System

Abstract

An object of a system according to an embodiment is to automate and efficiently operate reservation handling by telephone.SOLUTION: A system includes a speech recognition unit, a generation unit, a speech synthesis unit, and a reservation acceptance unit. The voice recognition unit performs voice recognition on the content of the call. The generation unit generates an appropriate answer based on the content recognized by the voice recognition unit. The speech synthesis unit converts the response generated by the generation unit into voice. The reservation acceptance unit accepts reservation contents using the voice generated by the speech synthesis 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] With conventional technology, handling reservations over the phone was time-consuming and laborious, making it difficult to operate efficiently.

[0005] The system according to the embodiment aims to automate and efficiently operate reservation responses by telephone. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice recognition unit, a generation unit, a voice synthesis unit, and a reservation reception unit. The voice recognition unit performs voice recognition of the contents of the telephone call. The generation unit generates an appropriate response based on the contents recognized by the voice recognition unit. The voice synthesis unit converts the response generated by the generation unit into voice. The reservation reception unit accepts the reservation contents using the voice generated by the voice synthesis unit. [Effects of the Invention]

[0007] The system according to the embodiment automates reservation handling by telephone and can be operated efficiently. [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 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 automated telephone response system according to an embodiment of the present invention is a system that automatically handles telephone calls from restaurants and other establishments. In this system, a generation AI recognizes the content of the call and responds appropriately based on that content. As a result, the automated telephone response system can improve the efficiency of telephone responses from restaurants and other establishments, and in particular automate the handling of reservations.

[0029] An automated telephone response system according to an embodiment includes a speech recognition unit, a generation unit, a speech synthesis unit, and a reservation reception unit. The speech recognition unit recognizes the content of a call. For example, when a call comes in, the speech recognition unit converts the speech of the caller into text data. For example, a speech such as "Hello, I'd like to make a reservation" is converted into text using speech recognition technology. The generation unit generates an appropriate response based on the content recognized by the speech recognition unit. For example, the generation unit responds in the form of "How many people are you booking for?" or "What date and time would you like?" The generation AI generates the appropriate response using a text generation AI (e.g., LLM). The speech synthesis unit converts the response generated by the generation unit into speech. For example, the speech synthesis unit converts the text data "How many people are you booking for?" into speech using speech synthesis technology, and conveys this to the caller. The reservation reception unit accepts the reservation details using the speech generated by the speech synthesis unit. For example, if the caller says, "I'd like to make a reservation for four people tomorrow at 7 p.m.", the reservation reception unit understands this content and enters it into the reservation system. The caller is also notified that the reservation has been completed. As a result, the automated telephone response system according to the embodiment can improve the efficiency of telephone responses at restaurants and other establishments, and can automate the process of accepting reservations in particular. For example, reservations can be accepted quickly even during busy times, reducing the burden on staff. It can also reduce mistakes in confirming reservation details.

[0030] The speech recognition unit can make the speech recognition technology multilingual, enabling it to accommodate users of different languages. The speech recognition unit, for example, makes the speech recognition technology multilingual, enabling it to accommodate users of different languages. For example, it can recognize multiple languages, such as English, French, and Chinese. Multilingual support is achieved based on the types of languages ​​that can be supported and translation algorithms. Different languages ​​are analyzed based on specific types and support methods, such as English, Chinese, and Spanish. Support is based on automatic language detection and translation accuracy. This enables multilingual support, enabling it to accommodate users of different languages.

[0031] The voice recognition unit can use voice recognition technology to automatically remove background sounds and noise and provide clear voice data to the generation AI. The voice recognition unit can, for example, use voice recognition technology to automatically remove background sounds and noise and provide clear voice data to the generation AI. For example, it can filter out ambient noise and extract only the user's speech. Background sounds are analyzed based on specific types and removal methods, such as environmental sounds and noise. Noise is analyzed based on specific types and removal methods, such as white noise and echo. Clear voice data is generated based on sound quality evaluation criteria and filtering methods. This allows background sounds and noise to be removed and clear voice data to be provided.

[0032] The generation unit can have the generation AI learn past reservation data and generate responses based on the user's past behavioral patterns. For example, the generation unit can have the generation AI learn past reservation data and generate responses based on the user's past behavioral patterns. For example, special treatment can be given to repeat customers. Past reservation data is analyzed based on specific types and usage methods, such as reservation date and time and reservation content. Behavior patterns are analyzed based on frequency analysis and pattern recognition algorithms. Responses are generated based on past behavioral patterns. This makes it possible to generate responses based on past behavioral patterns.

[0033] The generation unit allows the generation AI to retain a dialogue history and generate consistent responses in successive dialogues. The generation unit, for example, allows the generation AI to retain a dialogue history and generate consistent responses in successive dialogues. For example, referencing the details of a previous reservation and confirming the same details. The dialogue history is saved based on the database structure and the history storage period. Successive dialogues are analyzed based on dialogue intervals and consistency evaluation criteria. Consistent responses are generated based on dialogue context analysis and response consistency. This makes it possible to have consistent responses in successive dialogues.

[0034] The generation unit can use generation AI to enable responses to inquiries other than reservations. The generation unit, for example, uses generation AI to enable responses to inquiries other than reservations. For example, detailed explanations are provided for questions about the menu. Inquiries other than reservations are analyzed based on their specific type and response method, such as questions about the menu or confirmation of business hours. Whether a response can be made is determined based on the automatic classification of the inquiry and the method of generating a response. This makes it possible to respond to inquiries other than reservations.

[0035] The generation unit can use the generation AI to flexibly respond to specific user requests. The generation unit, for example, uses the generation AI to flexibly respond to specific user requests. For example, when a specific seat is reserved, it checks whether the seat is available. The specific request is analyzed based on the specific type and response method, such as allergy information or the reservation of a specific seat. The flexible response is based on the priority of the request and the method of changing the response. This makes it possible to flexibly respond to specific user requests.

[0036] The voice synthesis unit uses voice synthesis technology to generate characters with different voices and respond in a voice that suits the user's preferences. The voice synthesis unit, for example, uses voice synthesis technology to generate characters with different voices and respond in a voice that suits the user's preferences. For example, it is possible to select a male voice, a female voice, a young voice, an elderly voice, etc. Characters with different voices are generated based on specific types and generation methods, such as male voices, female voices, and child voices. A voice that suits the user's preferences is selected based on the user's settings and past selection history. This allows the user to respond in a voice that suits the user's preferences.

[0037] The speech synthesis unit can use speech synthesis technology to translate the user's speech in real time and generate speech that supports multiple languages. The speech synthesis unit can, for example, use speech synthesis technology to translate the user's speech in real time and generate speech that supports multiple languages. For example, speech in Japanese can be translated into English to generate speech. The speech is analyzed based on a speech recognition algorithm and text analysis. Real-time translation is achieved based on a translation algorithm and an allowable delay time range. This allows the user's speech to be translated in real time and speech that supports multiple languages ​​to be generated.

[0038] The speech synthesis unit can use speech synthesis technology to summarize the content of a user's utterance and generate a concise response. The speech synthesis unit can use, for example, speech synthesis technology to summarize the content of a user's utterance and generate a concise response. For example, it can respond by summarizing a long explanation in a short way. The summary is performed based on extraction of important information and a summarization algorithm. The concise response is generated based on the length of the response and the comprehensiveness of the information. This makes it possible to summarize the content of a user's utterance and generate a concise response.

[0039] The reservation reception unit can refer to the user's past reservation history when accepting a reservation and provide special treatment to repeat customers. For example, when accepting a reservation, the reservation reception unit can refer to the user's past reservation history and provide special treatment to repeat customers. For example, if a user has preferred to reserve a specific seat in the past, that seat can be provided preferentially. The past reservation history is analyzed based on the specific type and usage method of the reservation, such as the reservation date and time and reservation content. Special treatment to repeat customers is based on the provision of benefits and priority reservations. This makes it possible to provide special treatment to repeat customers.

[0040] The reservation reception unit can automatically consider specific requests from users when accepting reservations and respond appropriately. The reservation reception unit can automatically consider specific requests from users when accepting reservations and respond appropriately. For example, it can propose an appropriate menu item taking allergy information into consideration. The specific request is analyzed based on specific types and response methods, such as allergy information or reservations for specific seats. The automatic consideration is based on automatic detection of requests and methods for changing responses. This makes it possible to automatically consider specific requests from users and respond appropriately.

[0041] The reservation reception unit can provide other services simultaneously when accepting a reservation. For example, the reservation reception unit can provide other services simultaneously when accepting a reservation. For example, arranging a taxi at the same time as making a restaurant reservation. The other services are provided based on the specific type and method of provision, such as arranging a taxi or providing information about special events. The simultaneous provision is achieved based on the method of cooperation between multiple services and the processing priority. This makes it possible to provide other services simultaneously when accepting a reservation.

[0042] The reservation reception unit can suggest recommended menu items according to the user's preferences when accepting a reservation. For example, the reservation reception unit suggests recommended menu items according to the user's preferences when accepting a reservation. For example, the recommended menu items may be suggested based on past order history. Recommended menu items are made based on specific types and suggestion methods, such as seasonal menu items or popular menu items. Suggestions according to preferences are made based on past order history and user settings. This makes it possible to suggest recommended menu items according to the user's preferences.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The automated telephone response system can further include a location information acquisition unit that acquires the user's location information. For example, if the user is calling from a specific area, it can provide information about local specialties and events. The location information acquisition unit identifies the user's location based on GPS or IP address. This makes it possible to provide information specific to the area, improving the quality of service provided to users.

[0045] The automated telephone response system can also be equipped with a history learning unit that learns the user's past inquiry history and responds quickly to similar inquiries. For example, if a user has previously inquired about a specific menu item, the system will provide the latest information on that menu item. The history learning unit stores the contents of past inquiries in a database and responds quickly when a similar inquiry is received. This makes it possible to provide personalized responses based on the user's past behavior.

[0046] The automated telephone response system can further include a health estimation unit that analyzes the user's voice data and estimates the user's health condition. For example, if the user's voice is hoarse, it may suggest that the user has a cold. The health estimation unit analyzes the characteristics of the voice data and uses an algorithm to estimate the user's health condition. This makes it possible to provide advice and services tailored to the user's health condition.

[0047] The automated telephone response system can further include a feature analysis unit that analyzes the characteristics of the user's voice and estimates the user's age and gender. For example, the feature analysis unit analyzes the tone and pitch of the user's voice to estimate the user's age and gender. The feature analysis unit uses an algorithm to analyze the characteristics of the voice data and estimate the user's age and gender. This enables personalized responses based on the user's attributes.

[0048] The automated telephone response system may further include a music estimation unit that analyzes the characteristics of the user's voice and estimates the user's preferred music genre. For example, the music estimation unit may analyze the tone and rhythm of the user's voice to estimate the user's preferred music genre. The music estimation unit analyzes the characteristics of the voice data and uses an algorithm to estimate the user's preferred music genre. This allows the system to provide music that matches the user's preferences.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The speech recognition unit recognizes the content of the call. For example, when a call comes in, the speech of the other party is converted into text data. Specifically, the speech recognition technology converts a statement such as "Hello, I'd like to make a reservation" into text. Step 2: The generation unit generates an appropriate response based on the content recognized by the speech recognition unit. For example, it responds in the form of "How many people are you booking for?" or "Please let us know your preferred date and time." The generation AI uses a text generation AI (e.g., LLM) to generate the appropriate response. Step 3: The speech synthesis unit converts the response generated by the generation unit into speech. For example, the speech synthesis technology converts text data such as "How many people are making your reservation?" into speech and conveys it to the other party. Step 4: The reservation reception unit accepts the reservation details using the voice generated by the speech synthesis unit. For example, if the other party says, "I'd like to make a reservation for four people at 7 p.m. tomorrow," the reservation reception unit understands the details and enters them into the reservation system. It also notifies the other party that the reservation has been completed.

[0051] (Example 2) The automated telephone response system according to an embodiment of the present invention is a system that automatically handles telephone calls from restaurants and other establishments. In this system, a generation AI recognizes the content of the call and responds appropriately based on that content. As a result, the automated telephone response system can improve the efficiency of telephone responses from restaurants and other establishments, and in particular automate the handling of reservations.

[0052] An automated telephone response system according to an embodiment includes a speech recognition unit, a generation unit, a speech synthesis unit, and a reservation reception unit. The speech recognition unit recognizes the content of a call. For example, when a call comes in, the speech recognition unit converts the speech of the caller into text data. For example, a speech such as "Hello, I'd like to make a reservation" is converted into text using speech recognition technology. The generation unit generates an appropriate response based on the content recognized by the speech recognition unit. For example, the generation unit responds in the form of "How many people are you booking for?" or "What date and time would you like?" The generation AI generates the appropriate response using a text generation AI (e.g., LLM). The speech synthesis unit converts the response generated by the generation unit into speech. For example, the speech synthesis unit converts the text data "How many people are you booking for?" into speech using speech synthesis technology, and conveys this to the caller. The reservation reception unit accepts the reservation details using the speech generated by the speech synthesis unit. For example, if the caller says, "I'd like to make a reservation for four people tomorrow at 7 p.m.", the reservation reception unit understands this content and enters it into the reservation system. The caller is also notified that the reservation has been completed. As a result, the automated telephone response system according to the embodiment can improve the efficiency of telephone responses at restaurants and other establishments, and can automate the process of accepting reservations in particular. For example, reservations can be accepted quickly even during busy times, reducing the burden on staff. It can also reduce mistakes in confirming reservation details.

[0053] The speech recognition unit incorporates an emotion estimation function into its speech recognition technology, allowing it to analyze the user's emotional state in real time and respond accordingly. For example, the speech recognition unit analyzes the content of the user's speech and uses the emotion estimation function to grasp the emotional state in real time. For example, if the user is angry, it instructs the generation AI to respond calmly. The emotion estimation function is realized using technologies such as voice tone analysis and facial expression recognition. The emotional state is analyzed based on specific types and classification methods, such as joy, anger, and sadness. Real-time analysis is performed taking into account the acceptable range of latency and processing speed. Responses are based on response patterns and response changes according to the emotion. This makes it possible to respond according to the user's emotions.

[0054] The speech recognition unit can make the speech recognition technology multilingual, enabling it to accommodate users of different languages. The speech recognition unit, for example, makes the speech recognition technology multilingual, enabling it to accommodate users of different languages. For example, it can recognize multiple languages, such as English, French, and Chinese. Multilingual support is achieved based on the types of languages ​​that can be supported and translation algorithms. Different languages ​​are analyzed based on specific types and support methods, such as English, Chinese, and Spanish. Support is based on automatic language detection and translation accuracy. This enables multilingual support, enabling it to accommodate users of different languages.

[0055] The voice recognition unit can use voice recognition technology to automatically remove background sounds and noise and provide clear voice data to the generation AI. The voice recognition unit can, for example, use voice recognition technology to automatically remove background sounds and noise and provide clear voice data to the generation AI. For example, it can filter out ambient noise and extract only the user's speech. Background sounds are analyzed based on specific types and removal methods, such as environmental sounds and noise. Noise is analyzed based on specific types and removal methods, such as white noise and echo. Clear voice data is generated based on sound quality evaluation criteria and filtering methods. This allows background sounds and noise to be removed and clear voice data to be provided.

[0056] The generation unit incorporates an emotion estimation function into the generation AI, and can generate responses that correspond to the user's emotions. For example, the generation unit incorporates an emotion estimation function into the generation AI and generates responses that correspond to the user's emotions. For example, if the user is angry, the response will be in a calm and collected tone. The emotion estimation function is realized using technologies such as voice tone analysis and facial expression recognition. Emotions are analyzed based on specific types and classification methods, such as joy, anger, and sadness. Responses are based on pre-set response patterns or dynamic generation by the AI. This makes it possible to generate responses that correspond to the user's emotions.

[0057] The generation unit can have the generation AI learn past reservation data and generate responses based on the user's past behavioral patterns. For example, the generation unit can have the generation AI learn past reservation data and generate responses based on the user's past behavioral patterns. For example, special treatment can be given to repeat customers. Past reservation data is analyzed based on specific types and usage methods, such as reservation date and time and reservation content. Behavior patterns are analyzed based on frequency analysis and pattern recognition algorithms. Responses are generated based on past behavioral patterns. This makes it possible to generate responses based on past behavioral patterns.

[0058] The generation unit allows the generation AI to retain a dialogue history and generate consistent responses in successive dialogues. The generation unit, for example, allows the generation AI to retain a dialogue history and generate consistent responses in successive dialogues. For example, referencing the details of a previous reservation and confirming the same details. The dialogue history is saved based on the database structure and the history storage period. Successive dialogues are analyzed based on dialogue intervals and consistency evaluation criteria. Consistent responses are generated based on dialogue context analysis and response consistency. This makes it possible to have consistent responses in successive dialogues.

[0059] The generation unit can use generation AI to enable responses to inquiries other than reservations. The generation unit, for example, uses generation AI to enable responses to inquiries other than reservations. For example, detailed explanations are provided for questions about the menu. Inquiries other than reservations are analyzed based on their specific type and response method, such as questions about the menu or confirmation of business hours. Whether a response can be made is determined based on the automatic classification of the inquiry and the method of generating a response. This makes it possible to respond to inquiries other than reservations.

[0060] The generation unit can use the generation AI to flexibly respond to specific user requests. The generation unit, for example, uses the generation AI to flexibly respond to specific user requests. For example, when a specific seat is reserved, it checks whether the seat is available. The specific request is analyzed based on the specific type and response method, such as allergy information or the reservation of a specific seat. The flexible response is based on the priority of the request and the method of changing the response. This makes it possible to flexibly respond to specific user requests.

[0061] The generation unit uses the emotion estimation function to adjust the tone and content of the response according to the user's emotion, thereby enabling a more friendly response. For example, the generation unit uses the emotion estimation function to adjust the tone and content of the response according to the user's emotion, thereby enabling a more friendly response. For example, if the user is nervous, the response will be in a tone that relaxes the user. The tone and content are adjusted based on the pitch of the voice and the style of the response. A friendly response is achieved based on friendly language and tone adjustment according to the emotion. This makes it possible to provide a friendly response according to the user's emotion.

[0062] The speech synthesis unit incorporates an emotion estimation function into speech synthesis technology, and can generate speech with a tone and intonation that corresponds to the user's emotion. The speech synthesis unit, for example, incorporates an emotion estimation function into speech synthesis technology, and generates speech with a tone and intonation that corresponds to the user's emotion. For example, if the user is happy, speech is generated with a bright and cheerful tone. The tone and intonation are determined by adjusting the pitch and rhythm of the speech. The speech synthesis technology is realized based on text-to-speech synthesis and waveform generation technology. This makes it possible to generate speech with a tone and intonation that corresponds to the user's emotion.

[0063] The voice synthesis unit uses voice synthesis technology to generate characters with different voices and respond in a voice that suits the user's preferences. The voice synthesis unit, for example, uses voice synthesis technology to generate characters with different voices and respond in a voice that suits the user's preferences. For example, it is possible to select a male voice, a female voice, a young voice, an elderly voice, etc. Characters with different voices are generated based on specific types and generation methods, such as male voices, female voices, and child voices. A voice that suits the user's preferences is selected based on the user's settings and past selection history. This allows the user to respond in a voice that suits the user's preferences.

[0064] The speech synthesis unit can use speech synthesis technology to translate the user's speech in real time and generate speech that supports multiple languages. The speech synthesis unit can, for example, use speech synthesis technology to translate the user's speech in real time and generate speech that supports multiple languages. For example, speech in Japanese can be translated into English to generate speech. The speech is analyzed based on a speech recognition algorithm and text analysis. Real-time translation is achieved based on a translation algorithm and an allowable delay time range. This allows the user's speech to be translated in real time and speech that supports multiple languages ​​to be generated.

[0065] The speech synthesis unit can use speech synthesis technology to summarize the content of a user's utterance and generate a concise response. The speech synthesis unit can use, for example, speech synthesis technology to summarize the content of a user's utterance and generate a concise response. For example, it can respond by summarizing a long explanation in a short way. The summary is performed based on extraction of important information and a summarization algorithm. The concise response is generated based on the length of the response and the comprehensiveness of the information. This makes it possible to summarize the content of a user's utterance and generate a concise response.

[0066] The speech synthesis unit uses the emotion estimation function to adjust the speed and rhythm of the speech according to the user's emotion, thereby enabling a more natural dialogue. The speech synthesis unit, for example, uses the emotion estimation function to adjust the speed and rhythm of the speech according to the user's emotion, thereby enabling a more natural dialogue. For example, if the user is in a hurry, the response speed is increased. The speed and rhythm of the speech are adjusted based on the tempo and rhythm pattern of the speech. A natural dialogue is achieved based on the flow of the dialogue and the naturalness of the responses. In this way, the speed and rhythm of the speech can be adjusted according to the user's emotion, thereby enabling a more natural dialogue.

[0067] The reservation acceptance unit can use the emotion estimation function when accepting a reservation to generate a reservation confirmation message that corresponds to the user's emotion. For example, the reservation acceptance unit uses the emotion estimation function when accepting a reservation to generate a reservation confirmation message that corresponds to the user's emotion. For example, if the user is happy, the reservation confirmation message is generated in a bright tone. The reservation confirmation message is generated based on confirmation of the reservation details and provision of additional information. In this way, a reservation confirmation message that corresponds to the user's emotion can be generated.

[0068] The reservation reception unit can refer to the user's past reservation history when accepting a reservation and provide special treatment to repeat customers. For example, when accepting a reservation, the reservation reception unit can refer to the user's past reservation history and provide special treatment to repeat customers. For example, if a user has preferred to reserve a specific seat in the past, that seat can be provided preferentially. The past reservation history is analyzed based on the specific type and usage method of the reservation, such as the reservation date and time and reservation content. Special treatment to repeat customers is based on the provision of benefits and priority reservations. This makes it possible to provide special treatment to repeat customers.

[0069] The reservation reception unit can automatically consider specific requests from users when accepting reservations and respond appropriately. The reservation reception unit can automatically consider specific requests from users when accepting reservations and respond appropriately. For example, it can propose an appropriate menu item taking allergy information into consideration. The specific request is analyzed based on specific types and response methods, such as allergy information or reservations for specific seats. The automatic consideration is based on automatic detection of requests and methods for changing responses. This makes it possible to automatically consider specific requests from users and respond appropriately.

[0070] The reservation reception unit can provide other services simultaneously when accepting a reservation. For example, the reservation reception unit can provide other services simultaneously when accepting a reservation. For example, arranging a taxi at the same time as making a restaurant reservation. The other services are provided based on the specific type and method of provision, such as arranging a taxi or providing information about special events. The simultaneous provision is achieved based on the method of cooperation between multiple services and the processing priority. This makes it possible to provide other services simultaneously when accepting a reservation.

[0071] The reservation reception unit can suggest recommended menu items according to the user's preferences when accepting a reservation. For example, the reservation reception unit suggests recommended menu items according to the user's preferences when accepting a reservation. For example, the recommended menu items may be suggested based on past order history. Recommended menu items are made based on specific types and suggestion methods, such as seasonal menu items or popular menu items. Suggestions according to preferences are made based on past order history and user settings. This makes it possible to suggest recommended menu items according to the user's preferences.

[0072] The reservation reception unit can use the emotion estimation function to propose special services according to the user's emotions when accepting a reservation. The reservation reception unit, for example, uses the emotion estimation function to propose special services according to the user's emotions when accepting a reservation. For example, if the user is happy, a birthday surprise is proposed. The special service is made based on a specific type and method of provision, such as a birthday surprise or information about a special event. The proposal according to the emotion is made based on a method for classifying emotions and a method for changing the proposal. In this way, it is possible to propose special services according to the user's emotions.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The automated telephone response system can further include a location information acquisition unit that acquires the user's location information. For example, if the user is calling from a specific area, it can provide information about local specialties and events. The location information acquisition unit identifies the user's location based on GPS or IP address. This makes it possible to provide information specific to the area, improving the quality of service provided to users.

[0075] The automated telephone response system may further include a music providing unit that estimates the user's emotions and provides appropriate music based on the estimated emotions. For example, if the user is feeling stressed, relaxing music may be provided. The music providing unit uses the emotion estimation function to analyze the user's emotions and selects music that corresponds to those emotions. This allows the system to provide music that matches the user's emotions, providing a more comfortable experience.

[0076] The automated telephone response system can also be equipped with a history learning unit that learns the user's past inquiry history and responds quickly to similar inquiries. For example, if a user has previously inquired about a specific menu item, the system will provide the latest information on that menu item. The history learning unit stores the contents of past inquiries in a database and responds quickly when a similar inquiry is received. This makes it possible to provide personalized responses based on the user's past behavior.

[0077] The automated telephone response system may further include an advice providing unit that estimates the user's emotions and provides appropriate advice based on the estimated emotions. For example, if the user is feeling anxious, the advice providing unit provides reassuring advice. The advice providing unit uses the emotion estimation function to analyze the user's emotions and generates advice according to those emotions. This allows the system to provide appropriate advice according to the user's emotions, thereby improving user satisfaction.

[0078] The automated telephone response system can further include a health estimation unit that analyzes the user's voice data and estimates the user's health condition. For example, if the user's voice is hoarse, it may suggest that the user has a cold. The health estimation unit analyzes the characteristics of the voice data and uses an algorithm to estimate the user's health condition. This makes it possible to provide advice and services tailored to the user's health condition.

[0079] The automated telephone response system may further include a coupon provision unit that estimates the user's emotions and provides appropriate coupons based on the estimated emotions. For example, if the user is happy, a special discount coupon may be provided. The coupon provision unit uses the emotion estimation function to analyze the user's emotions and selects a coupon that corresponds to the emotion. This allows the provision of benefits that correspond to the user's emotions, improving customer satisfaction.

[0080] The automated telephone response system can further include a feature analysis unit that analyzes the characteristics of the user's voice and estimates the user's age and gender. For example, the feature analysis unit analyzes the tone and pitch of the user's voice to estimate the user's age and gender. The feature analysis unit uses an algorithm to analyze the characteristics of the voice data and estimate the user's age and gender. This enables personalized responses based on the user's attributes.

[0081] The automated telephone response system may further include a reminder providing unit that estimates the user's emotions and provides appropriate reminders based on the estimated emotions. For example, if the user is busy, the reminder providing unit may provide a reminder for an important appointment. The reminder providing unit uses the emotion estimation function to analyze the user's emotions and generate a reminder according to the emotions. This provides appropriate reminders according to the user's emotions, improving user convenience.

[0082] The automated telephone response system may further include a music estimation unit that analyzes the characteristics of the user's voice and estimates the user's preferred music genre. For example, the music estimation unit may analyze the tone and rhythm of the user's voice to estimate the user's preferred music genre. The music estimation unit analyzes the characteristics of the voice data and uses an algorithm to estimate the user's preferred music genre. This allows the system to provide music that matches the user's preferences.

[0083] The automated telephone response system may further include a feedback providing unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. For example, if the user is dissatisfied, the feedback providing unit provides feedback on areas for improvement. The feedback providing unit uses the emotion estimation function to analyze the user's emotions and generates feedback according to the emotions. This allows the system to provide appropriate feedback according to the user's emotions and improve the quality of service.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The speech recognition unit recognizes the content of the call. For example, when a call comes in, the speech of the other party is converted into text data. Specifically, the speech recognition technology converts a statement such as "Hello, I'd like to make a reservation" into text. Step 2: The generation unit generates an appropriate response based on the content recognized by the speech recognition unit. For example, it responds in the form of "How many people are you booking for?" or "Please let us know your preferred date and time." The generation AI uses a text generation AI (e.g., LLM) to generate the appropriate response. Step 3: The speech synthesis unit converts the response generated by the generation unit into speech. For example, the speech synthesis technology converts text data such as "How many people are making your reservation?" into speech and conveys it to the other party. Step 4: The reservation reception unit accepts the reservation details using the voice generated by the speech synthesis unit. For example, if the other party says, "I'd like to make a reservation for four people at 7 p.m. tomorrow," the reservation reception unit understands the details and enters them into the reservation system. It also notifies the other party that the reservation has been completed.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 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 voice recognition unit that recognizes the contents of a call; a generation unit that generates an appropriate response based on the content recognized by the speech recognition unit; a voice synthesis unit that converts the response generated by the generation unit into voice; a reservation receiving unit that receives reservation details using the voice generated by the voice synthesis unit; A system characterized by:

2. The voice recognition unit Incorporating emotion estimation functionality into speech recognition technology to analyze the user's emotional state in real time and respond accordingly 2. The system of claim 1.

3. The generation unit Incorporating an emotion estimation function into the generation AI will generate responses that correspond to the user's emotions.

2. The system of claim 1.

4. The speech synthesis unit Incorporating emotion estimation functionality into speech synthesis technology to generate speech with a tone and intonation that matches the user's emotions 2. The system of claim 1.

5. The reservation reception unit When accepting a reservation, use emotion estimation function to generate a reservation confirmation message according to the user's emotion.

2. The system of claim 1.

6. The voice recognition unit Make speech recognition technology multilingual to accommodate users of different languages 2. The system of claim 1.

7. The generation unit The generation AI learns past reservation data and generates responses based on the user's past behavioral patterns.

2. The system of claim 1.

8. The speech synthesis unit Using speech synthesis technology, the content of a user's speech is translated in real time to generate multilingual speech.

2. The system of claim 1.

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