058] The text conversion unit can simultaneously store the audio data when converting the contents of a call into text, allowing the audio and text to be compared later. For example, when converting the contents of a call into text, the text conversion unit can simultaneously store the audio data, allowing the audio and text to be compared later. For example, the text conversion unit uses a generation AI to analyze the contents of the call and link the converted content to the corresponding audio file. In addition, when saving the audio data, the text conversion unit uses technology to maintain the quality of the audio. For example, the text conversion unit uses a generation AI to compress the audio data and save it while maintaining the quality. This allows the audio and text to be compared later.

[0059] When converting the contents of a call into text, the text conversion unit can generate reports in different formats and provide them in a format that meets the needs of the user. For example, when converting the contents of a call into text, the text conversion unit can generate reports in different formats. For example, the text conversion unit can analyze the contents of the call using a generation AI and create reports in a summary format, a detailed format, or a bulleted format. The text conversion unit can also provide reports in a format that meets the needs of the user. For example, the text conversion unit can analyze the contents of the call using a generation AI and generate reports in a format selected by the user. This allows reports to be provided in a format that meets the needs of the user.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The telephone answering system further includes a call content analysis unit. The analysis unit can analyze the call content and classify it based on specific topics or themes. For example, the analysis unit can analyze the call content and classify customer inquiries into product-related, service-related, technical support-related, etc. The analysis unit can also analyze the call content and extract frequently occurring keywords and phrases. For example, the analysis unit can analyze the call content and extract keywords such as "returns," "complaints," and "new products." The analysis unit can also analyze the call content and evaluate customer satisfaction. For example, the analysis unit can analyze the call content and score satisfaction based on what the customer says. This allows the user to analyze the call content in detail and understand customer needs and problems.

[0062] The telephone answering system further includes an emotion estimation unit. The emotion estimation unit can estimate the emotion of the other party in real time during a call and adjust the response based on the estimated emotion. For example, the emotion estimation unit analyzes the tone of the other party's voice and language, and generates a response that gives the other party a sense of security if the other party feels anxious. The emotion estimation unit can also respond calmly if the other party is excited, and generate a response that guides the other party to calm down. Furthermore, the emotion estimation unit can monitor changes in the other party's emotion in real time and adjust the content and tone of the response as appropriate. This enables flexible responses according to the other party's emotions.

[0063] The telephone answering system further includes a learning unit. The learning unit can learn from past call data and improve the accuracy of responses. For example, the learning unit analyzes past call data and stores frequently asked questions and their answers in a database. The learning unit can also improve the content of responses based on user feedback. For example, the learning unit analyzes feedback provided by the user and adjusts the content and tone of the responses. Furthermore, the learning unit can regularly update the database to respond to new topics and trends. This allows the telephone answering system to always respond based on the latest information.

[0064] The telephone answering system further includes an emotion estimation unit. The emotion estimation unit estimates the emotion of the other party in real time during a call and can adjust the tone and content of the response based on the estimated emotion. For example, the emotion estimation unit analyzes the tone of the other party's voice and language, and responds calmly if the other party is angry, and generates a response that shows empathy if the other party is happy. The emotion estimation unit can also monitor changes in the other party's emotion in real time and adjust the content and tone of the response as appropriate. Furthermore, the emotion estimation unit can suggest appropriate actions according to the other party's emotion. This enables flexible responses according to the other party's emotion.

[0065] The telephone answering system further includes a call content summarizing unit. The summarizing unit can summarize the call content and extract important points. For example, the summarizing unit analyzes the call content and concisely summarizes the customer's requests and problems. The summarizing unit can also highlight important keywords and phrases when summarizing the call content. For example, the summarizing unit highlights keywords such as "urgent," "important," and "deadline." Furthermore, the summarizing unit can automatically attach related materials and links when reporting the summarized content to the user. This allows the user to concisely understand the call content and quickly refer to the information they need.

[0066] The telephone answering system further includes an emotion estimation unit. The emotion estimation unit can estimate the emotion of the other party in real time during a call and adjust the content of the response based on the estimated emotion. For example, the emotion estimation unit analyzes the tone of the other party's voice and choice of words, and generates a response that gives the other party a sense of security if the other party feels anxious. The emotion estimation unit can also respond calmly if the other party is excited, and generate a response that guides the other party to calm down. Furthermore, the emotion estimation unit can monitor changes in the other party's emotion in real time and adjust the content and tone of the response as appropriate. This enables flexible responses according to the other party's emotions.

[0067] The telephone answering system further includes a translation unit for the call content. The translation unit translates the call content in real time, allowing for smooth communication even with people who speak different languages. For example, the translation unit analyzes the call content and automatically detects and translates the language of the other party. The translation unit can also understand the context of the other party's language to improve the accuracy of the translation. For example, the translation unit analyzes the context of the other party's language and provides an appropriate translation. Furthermore, the translation unit can also handle technical terms and industry jargon when translating the call content. This allows for smooth communication even with people who speak different languages.

[0068] The telephone answering system further includes an emotion estimation unit. The emotion estimation unit can estimate the emotion of the other party in real time during a call and adjust the content of the response based on the estimated emotion. For example, the emotion estimation unit analyzes the tone of the other party's voice and language, and responds calmly if the other party is angry, and generates a response that shows empathy if the other party is happy. The emotion estimation unit can also monitor changes in the other party's emotion in real time and adjust the content and tone of the response as appropriate. Furthermore, the emotion estimation unit can also suggest appropriate actions according to the other party's emotion. This enables flexible responses according to the other party's emotion.

[0069] The telephone answering system further includes a call content storage unit. When converting the call content into text, the storage unit can simultaneously store the voice data, allowing the voice and text to be compared later. For example, the storage unit analyzes the call content and links the converted text to the corresponding voice file. The storage unit can also use technology to maintain the quality of the voice when storing the voice data. For example, the storage unit compresses the voice data and stores it while maintaining its quality. The storage unit can also make the stored voice data searchable. For example, the storage unit can analyze the voice data and enable searches based on specific keywords or phrases. This allows the voice and text to be compared later and needed information to be found quickly.

[0070] The telephone answering system further includes a call content format conversion unit. When converting the call content into text, the format conversion unit can generate reports in different formats and provide them in a format that meets the user's needs. For example, the format conversion unit analyzes the call content and creates reports in a summary format, a detailed format, or a bulleted list format. The format conversion unit can also provide the report in a format that meets the user's needs. For example, the format conversion unit generates the report in a format selected by the user. Furthermore, when generating the report, the format conversion unit can automatically attach related materials and links. This allows the report to be provided in a format that meets the user's needs, allowing the user to quickly refer to the information they need.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The response unit answers the phone in natural language. For example, when a call comes in, it automatically answers, saying something like, "Hello, this is our automated response system. How can I help you?" It also uses speech recognition technology to understand what the other person is saying, and uses generation AI to generate an appropriate response. Step 2: The text conversion unit converts the contents of the call made by the response unit into text. For example, the contents of the call can be converted into text using voice recognition technology, and the contents of the call can be summarized and converted into text using generation AI. Step 3: The reporting unit reports the contents of the call that have been converted into text by the text conversion unit to the user. For example, the converted contents of the call can be sent by email or displayed on the screen of a smartphone or computer.

[0073] 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.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0140] 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 response section that answers calls in natural language; a text conversion unit that converts the contents of the call made by the answering unit into text; a reporting unit that reports the contents of the call that have been converted into text by the text conversion unit to a user. A system characterized by:

2. The response unit Estimate the other person's emotions in real time during a call and generate a response based on those emotions 2. The system of claim 1.

3. The response unit During a call, analyze the tone or speed of the other party's voice to determine the urgency of the other party and determine the priority of the response.

2. The system of claim 1.

4. The text conversion unit Automatically highlight important keywords and phrases when transcribing calls 2. The system of claim 1.

5. The text conversion unit When converting call content into text, sentiment analysis is performed and changes in sentiment during the call are displayed in a graph.

2. The system of claim 1.

6. The response unit See contextual information about the person you're calling to provide a more personalized response 2. The system of claim 1.

7. The response unit Automatically translate the other person's language during a call, allowing smooth communication even with people who speak different languages 2. The system of claim 1.

8. The text conversion unit Automatically attach relevant documents and links when transcribing calls 2. The system of claim 1.

Images