system

Abstract

The system according to this embodiment aims to efficiently process inquiry content. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a calling unit, and a summarization unit. The reception unit receives the inquiry details. The analysis unit analyzes the information received by the reception unit. The calling unit makes a phone call based on the information analyzed by the analysis unit. The summarization unit summarizes the information obtained by the calling unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is time-consuming and difficult to efficiently process the contents of inquiries.

[0005] The system according to the embodiment aims to efficiently process the contents of inquiries.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a transmission unit, and a summarization unit. The reception unit inputs the contents of an inquiry. The analysis unit analyzes the information received by the reception unit. The transmission unit makes a call based on the information analyzed by the analysis unit. The summarization unit summarizes the information obtained by the transmission unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently process inquiry content. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a "silent" telephone agent connected via a smartphone. This system works by having the user input an inquiry via their smartphone, which is then analyzed by an AI. The AI ​​then makes a phone call to obtain the necessary information, summarizes the information, and notifies the user. For example, the user inputs an inquiry via their smartphone. The user can choose either text input or voice input. For example, the user might input something like, "I would like to make a reservation for a special course for two people at 7 PM on December 15th." This information is sent to the AI. Next, the AI ​​analyzes the input and makes a phone call to obtain the necessary information. The AI ​​engages in natural conversation and obtains information relevant to the inquiry. For example, the AI ​​might call a restaurant to make a reservation, including allergy information and time adjustments. In this case, the AI ​​uses generated voice to make the call using the user's voice. Finally, the AI ​​summarizes the information obtained and notifies the user. The AI ​​transcribes the information obtained by phone, summarizes it, and notifies the user. For example, it might notify the user with something like, "Your reservation is complete. It is for 6:30 PM on December 15th. Your request to change the main dish has also been accepted." This system eliminates the need for users to make phone calls and allows them to easily obtain the information they need. Furthermore, it enables efficient information acquisition without the stress of phone calls. For example, it can be used for various purposes such as restaurant reservations, checking product inventory, and making inquiries. Additionally, by having the AI ​​make the calls, it prevents interruptions to business operations caused by phone calls, allowing employees to focus on their core tasks. Thus, the system according to this embodiment eliminates the need for users to make phone calls and allows them to easily obtain the information they need.

[0029] The system according to this embodiment comprises a reception unit, an analysis unit, a calling unit, and a summarization unit. The reception unit receives the user's inquiry. When the user enters the inquiry, they can choose either text input or voice input. For example, the user can enter something like, "I would like to make a reservation for a special course for two people at 7 PM on December 15th." The reception unit transmits the entered information to the analysis unit. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the entered information using, for example, text analysis or voice analysis. The analysis unit transmits the analysis results to the calling unit. The calling unit makes a phone call based on the information analyzed by the analysis unit. The calling unit makes a phone call using, for example, generated voice, in the user's voice. The calling unit makes the phone call and obtains the necessary information. For example, the calling unit calls a restaurant to make a reservation and obtains allergy information and adjusts the time. The calling unit transmits the obtained information to the summarization unit. The summarization unit summarizes the information obtained by the calling unit. The summarization unit has, for example, AI transcription and summarization functions. The summarization unit transcribes and summarizes the acquired information and notifies the user. For example, it might notify the user with a message such as, "Your reservation is complete. You will be seated at 6:30 PM on December 15th. Your request to change your main course has also been accepted." This allows the system according to the embodiment to eliminate the need for the user to make a phone call and easily obtain the necessary information.

[0030] The reception desk receives the user's inquiry. Users can choose to enter their inquiry details using either text input or voice input. For example, a user might enter something like, "I would like to make a reservation for a special course for two people at 7 PM on December 15th." The reception desk then sends the entered information to the analysis department. Specifically, users access a dedicated application or website using their smartphone or computer and enter their inquiry details. For text input, the user uses a keyboard to type text; for voice input, the user uses a microphone to record their voice. Voice input utilizes speech recognition technology to convert the user's voice into text. The reception desk sends the entered information to the analysis department in real time, so the analysis department receives the information as soon as the user completes the input. Furthermore, the reception desk has a function to check the user's input and prompt them to correct or add information as needed. For example, if a user enters incomplete information, the reception desk will display a message such as, "Reservation date and time are unknown. Please enter again," prompting the user to re-enter the information. In this way, the reception desk can support users in entering accurate and complete information, improving the overall accuracy and reliability of the system.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the input information using methods such as text analysis and speech analysis. Specifically, in text analysis, natural language processing technology is used to understand the meaning of the text entered by the user and extract the necessary information. For example, from the text "I would like to make a reservation for the special course for 2 people at 7 PM on December 15th," information such as the reservation date and time, number of people, and course name is extracted. In speech analysis, speech recognition technology is used to convert the user's voice into text, and then text analysis is performed. The analysis unit sends the analysis results to the transmission unit. The analysis unit uses AI to implement advanced algorithms to accurately understand the user's intent and extract the necessary information. For example, machine learning models are used to classify the user's input and perform appropriate processing. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and user history. For example, it analyzes the current input by referring to reservation details and inquiries made by the same user in the past. This allows the analysis unit to understand the user's intent more accurately and provide appropriate information to the transmission unit.

[0032] The calling unit makes phone calls based on information analyzed by the analysis unit. For example, the calling unit uses generated voice to make calls using the user's voice. Specifically, based on the information received from the analysis unit, the calling unit automatically dials the phone number of the reservation destination and conveys the reservation details using generated voice. The generated voice uses technology to produce a voice that resembles the user's voice, enabling natural conversation. The calling unit makes phone calls and obtains necessary information. For example, the calling unit calls a restaurant to make a reservation and obtains information such as allergy details and time adjustments. The calling unit sends the obtained information to the summarization unit. The calling unit uses AI to analyze the content of the phone conversation in real time and extract the necessary information. For example, if the restaurant staff member responds, "The reservation is for 7 PM, and there is no particular allergy information," the calling unit accurately extracts this information and sends it to the summarization unit. Furthermore, the calling unit also has a function to record the content of the phone conversation so that it can be reviewed later. This allows the calling unit to make phone calls on behalf of the user and efficiently obtain the necessary information.

[0033] The summarization unit summarizes the information obtained by the transmission unit. The summarization unit has functions such as AI transcription and summarization. Specifically, the summarization unit converts the conversation content received from the transmission unit into text, extracts important information, and summarizes it. For example, it might notify the user with content such as, "Your reservation is complete. You will be seated at 6:30 PM on December 15th. Your change of main course has also been accepted." The summarization unit uses AI to automatically analyze the conversation content and implements advanced algorithms to extract important information. For example, it uses natural language processing technology to extract important keywords and phrases from the conversation content and creates a summary based on them. Furthermore, the summarization unit can customize the format and content of the summary according to the user's preferences. For example, if the user wants detailed information, it will provide a more detailed summary, and if they want concise information, it will provide a concise summary. In this way, the summarization unit can provide users with appropriate information and improve the overall usability of the system.

[0034] The calling unit can make phone calls using the user's voice with generated speech. For example, the calling unit can generate the user's voice using speech synthesis technology and make phone calls using that generated speech. For example, the calling unit can make phone calls to restaurants to make reservations using generated speech that mimics the user's voice. The calling unit can also use generated speech to check product inventory using the user's voice. Furthermore, the calling unit can use generated speech to make inquiries using the user's voice. This enables natural conversation by making phone calls using the user's voice. Some or all of the above processing in the calling unit may be performed using AI, for example, or without AI. For example, the calling unit can input a sample of the user's voice into a generating AI, and the generating AI can output generated speech that mimics the user's voice.

[0035] The summarization unit has AI transcription and summarization functions. The summarization unit transcribes information acquired by the transmission unit, for example, using speech recognition technology. The summarization unit summarizes the transcribed information. For example, the summarization unit summarizes the acquired information using natural language processing technology. The summarization unit concisely summarizes the acquired information and notifies the user. For example, the summarization unit notifies the user with content such as, "Your reservation is complete. You will be seated from 6:30 PM on December 15th. Your change of main course has also been accepted." This allows the acquired information to be efficiently summarized by the AI ​​transcription and summarization functions. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the audio data acquired by the transmission unit into a generating AI, which can perform transcription and summarization.

[0036] The reception desk allows users to choose between text input and voice input. For example, the reception desk allows users to input text using a keyboard. Alternatively, the reception desk allows users to input voice using a microphone. For example, the reception desk can use speech recognition technology to convert the user's voice into text. Furthermore, the reception desk allows users to input text using a smartphone's touchscreen. This improves convenience by allowing users to choose between text input and voice input. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's voice data into a generating AI, which can then convert the voice data into text.

[0037] The analysis unit can obtain information corresponding to the content of the inquiry. For example, the analysis unit analyzes the content of the inquiry entered by the user using text analysis technology. Based on the analysis results, the analysis unit obtains appropriate information. For example, the analysis unit can refer to an FAQ database to obtain information related to the content of the inquiry. The analysis unit can also obtain information related to the content of the inquiry using data mining technology. Furthermore, the analysis unit can analyze the content of the inquiry entered by voice using voice analysis technology and obtain appropriate information. In this way, appropriate information can be provided by obtaining information corresponding to the content of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data entered by the user into a generating AI, and the generating AI can obtain appropriate information.

[0038] The reception desk can analyze a user's past inquiry history and suggest the optimal input format. For example, the reception desk might use data mining techniques to analyze the user's past inquiry history. Based on the analysis results, the reception desk will suggest the optimal input format. For example, it might automatically customize the input format based on the type of inquiry the user frequently made in the past. It could also extract specific patterns from the user's past inquiry history and suggest the optimal input format. Furthermore, it could prioritize suggesting input methods the user has used in the past (voice, text, etc.). This allows the reception desk to provide the optimal input format by analyzing past inquiry history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk could input the user's past inquiry data into a generating AI, which could then suggest the optimal input format.

[0039] The reception desk can automatically complete input content based on the user's current situation and environment during input. For example, if the user is on the move, the reception desk can automatically acquire the user's current location and set it as the starting point. Furthermore, if the user is participating in a specific event, the reception desk can automatically complete information related to that event. In addition, the reception desk can predict the type of inquiry a user might make during a specific time period and automatically complete it. This reduces the effort required for input by automatically completing input content based on the user's situation and environment. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current location data into a generating AI, which can then automatically complete the input content.

[0040] The reception desk can prioritize inputting highly relevant information based on the user's geographical location during input. For example, the reception desk can obtain the user's current location using GPS data. Based on the acquired geographical location information, the reception desk prioritizes inputting highly relevant information. For example, if the user is in a specific region, the reception desk can prompt the user to prioritize inputting inquiries related to that region. Also, if the user is traveling, the reception desk can prompt the user to prioritize inputting information related to their travel destination. Furthermore, if the user is in a specific facility, the reception desk can prompt the user to prioritize inputting information related to that facility. This allows for the provision of appropriate information by prioritizing the input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's GPS data into a generating AI, which can then prioritize inputting highly relevant information.

[0041] The reception desk can analyze the user's social media activity during input and automatically input relevant information. For example, the reception desk can analyze the user's social media activity using data mining techniques. Based on the analysis results, the reception desk automatically inputs relevant information. For example, it can automatically complete the inquiry based on information the user has shared on social media. It can also automatically input relevant inquiry information based on information about accounts the user follows on social media. Furthermore, it can automatically input inquiry information based on information about events the user has participated in on social media. In this way, relevant information can be automatically input by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI, and the generating AI can automatically input relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry content during the analysis. The analysis unit evaluates the importance of the inquiry content using, for example, text analysis technology. The analysis unit adjusts the level of detail of the analysis based on the evaluation results. For example, for important inquiries, the analysis unit performs a detailed analysis to provide accurate information. For general inquiries, the analysis unit can perform a rapid analysis to provide basic information. Furthermore, for urgent inquiries, the analysis unit can perform a rapid and detailed analysis to provide results immediately. In this way, appropriate information can be provided by adjusting the level of detail of the analysis based on the importance of the inquiry content. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the text data of the inquiry content into a generating AI, which can evaluate the importance and adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit classifies the category of the inquiry using data mining techniques. Based on the classification results, the analysis unit applies an appropriate analysis algorithm. For example, for an inquiry about restaurant reservations, the analysis unit applies an analysis algorithm specialized for reservation systems. For an inquiry about checking product inventory, the analysis unit can apply an analysis algorithm specialized for inventory management systems. Furthermore, for an inquiry about how to use a service, the analysis unit can apply an analysis algorithm specialized for user guides. In this way, appropriate information can be provided by applying different analysis algorithms depending on the category of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data of the inquiry into a generating AI, which can classify the category and apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the submission date of the inquiry during the analysis process. For example, the analysis unit evaluates the submission date of the inquiry based on the submission date and time. Based on the evaluation results, the analysis unit determines the priority of analysis. For example, for urgent inquiries, the analysis unit performs the analysis with the highest priority. For regular inquiries, the analysis unit can perform the analysis with the normal priority. Furthermore, for past inquiries, the analysis unit can postpone the analysis. In this way, appropriate information can be provided by determining the priority of analysis based on the submission date of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date and time data of the inquiry into a generating AI, which can evaluate the submission date and time and determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the query content during analysis. The analysis unit evaluates the relevance of the query content, for example, using data mining techniques. Based on the evaluation results, the analysis unit adjusts the order of analysis. For example, the analysis unit prioritizes analyzing highly relevant query content. Conversely, it can postpone the analysis of less relevant query content. Furthermore, if multiple query content is related, the analysis unit can analyze them together. By adjusting the order of analysis based on the relevance of the query content, appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input query data into a generating AI, which can evaluate the relevance and adjust the order of analysis.

[0046] The calling unit can customize the content of a call by considering the attribute information of the caller. The calling unit can acquire the attribute information of the caller, for example, using data mining technology. Based on the acquired attribute information, the calling unit customizes the content of the call. For example, if the caller is a restaurant, the calling unit can prioritize acquiring information related to reservations. If the caller is a retail store, the calling unit can prioritize acquiring information related to product inventory. Furthermore, if the caller is a service provider, the calling unit can prioritize acquiring information on how to use the service. In this way, appropriate information can be provided by considering the attribute information of the caller. Some or all of the above processing in the calling unit may be performed using AI, for example, or without AI. For example, the calling unit can input the attribute data of the caller into a generating AI, and the generating AI can customize the content of the call.

[0047] The calling unit can select the optimal calling method by referring to past calling history when making a call. The calling unit can analyze past calling history using, for example, data mining technology. Based on the analysis results, the calling unit selects the optimal calling method. For example, based on successful calling methods in the past, the calling unit can select the optimal calling method. In addition, based on past calling history, the calling unit can analyze the recipient's response patterns and select the optimal calling method. Furthermore, based on past calling history, the calling unit can also select a calling method that suits the recipient's attributes. In this way, the optimal calling method can be selected by referring to past calling history. Some or all of the above processing in the calling unit may be performed using, for example, AI, or not using AI. For example, the calling unit can input past calling history data into a generating AI, and the generating AI can select the optimal calling method.

[0048] The calling unit can adjust the content of a call considering the geographical location of the caller. The calling unit can acquire the geographical location of the caller, for example, using data mining technology. Based on the acquired geographical location information, the calling unit adjusts the content of the call. For example, if the caller is in a specific region, the calling unit prioritizes acquiring information related to that region. Also, if the caller is traveling, the calling unit can prioritize acquiring information related to their travel destination. Furthermore, if the caller is in a specific facility, the calling unit can prioritize acquiring information related to that facility. In this way, appropriate information can be provided by considering the geographical location of the caller. Some or all of the above processing in the calling unit may be performed using AI, for example, or without AI. For example, the calling unit can input the geographical location data of the caller into a generating AI, and the generating AI can adjust the content of the call.

[0049] The calling unit can analyze the recipient's social media activity when making a call and reflect relevant information in the message. For example, the calling unit can use data mining techniques to analyze the recipient's social media activity. Based on the analysis results, the calling unit reflects relevant information in the message. For example, it can customize the message based on information the recipient has shared on social media. It can also adjust the message based on information about accounts the recipient follows on social media. Furthermore, it can adjust the message based on information about events the recipient is participating in on social media. In this way, by analyzing the recipient's social media activity, relevant information can be provided. Some or all of the above processing in the calling unit may be performed using AI, for example, or without AI. For example, the calling unit can input the recipient's social media data into a generating AI, which can then reflect relevant information in the message.

[0050] The summarization unit can adjust the level of detail in the summary based on the importance of the information acquired. The summarization unit can evaluate the importance of the acquired information, for example, using data mining techniques. Based on the evaluation results, the summarization unit adjusts the level of detail in the summary. For example, for important information, the summarization unit provides a detailed summary. For general information, the summarization unit can provide a concise summary. Furthermore, for urgent information, the summarization unit can provide a quick summary that gets straight to the point. In this way, by adjusting the level of detail in the summary based on the importance of the acquired information, appropriate information can be provided. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the data of the acquired information into a generating AI, which can evaluate the importance and adjust the level of detail in the summary.

[0051] The summarization unit can apply different summarization algorithms depending on the category of the information acquired during summarization. For example, the summarization unit classifies the category of the acquired information using data mining techniques. Based on the classification results, the summarization unit applies an appropriate summarization algorithm. For example, for information regarding restaurant reservations, the summarization unit can apply a summarization algorithm specialized for reservation systems. Similarly, for information regarding product inventory checks, the summarization unit can apply a summarization algorithm specialized for inventory management systems. Furthermore, for information regarding how to use a service, the summarization unit can apply a summarization algorithm specialized for user guides. This allows for the provision of appropriate information by applying different summarization algorithms depending on the category of the acquired information. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the acquired information data into a generating AI, which can classify the categories and apply an appropriate summarization algorithm.

[0052] The summarization unit can determine the priority of summarization based on the submission date of the acquired information. For example, the summarization unit evaluates the submission date of the acquired information based on the submission date and time. Based on the evaluation result, the summarization unit determines the priority of summarization. For example, for urgent information, the summarization unit will summarize it with the highest priority. For regular information, the summarization unit can summarize it with the normal priority. Furthermore, for past information, the summarization unit can postpone summarization. In this way, appropriate information can be provided by determining the priority of summarization based on the submission date of the acquired information. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the submission date and time data of the acquired information into a generating AI, which can evaluate the submission date and time and determine the priority of summarization.

[0053] The summarization unit can adjust the order of summaries based on the relevance of the acquired information during the summarization process. The summarization unit evaluates the relevance of the acquired information, for example, using data mining techniques. Based on the evaluation results, the summarization unit adjusts the order of summaries. For example, the summarization unit prioritizes summarizing highly relevant information. Conversely, it can postpone summarizing less relevant information. Furthermore, if multiple pieces of information are related, the summarization unit can summarize them together. This allows for the provision of appropriate information by adjusting the order of summaries based on the relevance of the acquired information. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the data of the acquired information into a generating AI, which can evaluate the relevance and adjust the order of summaries.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can automatically categorize inquiries based on user input. For example, it analyzes user input and categorizes it into items such as restaurant reservations, product inventory checks, and service usage instructions. This allows the reception desk to process inquiries appropriately. The reception desk can also prioritize inquiries based on user input. For example, urgent inquiries are given top priority. Furthermore, the reception desk can adjust the level of detail required for inquiries based on user input. For example, inquiries requiring detailed information are prompted for more detailed input. This allows the reception desk to process inquiries appropriately by categorizing them, prioritizing them, and adjusting the level of detail based on user input.

[0056] The summarization unit can evaluate the reliability of the acquired information and adjust the content of the summary based on that reliability. For example, the summarization unit can evaluate the source and reliability of the acquired information and create a summary based on the reliable information. Furthermore, the summarization unit can issue warnings for information with low reliability. In addition, the summarization unit can adjust the level of detail of the summary based on the reliability of the acquired information. For example, it can provide a detailed summary for highly reliable information and a concise summary for unreliable information. This allows the summarization unit to provide appropriate information by evaluating the reliability of the acquired information and adjusting the content of the summary based on that reliability. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the acquired information data into a generating AI, which can then evaluate the reliability and adjust the content of the summary.

[0057] The reception desk can automatically generate inquiry templates based on user input. For example, the reception desk analyzes user input and automatically generates a template corresponding to the inquiry. This allows the reception desk to easily input inquiry details. The reception desk can also customize inquiry templates based on user input. For example, if a user enters specific information, the reception desk customizes the template based on that information. Furthermore, the reception desk can update inquiry templates based on user input. This allows the reception desk to automatically generate, customize, and update inquiry templates based on user input, enabling appropriate processing.

[0058] The calling unit can adjust the timing of a call by considering the business hours of the recipient. For example, the calling unit can obtain the recipient's business hours and make the call within those hours. If the recipient is closed on a holiday, the calling unit can avoid making the call on that day. Furthermore, if the recipient is busy during a specific time period, the calling unit can avoid making the call during that time. In this way, the calling unit can select an appropriate calling timing by considering the recipient's business hours. Some or all of the above processing in the calling unit may be performed using AI, for example, or not. For example, the calling unit can input the recipient's business hours data into a generating AI, which can then adjust the calling timing.

[0059] The analysis unit can select an analysis algorithm based on the language of the inquiry during analysis. For example, if the inquiry is entered in English, the analysis unit can apply an analysis algorithm specialized for English. If the inquiry is entered in Japanese, it can apply an analysis algorithm specialized for Japanese. Furthermore, if the inquiry is entered in multiple languages, the analysis unit can apply an analysis algorithm corresponding to each language. In this way, the analysis unit can provide accurate information by selecting the appropriate analysis algorithm based on the language of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the text data of the inquiry into a generating AI, which can identify the language and select an appropriate analysis algorithm.

[0060] The calling unit can adjust the content of a call considering the cultural background of the recipient. For example, if the recipient is in a different cultural sphere, the calling unit can provide content that is considerate of that culture. Also, if the recipient has specific cultural customs, the calling unit can provide content that is considerate of those customs. Furthermore, if the recipient speaks a specific language, the calling unit can provide content that corresponds to that language. In this way, the calling unit can provide appropriate content by considering the cultural background of the recipient. Some or all of the above processing in the calling unit may be performed using AI, for example, or not. For example, the calling unit can input the recipient's cultural background data into a generating AI, which can then adjust the content of the call.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives the user's inquiry. The user can choose to enter the information via text or voice. For example, they might enter something like, "I would like to make a reservation for the special course for two people at 7 PM on December 15th." The reception desk then sends the entered information to the analysis department. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the input information using text analysis and speech analysis. The analysis unit transmits the analysis results to the transmission unit. Step 3: The calling unit makes a phone call based on the information analyzed by the analysis unit. The calling unit makes a phone call using the user's voice with generated speech. The calling unit makes the phone call and obtains the necessary information. For example, it might call a restaurant to make a reservation and obtain information about allergies and adjust the time. The calling unit then transmits the obtained information to the summarization unit. Step 4: The summarization unit summarizes the information obtained by the transmission unit. The summarization unit has AI transcription and summarization functions, and transcribes and summarizes the obtained information and notifies the user. For example, it might notify the user with content such as, "Your reservation is complete. You will be seated at 6:30 PM on December 15th. Your request to change your main dish has also been accepted."

[0063] (Example of form 2) The system according to an embodiment of the present invention is a "silent" telephone agent connected via a smartphone. This system works by having the user input an inquiry via their smartphone, which is then analyzed by an AI. The AI ​​then makes a phone call to obtain the necessary information, summarizes the information, and notifies the user. For example, the user inputs an inquiry via their smartphone. The user can choose either text input or voice input. For example, the user might input something like, "I would like to make a reservation for a special course for two people at 7 PM on December 15th." This information is sent to the AI. Next, the AI ​​analyzes the input and makes a phone call to obtain the necessary information. The AI ​​engages in natural conversation and obtains information relevant to the inquiry. For example, the AI ​​might call a restaurant to make a reservation, including allergy information and time adjustments. In this case, the AI ​​uses generated voice to make the call using the user's voice. Finally, the AI ​​summarizes the information obtained and notifies the user. The AI ​​transcribes the information obtained by phone, summarizes it, and notifies the user. For example, it might notify the user with something like, "Your reservation is complete. It is for 6:30 PM on December 15th. Your request to change the main dish has also been accepted." This system eliminates the need for users to make phone calls and allows them to easily obtain the information they need. Furthermore, it enables efficient information acquisition without the stress of phone calls. For example, it can be used for various purposes such as restaurant reservations, checking product inventory, and making inquiries. Additionally, by having the AI ​​make the calls, it prevents interruptions to business operations caused by phone calls, allowing employees to focus on their core tasks. Thus, the system according to this embodiment eliminates the need for users to make phone calls and allows them to easily obtain the information they need.

[0064] The system according to this embodiment comprises a reception unit, an analysis unit, a calling unit, and a summarization unit. The reception unit receives the user's inquiry. When the user enters the inquiry, they can choose either text input or voice input. For example, the user can enter something like, "I would like to make a reservation for a special course for two people at 7 PM on December 15th." The reception unit transmits the entered information to the analysis unit. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the entered information using, for example, text analysis or voice analysis. The analysis unit transmits the analysis results to the calling unit. The calling unit makes a phone call based on the information analyzed by the analysis unit. The calling unit makes a phone call using, for example, generated voice, in the user's voice. The calling unit makes the phone call and obtains the necessary information. For example, the calling unit calls a restaurant to make a reservation and obtains allergy information and adjusts the time. The calling unit transmits the obtained information to the summarization unit. The summarization unit summarizes the information obtained by the calling unit. The summarization unit has, for example, AI transcription and summarization functions. The summarization unit transcribes and summarizes the acquired information and notifies the user. For example, it might notify the user with a message such as, "Your reservation is complete. You will be seated at 6:30 PM on December 15th. Your request to change your main course has also been accepted." This allows the system according to the embodiment to eliminate the need for the user to make a phone call and easily obtain the necessary information.

[0065] The reception desk receives the user's inquiry. Users can choose to enter their inquiry details using either text input or voice input. For example, a user might enter something like, "I would like to make a reservation for a special course for two people at 7 PM on December 15th." The reception desk then sends the entered information to the analysis department. Specifically, users access a dedicated application or website using their smartphone or computer and enter their inquiry details. For text input, the user uses a keyboard to type text; for voice input, the user uses a microphone to record their voice. Voice input utilizes speech recognition technology to convert the user's voice into text. The reception desk sends the entered information to the analysis department in real time, so the analysis department receives the information as soon as the user completes the input. Furthermore, the reception desk has a function to check the user's input and prompt them to correct or add information as needed. For example, if a user enters incomplete information, the reception desk will display a message such as, "Reservation date and time are unknown. Please enter again," prompting the user to re-enter the information. In this way, the reception desk can support users in entering accurate and complete information, improving the overall accuracy and reliability of the system.

[0066] The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the input information using methods such as text analysis and speech analysis. Specifically, in text analysis, natural language processing technology is used to understand the meaning of the text entered by the user and extract the necessary information. For example, from the text "I would like to make a reservation for the special course for 2 people at 7 PM on December 15th," information such as the reservation date and time, number of people, and course name is extracted. In speech analysis, speech recognition technology is used to convert the user's voice into text, and then text analysis is performed. The analysis unit sends the analysis results to the transmission unit. The analysis unit uses AI to implement advanced algorithms to accurately understand the user's intent and extract the necessary information. For example, machine learning models are used to classify the user's input and perform appropriate processing. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and user history. For example, it analyzes the current input by referring to reservation details and inquiries made by the same user in the past. This allows the analysis unit to understand the user's intent more accurately and provide appropriate information to the transmission unit.

[0067] The calling unit makes phone calls based on information analyzed by the analysis unit. For example, the calling unit uses generated voice to make calls using the user's voice. Specifically, based on the information received from the analysis unit, the calling unit automatically dials the phone number of the reservation destination and conveys the reservation details using generated voice. The generated voice uses technology to produce a voice that resembles the user's voice, enabling natural conversation. The calling unit makes phone calls and obtains necessary information. For example, the calling unit calls a restaurant to make a reservation and obtains information such as allergy details and time adjustments. The calling unit sends the obtained information to the summarization unit. The calling unit uses AI to analyze the content of the phone conversation in real time and extract the necessary information. For example, if the restaurant staff member responds, "The reservation is for 7 PM, and there is no particular allergy information," the calling unit accurately extracts this information and sends it to the summarization unit. Furthermore, the calling unit also has a function to record the content of the phone conversation so that it can be reviewed later. This allows the calling unit to make phone calls on behalf of the user and efficiently obtain the necessary information.

[0068] The summarization unit summarizes the information obtained by the transmission unit. The summarization unit has functions such as AI transcription and summarization. Specifically, the summarization unit converts the conversation content received from the transmission unit into text, extracts important information, and summarizes it. For example, it might notify the user with content such as, "Your reservation is complete. You will be seated at 6:30 PM on December 15th. Your change of main course has also been accepted." The summarization unit uses AI to automatically analyze the conversation content and implements advanced algorithms to extract important information. For example, it uses natural language processing technology to extract important keywords and phrases from the conversation content and creates a summary based on them. Furthermore, the summarization unit can customize the format and content of the summary according to the user's preferences. For example, if the user wants detailed information, it will provide a more detailed summary, and if they want concise information, it will provide a concise summary. In this way, the summarization unit can provide users with appropriate information and improve the overall usability of the system.

[0069] The calling unit can make phone calls using the user's voice with generated speech. For example, the calling unit can generate the user's voice using speech synthesis technology and make phone calls using that generated speech. For example, the calling unit can make phone calls to restaurants to make reservations using generated speech that mimics the user's voice. The calling unit can also use generated speech to check product inventory using the user's voice. Furthermore, the calling unit can use generated speech to make inquiries using the user's voice. This enables natural conversation by making phone calls using the user's voice. Some or all of the above processing in the calling unit may be performed using AI, for example, or without AI. For example, the calling unit can input a sample of the user's voice into a generating AI, and the generating AI can output generated speech that mimics the user's voice.

[0070] The summarization unit has AI transcription and summarization functions. The summarization unit transcribes information acquired by the transmission unit, for example, using speech recognition technology. The summarization unit summarizes the transcribed information. For example, the summarization unit summarizes the acquired information using natural language processing technology. The summarization unit concisely summarizes the acquired information and notifies the user. For example, the summarization unit notifies the user with content such as, "Your reservation is complete. You will be seated from 6:30 PM on December 15th. Your change of main course has also been accepted." This allows the acquired information to be efficiently summarized by the AI ​​transcription and summarization functions. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the audio data acquired by the transmission unit into a generating AI, which can perform transcription and summarization.

[0071] The reception desk allows users to choose between text input and voice input. For example, the reception desk allows users to input text using a keyboard. Alternatively, the reception desk allows users to input voice using a microphone. For example, the reception desk can use speech recognition technology to convert the user's voice into text. Furthermore, the reception desk allows users to input text using a smartphone's touchscreen. This improves convenience by allowing users to choose between text input and voice input. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's voice data into a generating AI, which can then convert the voice data into text.

[0072] The analysis unit can obtain information corresponding to the content of the inquiry. For example, the analysis unit analyzes the content of the inquiry entered by the user using text analysis technology. Based on the analysis results, the analysis unit obtains appropriate information. For example, the analysis unit can refer to an FAQ database to obtain information related to the content of the inquiry. The analysis unit can also obtain information related to the content of the inquiry using data mining technology. Furthermore, the analysis unit can analyze the content of the inquiry entered by voice using voice analysis technology and obtain appropriate information. In this way, appropriate information can be provided by obtaining information corresponding to the content of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data entered by the user into a generating AI, and the generating AI can obtain appropriate information.

[0073] The reception desk can estimate the user's emotions and automatically select an input method based on the estimated emotions. The reception desk estimates the user's emotions, for example, using voice tone analysis technology. Based on the estimated emotions, the reception desk selects the optimal input method. For example, if the user is stressed, the reception desk prioritizes voice input, allowing the user to enter their inquiry with simple operations. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk presents an input method using templates, allowing the user to enter their inquiry quickly. This improves user convenience by selecting the optimal input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's voice data into a generating AI, which can then estimate the user's emotions and select the optimal input method.

[0074] The reception desk can analyze a user's past inquiry history and suggest the optimal input format. For example, the reception desk might use data mining techniques to analyze the user's past inquiry history. Based on the analysis results, the reception desk will suggest the optimal input format. For example, it might automatically customize the input format based on the type of inquiry the user frequently made in the past. It could also extract specific patterns from the user's past inquiry history and suggest the optimal input format. Furthermore, it could prioritize suggesting input methods the user has used in the past (voice, text, etc.). This allows the reception desk to provide the optimal input format by analyzing past inquiry history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk could input the user's past inquiry data into a generating AI, which could then suggest the optimal input format.

[0075] The reception desk can automatically complete input content based on the user's current situation and environment during input. For example, if the user is on the move, the reception desk can automatically acquire the user's current location and set it as the starting point. Furthermore, if the user is participating in a specific event, the reception desk can automatically complete information related to that event. In addition, the reception desk can predict the type of inquiry a user might make during a specific time period and automatically complete it. This reduces the effort required for input by automatically completing input content based on the user's situation and environment. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current location data into a generating AI, which can then automatically complete the input content.

[0076] The reception desk can estimate the user's emotions and prioritize input content based on the estimated emotions. The reception desk estimates the user's emotions, for example, using voice tone analysis technology. Based on the estimated emotions, the reception desk prioritizes input content. For example, if the user is stressed, the reception desk may prompt the user to prioritize entering important inquiries. If the user is relaxed, the reception desk may prompt the user to enter detailed information. Furthermore, if the user is in a hurry, the reception desk may prompt the user to prioritize entering the most important information. In this way, by prioritizing input content according to the user's emotions, important information can be entered preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's voice data into a generating AI, which can then estimate the user's emotions and determine the priority of the input content.

[0077] The reception desk can prioritize inputting highly relevant information based on the user's geographical location during input. For example, the reception desk can obtain the user's current location using GPS data. Based on the acquired geographical location information, the reception desk prioritizes inputting highly relevant information. For example, if the user is in a specific region, the reception desk can prompt the user to prioritize inputting inquiries related to that region. Also, if the user is traveling, the reception desk can prompt the user to prioritize inputting information related to their travel destination. Furthermore, if the user is in a specific facility, the reception desk can prompt the user to prioritize inputting information related to that facility. This allows for the provision of appropriate information by prioritizing the input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's GPS data into a generating AI, which can then prioritize inputting highly relevant information.

[0078] The reception desk can analyze the user's social media activity during input and automatically input relevant information. For example, the reception desk can analyze the user's social media activity using data mining techniques. Based on the analysis results, the reception desk automatically inputs relevant information. For example, it can automatically complete the inquiry based on information the user has shared on social media. It can also automatically input relevant inquiry information based on information about accounts the user follows on social media. Furthermore, it can automatically input inquiry information based on information about events the user has participated in on social media. In this way, relevant information can be automatically input by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI, and the generating AI can automatically input relevant information.

[0079] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, the analysis unit estimates the user's emotions using voice tone analysis technology. The analysis unit adjusts the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit performs a rapid analysis and provides results quickly. If the user is relaxed, the analysis unit can perform a detailed analysis and provide more information. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing important information and provide results quickly. This allows for the provision of appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's voice data into a generative AI, which can estimate the user's emotions and adjust the analysis method.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry content during the analysis. The analysis unit evaluates the importance of the inquiry content using, for example, text analysis technology. The analysis unit adjusts the level of detail of the analysis based on the evaluation results. For example, for important inquiries, the analysis unit performs a detailed analysis to provide accurate information. For general inquiries, the analysis unit can perform a rapid analysis to provide basic information. Furthermore, for urgent inquiries, the analysis unit can perform a rapid and detailed analysis to provide results immediately. In this way, appropriate information can be provided by adjusting the level of detail of the analysis based on the importance of the inquiry content. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the text data of the inquiry content into a generating AI, which can evaluate the importance and adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit classifies the category of the inquiry using data mining techniques. Based on the classification results, the analysis unit applies an appropriate analysis algorithm. For example, for an inquiry about restaurant reservations, the analysis unit applies an analysis algorithm specialized for reservation systems. For an inquiry about checking product inventory, the analysis unit can apply an analysis algorithm specialized for inventory management systems. Furthermore, for an inquiry about how to use a service, the analysis unit can apply an analysis algorithm specialized for user guides. In this way, appropriate information can be provided by applying different analysis algorithms depending on the category of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data of the inquiry into a generating AI, which can classify the category and apply an appropriate analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions, for example, using voice tone analysis technology. The analysis unit adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, appropriate information can be provided by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's voice data into a generating AI, which can then estimate the user's emotions and adjust how the analysis results are displayed.

[0083] The analysis unit can determine the priority of analysis based on the submission date of the inquiry during the analysis process. For example, the analysis unit evaluates the submission date of the inquiry based on the submission date and time. Based on the evaluation results, the analysis unit determines the priority of analysis. For example, for urgent inquiries, the analysis unit performs the analysis with the highest priority. For regular inquiries, the analysis unit can perform the analysis with the normal priority. Furthermore, for past inquiries, the analysis unit can postpone the analysis. In this way, appropriate information can be provided by determining the priority of analysis based on the submission date of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date and time data of the inquiry into a generating AI, which can evaluate the submission date and time and determine the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the query content during analysis. The analysis unit evaluates the relevance of the query content, for example, using data mining techniques. Based on the evaluation results, the analysis unit adjusts the order of analysis. For example, the analysis unit prioritizes analyzing highly relevant query content. Conversely, it can postpone the analysis of less relevant query content. Furthermore, if multiple query content is related, the analysis unit can analyze them together. By adjusting the order of analysis based on the relevance of the query content, appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input query data into a generating AI, which can evaluate the relevance and adjust the order of analysis.

[0085] The transmitting unit can estimate the user's emotions and adjust the timing of the transmission based on the estimated emotions. The transmitting unit estimates the user's emotions, for example, using voice tone analysis technology. The transmitting unit adjusts the timing of the transmission based on the estimated emotions. For example, if the user is stressed, the transmitting unit will transmit quickly to obtain results sooner. If the user is relaxed, the transmitting unit will transmit at an appropriate time to obtain detailed information. Furthermore, if the user is in a hurry, the transmitting unit will transmit immediately to obtain results quickly. In this way, appropriate information can be provided by adjusting the timing of the transmission according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the user's voice data into a generating AI, which can then estimate the user's emotions and adjust the timing of the transmission.

[0086] The calling unit can customize the content of a call by considering the attribute information of the caller. The calling unit can acquire the attribute information of the caller, for example, using data mining technology. Based on the acquired attribute information, the calling unit customizes the content of the call. For example, if the caller is a restaurant, the calling unit can prioritize acquiring information related to reservations. If the caller is a retail store, the calling unit can prioritize acquiring information related to product inventory. Furthermore, if the caller is a service provider, the calling unit can prioritize acquiring information on how to use the service. In this way, appropriate information can be provided by considering the attribute information of the caller. Some or all of the above processing in the calling unit may be performed using AI, for example, or without AI. For example, the calling unit can input the attribute data of the caller into a generating AI, and the generating AI can customize the content of the call.

[0087] The calling unit can select the optimal calling method by referring to past calling history when making a call. The calling unit can analyze past calling history using, for example, data mining technology. Based on the analysis results, the calling unit selects the optimal calling method. For example, based on successful calling methods in the past, the calling unit can select the optimal calling method. In addition, based on past calling history, the calling unit can analyze the recipient's response patterns and select the optimal calling method. Furthermore, based on past calling history, the calling unit can also select a calling method that suits the recipient's attributes. In this way, the optimal calling method can be selected by referring to past calling history. Some or all of the above processing in the calling unit may be performed using, for example, AI, or not using AI. For example, the calling unit can input past calling history data into a generating AI, and the generating AI can select the optimal calling method.

[0088] The transmitting unit can estimate the user's emotions and determine the priority of the message content based on the estimated emotions. The transmitting unit estimates the user's emotions, for example, using voice tone analysis technology. Based on the estimated emotions, the transmitting unit determines the priority of the message content. For example, if the user is stressed, the transmitting unit adjusts the message content to prioritize important information. If the user is relaxed, the transmitting unit can adjust the message content to prioritize detailed information. Furthermore, if the user is in a hurry, the transmitting unit can prioritize including information that can be quickly obtained in the message content. In this way, by determining the priority of the message content according to the user's emotions, important information can be obtained preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the user's voice data into a generating AI, which can then estimate the user's emotions and determine the priority of the message to be sent.

[0089] The calling unit can adjust the content of a call considering the geographical location of the caller. The calling unit can acquire the geographical location of the caller, for example, using data mining technology. Based on the acquired geographical location information, the calling unit adjusts the content of the call. For example, if the caller is in a specific region, the calling unit prioritizes acquiring information related to that region. Also, if the caller is traveling, the calling unit can prioritize acquiring information related to their travel destination. Furthermore, if the caller is in a specific facility, the calling unit can prioritize acquiring information related to that facility. In this way, appropriate information can be provided by considering the geographical location of the caller. Some or all of the above processing in the calling unit may be performed using AI, for example, or without AI. For example, the calling unit can input the geographical location data of the caller into a generating AI, and the generating AI can adjust the content of the call.

[0090] The calling unit can analyze the recipient's social media activity when making a call and reflect relevant information in the message. For example, the calling unit can use data mining techniques to analyze the recipient's social media activity. Based on the analysis results, the calling unit reflects relevant information in the message. For example, it can customize the message based on information the recipient has shared on social media. It can also adjust the message based on information about accounts the recipient follows on social media. Furthermore, it can adjust the message based on information about events the recipient is participating in on social media. In this way, by analyzing the recipient's social media activity, relevant information can be provided. Some or all of the above processing in the calling unit may be performed using AI, for example, or without AI. For example, the calling unit can input the recipient's social media data into a generating AI, which can then reflect relevant information in the message.

[0091] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. The summarization unit estimates the user's emotions, for example, using speech tone analysis technology. Based on the estimated emotions, the summarization unit adjusts the way the summary is presented. For example, if the user is stressed, the summarization unit provides a simple and easily readable summary. If the user is relaxed, the summarization unit can provide a summary containing detailed information. Furthermore, if the user is in a hurry, the summarization unit can provide a concise summary. This allows for the provision of appropriate information by adjusting the summary's presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the summarization unit may be performed using AI, or not. For example, the summarization unit can input the user's voice data into a generative AI, which can estimate the user's emotions and adjust the way the summary is presented.

[0092] The summarization unit can adjust the level of detail in the summary based on the importance of the information acquired. The summarization unit can evaluate the importance of the acquired information, for example, using data mining techniques. Based on the evaluation results, the summarization unit adjusts the level of detail in the summary. For example, for important information, the summarization unit provides a detailed summary. For general information, the summarization unit can provide a concise summary. Furthermore, for urgent information, the summarization unit can provide a quick summary that gets straight to the point. In this way, by adjusting the level of detail in the summary based on the importance of the acquired information, appropriate information can be provided. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the data of the acquired information into a generating AI, which can evaluate the importance and adjust the level of detail in the summary.

[0093] The summarization unit can apply different summarization algorithms depending on the category of the information acquired during summarization. For example, the summarization unit classifies the category of the acquired information using data mining techniques. Based on the classification results, the summarization unit applies an appropriate summarization algorithm. For example, for information regarding restaurant reservations, the summarization unit can apply a summarization algorithm specialized for reservation systems. Similarly, for information regarding product inventory checks, the summarization unit can apply a summarization algorithm specialized for inventory management systems. Furthermore, for information regarding how to use a service, the summarization unit can apply a summarization algorithm specialized for user guides. This allows for the provision of appropriate information by applying different summarization algorithms depending on the category of the acquired information. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the acquired information data into a generating AI, which can classify the categories and apply an appropriate summarization algorithm.

[0094] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. The summarization unit estimates the user's emotions, for example, using speech tone analysis technology. Based on the estimated emotions, the summarization unit adjusts the length of the summary. For example, if the user is stressed, the summarization unit provides a short, concise summary. If the user is relaxed, the summarization unit can provide a longer summary with more detailed explanations. Furthermore, if the user is in a hurry, the summarization unit can provide a short summary that can be quickly understood. This allows for the provision of appropriate information by adjusting the length of the summary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI, or not. For example, the summarization unit can input the user's voice data into a generative AI, which can estimate the user's emotions and adjust the length of the summary.

[0095] The summarization unit can determine the priority of summarization based on the submission date of the acquired information. For example, the summarization unit evaluates the submission date of the acquired information based on the submission date and time. Based on the evaluation result, the summarization unit determines the priority of summarization. For example, for urgent information, the summarization unit will summarize it with the highest priority. For regular information, the summarization unit can summarize it with the normal priority. Furthermore, for past information, the summarization unit can postpone summarization. In this way, appropriate information can be provided by determining the priority of summarization based on the submission date of the acquired information. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the submission date and time data of the acquired information into a generating AI, which can evaluate the submission date and time and determine the priority of summarization.

[0096] The summarization unit can adjust the order of summaries based on the relevance of the acquired information during the summarization process. The summarization unit evaluates the relevance of the acquired information, for example, using data mining techniques. Based on the evaluation results, the summarization unit adjusts the order of summaries. For example, the summarization unit prioritizes summarizing highly relevant information. Conversely, it can postpone summarizing less relevant information. Furthermore, if multiple pieces of information are related, the summarization unit can summarize them together. This allows for the provision of appropriate information by adjusting the order of summaries based on the relevance of the acquired information. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the data of the acquired information into a generating AI, which can evaluate the relevance and adjust the order of summaries.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can automatically categorize inquiries based on user input. For example, it analyzes user input and categorizes it into items such as restaurant reservations, product inventory checks, and service usage instructions. This allows the reception desk to process inquiries appropriately. The reception desk can also prioritize inquiries based on user input. For example, urgent inquiries are given top priority. Furthermore, the reception desk can adjust the level of detail required for inquiries based on user input. For example, inquiries requiring detailed information are prompted for more detailed input. This allows the reception desk to process inquiries appropriately by categorizing them, prioritizing them, and adjusting the level of detail based on user input.

[0099] The transmitter can estimate the user's emotions and adjust the tone of its message based on those emotions. For example, if the user is stressed, the transmitter will deliver the message in a calm tone. If the user is relaxed, the transmitter can deliver the message in a friendly tone. Furthermore, if the user is in a hurry, the transmitter can deliver the message in a quick and concise tone. In this way, the transmitter can provide appropriate information by adjusting the tone of its message according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmitter may be performed using AI or not. For example, the transmitter can input the user's voice data into the generative AI, which can estimate the user's emotions and adjust the tone of its message.

[0100] The summarization unit can evaluate the reliability of the acquired information and adjust the content of the summary based on that reliability. For example, the summarization unit can evaluate the source and reliability of the acquired information and create a summary based on the reliable information. Furthermore, the summarization unit can issue warnings for information with low reliability. In addition, the summarization unit can adjust the level of detail of the summary based on the reliability of the acquired information. For example, it can provide a detailed summary for highly reliable information and a concise summary for unreliable information. This allows the summarization unit to provide appropriate information by evaluating the reliability of the acquired information and adjusting the content of the summary based on that reliability. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the acquired information data into a generating AI, which can then evaluate the reliability and adjust the content of the summary.

[0101] The reception desk can automatically generate inquiry templates based on user input. For example, the reception desk analyzes user input and automatically generates a template corresponding to the inquiry. This allows the reception desk to easily input inquiry details. The reception desk can also customize inquiry templates based on user input. For example, if a user enters specific information, the reception desk customizes the template based on that information. Furthermore, the reception desk can update inquiry templates based on user input. This allows the reception desk to automatically generate, customize, and update inquiry templates based on user input, enabling appropriate processing.

[0102] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand feedback. If the user is relaxed, the analysis unit can provide feedback that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide feedback that can be quickly understood. In this way, the analysis unit can provide appropriate information by adjusting the feedback method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's voice data into a generative AI, which can estimate the user's emotions and adjust the feedback method of the analysis results.

[0103] The calling unit can adjust the timing of a call by considering the business hours of the recipient. For example, the calling unit can obtain the recipient's business hours and make the call within those hours. If the recipient is closed on a holiday, the calling unit can avoid making the call on that day. Furthermore, if the recipient is busy during a specific time period, the calling unit can avoid making the call during that time. In this way, the calling unit can select an appropriate calling timing by considering the recipient's business hours. Some or all of the above processing in the calling unit may be performed using AI, for example, or not. For example, the calling unit can input the recipient's business hours data into a generating AI, which can then adjust the calling timing.

[0104] The reception desk can estimate the user's emotions and adjust the method of confirming the input content based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a concise confirmation method. If the user is relaxed, the reception desk can provide a more detailed confirmation method. Furthermore, if the user is in a hurry, the reception desk can provide a quick confirmation method. In this way, the reception desk can provide appropriate information by adjusting the method of confirming the input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's voice data into a generative AI, which can estimate the user's emotions and adjust the method of confirming the input content.

[0105] The analysis unit can select an analysis algorithm based on the language of the inquiry during analysis. For example, if the inquiry is entered in English, the analysis unit can apply an analysis algorithm specialized for English. If the inquiry is entered in Japanese, it can apply an analysis algorithm specialized for Japanese. Furthermore, if the inquiry is entered in multiple languages, the analysis unit can apply an analysis algorithm corresponding to each language. In this way, the analysis unit can provide accurate information by selecting the appropriate analysis algorithm based on the language of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the text data of the inquiry into a generating AI, which can identify the language and select an appropriate analysis algorithm.

[0106] The summarization unit can estimate the user's emotions and adjust the visual representation of the summary based on those emotions. For example, if the user is stressed, the summarization unit can provide a simple and easily visible visual representation. If the user is relaxed, the summarization unit can provide a visual representation that includes detailed information. Furthermore, if the user is in a hurry, the summarization unit can provide a visual representation that can be quickly understood. In this way, the summarization unit can provide appropriate information by adjusting the visual representation of the summary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not using AI. For example, the summarization unit can input the user's voice data into a generative AI, which can estimate the user's emotions and adjust the visual representation of the summary.

[0107] The calling unit can adjust the content of a call considering the cultural background of the recipient. For example, if the recipient is in a different cultural sphere, the calling unit can provide content that is considerate of that culture. Also, if the recipient has specific cultural customs, the calling unit can provide content that is considerate of those customs. Furthermore, if the recipient speaks a specific language, the calling unit can provide content that corresponds to that language. In this way, the calling unit can provide appropriate content by considering the cultural background of the recipient. Some or all of the above processing in the calling unit may be performed using AI, for example, or not. For example, the calling unit can input the recipient's cultural background data into a generating AI, which can then adjust the content of the call.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives the user's inquiry. The user can choose to enter the information via text or voice. For example, they might enter something like, "I would like to make a reservation for the special course for two people at 7 PM on December 15th." The reception desk then sends the entered information to the analysis department. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the input information using text analysis and speech analysis. The analysis unit transmits the analysis results to the transmission unit. Step 3: The calling unit makes a phone call based on the information analyzed by the analysis unit. The calling unit makes a phone call using the user's voice with generated speech. The calling unit makes the phone call and obtains the necessary information. For example, it might call a restaurant to make a reservation and obtain information about allergies and adjust the time. The calling unit then transmits the obtained information to the summarization unit. Step 4: The summarization unit summarizes the information obtained by the transmission unit. The summarization unit has AI transcription and summarization functions, and transcribes and summarizes the obtained information and notifies the user. For example, it might notify the user with content such as, "Your reservation is complete. You will be seated at 6:30 PM on December 15th. Your request to change your main dish has also been accepted."

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, transmission unit, and summarization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs the inquiry content. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the input information. The transmission unit is implemented by the specific processing unit 290 of the data processing unit 12, where it makes a phone call using generated voice. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12, where it summarizes the acquired information and notifies the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, transmission unit, and summarization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs the inquiry. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The transmission unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which makes a phone call using generated voice. The summarization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which summarizes the acquired information and notifies the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, transmission unit, and summarization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs the inquiry. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the input information. The transmission unit is implemented by the specific processing unit 290 of the data processing unit 12, where it makes a phone call using generated voice. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12, where it summarizes the acquired information and notifies the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, transmission unit, and summarization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs the inquiry. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The transmission unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which makes a phone call using generated voice. The summarization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which summarizes the acquired information and notifies the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The reception desk where you enter your inquiry details, An analysis unit that analyzes the information received by the reception unit, A calling unit that makes a phone call based on the information analyzed by the aforementioned analysis unit, The system includes a summarization unit that summarizes the information acquired by the transmitting unit. A system characterized by the following features. (Note 2) The transmitting unit is Make a phone call using the user's voice via generated speech. The system described in Appendix 1, characterized by the features described herein. (Note 3) The summary section above is, Features AI transcription and summarization capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is You can choose between text input or voice input. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Obtain information tailored to the content of your inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and automatically selects the input method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past inquiry history and suggest the most suitable input format. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system automatically completes input based on the user's current situation and environment during input. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input data, the system prioritizes inputting information that is highly relevant based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During input, the system analyzes the user's social media activity and automatically fills in relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the query content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The transmitting unit is It estimates the user's emotions and adjusts the timing of communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The transmitting unit is When making a call, the content of the call is customized by considering the attributes of the person being called. The system described in Appendix 1, characterized by the features described herein. (Note 20) The transmitting unit is When making a call, the system selects the most suitable method by referring to past call history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The transmitting unit is It estimates the user's emotions and determines the priority of the content to be communicated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The transmitting unit is When making a call, the content of the call is adjusted considering the geographical location of the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 23) The transmitting unit is When making a call, the system analyzes the recipient's social media activity and incorporates relevant information into the message. The system described in Appendix 1, characterized by the features described herein. (Note 24) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The summary section above is, When summarizing, adjust the level of detail in the summary based on the importance of the information obtained. The system described in Appendix 1, characterized by the features described herein. (Note 26) The summary section above is, When summarizing, different summarization algorithms are applied depending on the category of information obtained. The system described in Appendix 1, characterized by the features described herein. (Note 27) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The summary section above is, When summarizing, prioritize summaries based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The summary section above is, When summarizing, adjust the order of the summaries based on the relevance of the information obtained. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you enter your inquiry details, An analysis unit that analyzes the information received by the reception unit, A calling unit that makes a phone call based on the information analyzed by the aforementioned analysis unit, The system includes a summarization unit that summarizes the information acquired by the transmitting unit. A system characterized by the following features.

2. The transmitting unit is Make a phone call using the user's voice via generated speech. The system according to feature 1.

3. The summary section above is, It has AI transcription and summarization capabilities. The system according to feature 1.

4. The aforementioned reception unit is You can choose between text input or voice input. The system according to feature 1.

5. The aforementioned analysis unit, Obtain information tailored to the content of your inquiry. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and automatically selects the input method based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past inquiry history and suggest the most suitable input format. The system according to feature 1.

8. The aforementioned reception unit is The system automatically completes input based on the user's current situation and environment during input. The system according to feature 1.

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