System

Abstract

An object of a system according to an embodiment is to efficiently grasp contents of an incoming message and automatically reject an unnecessary invitation.SOLUTION: A system according to an embodiment includes a questioner, a text generator, a transmitter, and a learner. The question unit asks the content of the requirement for the incoming call. The text generation unit combines the answers obtained by the question unit into a text. The transmission unit transmits the text compiled by the texting unit to the messaging service. The learning unit learns information of the user and automatically rejects an unnecessary invitation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is not possible to efficiently understand the content of incoming messages and automatically decline unwanted solicitations.

[0005] The system according to the embodiment aims to efficiently understand the contents of incoming messages and automatically decline unwanted solicitations. [Means for solving the problem]

[0006] The system according to the embodiment includes a questioning unit, a text conversion unit, a transmission unit, and a learning unit. The questioning unit asks about the content of the requirements in response to an incoming call. The text conversion unit compiles the answers obtained by the questioning unit into text. The transmission unit transmits the text compiled by the text conversion unit to a messaging service. The learning unit learns information about the user and automatically declines unwanted solicitations. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently grasp the contents of incoming messages and automatically decline unwanted solicitations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An advanced answering machine system according to an embodiment of the present invention is a system that asks a caller about the call's requirements, compiles the response into text, and sends it to a messaging service. The advanced answering machine system asks a caller about the call's requirements, compiles the response into text, and sends it to a messaging service, providing an automatic memo function and eliminating the need to listen to voicemails. The advanced answering machine system also learns user information and provides a function for automatically refusing unwanted solicitations. For example, the advanced answering machine system automatically answers a call and asks about the call's requirements. For example, it asks a question such as, "What is it that you need?" Next, it asks for more details about the call's requirements. For example, it asks a question such as, "Can you tell me more about that?" It then asks whether a call will be returned and for contact information. For example, it asks questions such as, "Do you need a call back?" or "Please let me know your contact information." The answers to these questions are compiled into text and saved as text for the messaging service. For example, it saves text such as, "Call from Mr. / Ms. XX: The call is about XX. For details, please say XX. No call back is necessary. Contact information is XX." This saves users the trouble of listening to voicemails. In addition, by having AI learn user information, high-performance voicemail systems also provide a function that automatically declines solicitations for products or religions. For example, the AI ​​can automatically respond, "We decline solicitations for products or religions." In this way, users can avoid unwanted solicitations. This saves users the trouble of listening to voicemails, and allows them to easily check important information as text. Furthermore, by automatically declining unwanted solicitations, user convenience is improved.

[0029] An advanced answering machine system according to an embodiment includes a questioning unit, a text conversion unit, a transmission unit, and a learning unit. The questioning unit asks a caller about the content of the requirement. For example, the questioning unit asks a question such as, "What is your business?" The questioning unit can also ask for more details about the content of the requirement. For example, the questioning unit can ask a question such as, "Could you please tell me more about that?" The questioning unit can also ask whether a call will be returned and for contact information. For example, the questioning unit can ask a question such as, "Do you need a call back?" or "Please tell me your contact information." The text conversion unit compiles the answer obtained by the questioning unit into text. For example, the text conversion unit compiles information such as the content of the requirement, details, whether a call will be returned, and contact information into text. The text conversion unit can also compile the answer into text using a generation AI. For example, the generation AI receives the answer as input and outputs a summary. The transmission unit transmits the text compiled by the text conversion unit to a messaging service. For example, the transmission unit transmits the text to a messaging service such as SMS, email, or a chat app. The sending unit can also use the generation AI to optimize the content to be sent. For example, the generation AI receives the content to be sent as input and outputs the optimal sending format. The learning unit learns user information and automatically declines unwanted solicitations. For example, the learning unit learns the user's preferences and past response history and automatically declines solicitations for products or religion. The learning unit can also use the generation AI to learn user information. For example, the generation AI receives user information as input and outputs the learning results. This allows the high-performance answering machine system according to the embodiment to eliminate the need for the user to listen to voicemails and to easily check important information as text. Furthermore, automatically declining unwanted solicitations improves user convenience.

[0030] The questioning unit can ask about the content of the requirement, details about the content, whether or not a call will be made back, contact information, etc. The questioning unit, for example, asks about the content of the requirement. For example, the questioning unit asks a question such as, "What is your business?" The questioning unit can also ask about details about the content of the requirement. For example, the questioning unit can ask a question such as, "Please tell me more about that." The questioning unit can also ask about whether or not a call will be made back and contact information. For example, the questioning unit can ask a question such as, "Do you need a call back?" or "Please tell me your contact information." In this way, the questioning unit can ask for more detailed information, thereby more accurately grasping the requirements. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can use AI to obtain information such as the content of the requirement, details, whether or not a call will be made back, and contact information.

[0031] The text conversion unit can summarize the answers obtained by the question unit into text. The text conversion unit, for example, summarizes the answers obtained by the question unit into text. For example, the text conversion unit summarizes information such as the content of the requirements, details, whether or not there will be a callback, and contact information into text. The text conversion unit can also summarize the answers into text using a generation AI. For example, the generation AI receives the answers as input and outputs a summary. In this way, the text conversion unit summarizes the answers into text, making it easier to organize the information. Some or all of the above-mentioned processing in the text conversion unit may be performed using AI, or may be performed without using AI. For example, the text conversion unit can summarize the answers into text using AI.

[0032] The sending unit can send the text compiled by the text conversion unit to a messaging service. The sending unit, for example, sends the text compiled by the text conversion unit to a messaging service. For example, the sending unit sends the text to a messaging service such as SMS, email, or a chat app. The sending unit can also optimize the content to be sent using a generation AI. For example, the generation AI receives the content to be sent as input and outputs the optimal sending format. In this way, the sending unit can send the text to a messaging service, thereby saving the user the trouble of listening to voicemail. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can send the text to a messaging service using AI.

[0033] The learning unit can learn information about the user and automatically decline solicitations for products or religions. The learning unit, for example, learns information about the user and automatically declines solicitations for products or religions. For example, the learning unit can learn the user's preferences and past response history and automatically decline solicitations for products or religions. The learning unit can also use a generation AI to learn information about the user. For example, the generation AI receives user information as input and outputs the learning results. As a result, the learning unit automatically declines unnecessary solicitations, improving user convenience. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can use AI to learn information about the user and automatically decline unnecessary solicitations.

[0034] The questioning unit can select the most appropriate question by referring to the past call history. The questioning unit, for example, selects the most appropriate question by referring to the past call history. For example, the questioning unit automatically selects the most appropriate question based on questions that the user has frequently received in the past. The questioning unit can also ask related questions by referring to the answers the user has given in the past. The questioning unit can also analyze patterns of questions the user has received in the past and select the most effective question. This makes it possible to ask more effective questions by referring to the past call history. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can use AI to select the most appropriate question by referring to the past call history.

[0035] The question unit can customize the content of the questions taking into account the recipient's attribute information. The question unit customizes the content of the questions taking into account the recipient's attribute information, for example. For example, if the recipient is a business associate, the question unit prioritizes business-related questions. Furthermore, if the recipient is a family member or friend, the question unit can also prioritize personal questions. Furthermore, the question unit can select appropriate questions depending on the recipient's age and gender. In this way, customizing the content of the questions based on the recipient's attribute information enables more appropriate questions. Some or all of the above-described processing in the question unit may be performed, for example, using AI, or may be performed without using AI. For example, the question unit can customize the content of the questions taking into account the recipient's attribute information using AI.

[0036] The questioning unit can adjust the level of detail of the question based on the time of the incoming call. The questioning unit adjusts the level of detail of the question based on, for example, the time of the incoming call. For example, in the case of an incoming call at night, the questioning unit asks concise and to the point questions. In addition, in the case of an incoming call during the day, the questioning unit can ask detailed questions to elicit more information. In addition, in the case of an incoming call early in the morning, the questioning unit can prioritize important questions and quickly grasp the requirements. In this way, adjusting the level of detail of the question based on the time of the incoming call enables more appropriate questions to be asked. Some or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can adjust the level of detail of the question based on the time of the incoming call using AI.

[0037] The interrogation unit can ask highly relevant questions taking into account the geographical location information of the recipient. The interrogation unit can ask highly relevant questions taking into account, for example, the geographical location information of the recipient. For example, if the recipient is in a specific area, the interrogation unit can ask questions related to the area. Furthermore, if the recipient is traveling, the interrogation unit can ask questions related to the travel. Furthermore, if the recipient is at home, the interrogation unit can ask questions related to the home. In this way, by asking highly relevant questions based on the geographical location information of the recipient, more appropriate questions can be asked. Some or all of the above-described processing in the interrogation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interrogation unit can ask highly relevant questions taking into account the geographical location information of the recipient using AI.

[0038] The questioning unit can analyze the social media activity of the recipient and ask related questions. The questioning unit, for example, analyzes the social media activity of the recipient and asks related questions. For example, the questioning unit asks related questions based on content posted by the recipient on social media. The questioning unit can also analyze the recipient's social media activity and ask questions that the recipient might be interested in. The questioning unit can also ask related questions with reference to the activity of the recipient's friends on social media. In this way, asking related questions based on the recipient's social media activity enables more appropriate questions. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can analyze the recipient's social media activity and ask related questions using AI.

[0039] The questioning unit can customize the question content by reflecting past feedback. The questioning unit customizes the question content by reflecting past feedback, for example. For example, the questioning unit improves the question content based on feedback provided by the user in the past. The questioning unit can also avoid questions that the user has been dissatisfied with in the past and ask questions that will provide high satisfaction. The questioning unit can also analyze the user's past feedback and select optimal question content. This enables more appropriate questions to be asked by reflecting past feedback. Some or all of the above-described processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can customize the question content by reflecting past feedback using AI.

[0040] The text conversion unit can adjust the level of detail of the text based on the importance of the requirement. The text conversion unit adjusts the level of detail of the text based on, for example, the importance of the requirement. For example, the text conversion unit generates text including a detailed explanation for an important requirement. The text conversion unit can also generate concise, to-the-point text for a general requirement. The text conversion unit can also generate summarized text for an urgent requirement so that it can be quickly understood. In this way, by adjusting the level of detail of the text according to the importance of the requirement, more appropriate text is generated. Some or all of the above-described processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can adjust the level of detail of the text based on the importance of the requirement using AI.

[0041] The text conversion unit can apply different text conversion algorithms depending on the category of the requirement. For example, the text conversion unit can apply different text conversion algorithms depending on the category of the requirement. For example, the text conversion unit can generate text including technical terms for business-related requirements. For personal requirements, the text conversion unit can also generate text using familiar expressions. For urgent requirements, the text conversion unit can also generate summarized text for quick understanding. In this way, applying different text conversion algorithms depending on the category of the requirement generates more appropriate text. Some or all of the above-mentioned processing in the text conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the text conversion unit can use AI to apply different text conversion algorithms depending on the category of the requirement.

[0042] The text conversion unit can improve the accuracy of the text by referring to past text conversion results. The text conversion unit, for example, improves the accuracy of the text by referring to past text conversion results. For example, the text conversion unit analyzes past text conversion results and corrects errors to improve accuracy. The text conversion unit can also select an optimal expression method based on past text conversion results. The text conversion unit can also generate text that matches the user's preferences by referring to past text conversion results. In this way, the accuracy of the text is improved by referring to past text conversion results. Some or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can use AI to improve the accuracy of the text by referring to past text conversion results.

[0043] The text conversion unit can determine the priority of text based on the time of submission of requirements. The text conversion unit determines the priority of text based on, for example, the time of submission of requirements. For example, the text conversion unit gives the highest priority to text conversion for urgent requirements. The text conversion unit can also quickly convert important requirements into text. The text conversion unit can also convert general requirements into text later than other requirements. In this way, by determining the priority of text based on the time of submission of requirements, more appropriate text is generated. Some or all of the above-described processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can use AI to determine the priority of text based on the time of submission of requirements.

[0044] The text conversion unit can adjust the order of the text based on the relevance of the requirements. The text conversion unit, for example, adjusts the order of the text based on the relevance of the requirements. For example, the text conversion unit prioritizes highly relevant requirements for text conversion and adjusts the order. The text conversion unit can also put less relevant requirements on hold for text conversion and adjust the order. The text conversion unit can also convert requirements into text in an optimal order based on the relevance of the requirements. In this way, by adjusting the order of the text according to the relevance of the requirements, more appropriate text is generated. Some or all of the above-described processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can use AI to adjust the order of the text based on the relevance of the requirements.

[0045] The text conversion unit can adjust the use of technical terms in the text according to the user's level of expertise. The text conversion unit can adjust the use of technical terms in the text according to the user's level of expertise, for example. For example, if the user has technical knowledge, the text conversion unit can generate text including technical terms. Furthermore, if the user does not have technical knowledge, the text conversion unit can generate text using easy-to-understand expressions. The text conversion unit can also select an optimal expression method according to the user's level of expertise. In this way, more appropriate text is generated by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the text conversion unit can be performed using, for example, AI, or can be performed without using AI. For example, the text conversion unit can use AI to adjust the use of technical terms in the text according to the user's level of expertise.

[0046] The transmission unit can select the optimal transmission method by referring to past transmission history. The transmission unit, for example, selects the optimal transmission method by referring to past transmission history. For example, the transmission unit selects the optimal transmission method based on transmission methods used by the user in the past. The transmission unit can also select an effective transmission method from the user's past transmission history. The transmission unit can also analyze the user's past transmission history and select the most efficient transmission method. This enables more effective transmission by referring to the past transmission history. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can use AI to refer to the past transmission history and select the optimal transmission method.

[0047] The sending unit can customize the content to be sent taking into account the attribute information of the recipient. The sending unit customizes the content to be sent taking into account, for example, the attribute information of the recipient. For example, if the recipient is a business associate, the sending unit may prioritize sending business-related content. Furthermore, if the recipient is a family member or friend, the sending unit may prioritize sending personal content. Furthermore, the sending unit can select appropriate content to be sent depending on the age and gender of the recipient. In this way, customizing the content to be sent based on the attribute information of the recipient enables more appropriate sending. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can customize the content to be sent taking into account the attribute information of the recipient using AI.

[0048] The transmitting unit can adjust the level of detail of the transmission based on the time zone of the transmission. The transmitting unit adjusts the level of detail of the transmission based on, for example, the time zone of the transmission. For example, when transmitting at night, the transmitting unit transmits concise content that is to the point. The transmitting unit can also transmit detailed content when transmitting during the day. The transmitting unit can also prioritize important content when transmitting early in the morning. In this way, adjusting the level of detail of the transmission according to the time zone of the transmission enables more appropriate transmission. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can adjust the level of detail of the transmission based on the time zone of the transmission using AI.

[0049] The transmitting unit can perform highly relevant transmission by taking into account the geographical location information of the recipient. The transmitting unit can perform highly relevant transmission by taking into account, for example, the geographical location information of the recipient. For example, if the recipient is in a specific area, the transmitting unit can transmit information related to that area. Furthermore, if the recipient is traveling, the transmitting unit can also transmit travel-related information. Furthermore, if the recipient is at home, the transmitting unit can also transmit home-related information. This enables more appropriate transmission by performing highly relevant transmission based on the geographical location information of the recipient. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can perform highly relevant transmission by using AI and taking into account the geographical location information of the recipient.

[0050] The sending unit can analyze the recipient's social media activity and perform relevant transmission. The sending unit, for example, analyzes the recipient's social media activity and performs relevant transmission. For example, the sending unit can send relevant information based on content posted by the recipient on social media. The sending unit can also analyze the recipient's social media activity and send information that the recipient is likely to be interested in. The sending unit can also send relevant information by referring to the activities of the recipient's friends on social media. This enables more appropriate transmission by performing relevant transmission based on the recipient's social media activity. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can use AI to analyze the recipient's social media activity and perform relevant transmission.

[0051] The transmission unit can customize the transmission content by reflecting past feedback. The transmission unit customizes the transmission content by reflecting past feedback, for example. For example, the transmission unit improves the transmission content based on feedback provided by the user in the past. The transmission unit can also avoid transmission content that the user was dissatisfied with in the past and transmit content that satisfies the user. The transmission unit can also analyze the user's past feedback and select optimal transmission content. This enables more appropriate transmission by reflecting past feedback. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can customize the transmission content by using AI to reflect past feedback.

[0052] The learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, optimizes the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data and selects an optimal algorithm. The learning unit can also adjust parameters of the algorithm based on past learning data. The learning unit can also improve the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can optimize the learning algorithm by referring to past learning data using AI.

[0053] The learning unit can update the learning data by reflecting user feedback. The learning unit, for example, updates the learning data by reflecting user feedback. For example, the learning unit updates the learning data based on feedback provided by the user. The learning unit can also analyze user feedback and improve the accuracy of the learning data. The learning unit can also optimize the learning data by referring to user feedback. In this way, the accuracy of the learning data is improved by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can update the learning data by using AI to reflect user feedback.

[0054] The learning unit can analyze the user's lifestyle rhythm and select the optimal timing for studying. The learning unit, for example, analyzes the user's lifestyle rhythm and selects the optimal timing for studying. For example, the learning unit analyzes the user's lifestyle rhythm and selects the optimal timing for studying. The learning unit can also adjust the frequency of studying based on the user's lifestyle rhythm. The learning unit can also maximize the effectiveness of studying by referring to the user's lifestyle rhythm. This enables more effective studying by selecting the timing for studying based on the user's lifestyle rhythm. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can use AI to analyze the user's lifestyle rhythm and select the optimal timing for studying.

[0055] The learning unit can weight the learning data based on the time when the incoming call history was submitted. The learning unit weights the learning data based on, for example, the time when the incoming call history was submitted. For example, the learning unit prioritizes learning and weighting recent incoming call histories. The learning unit can also prioritize learning and weighting important incoming call histories. The learning unit can also prioritize learning and weighting urgent incoming call histories. In this way, weighting the learning data based on the time when the incoming call history was submitted enables more appropriate learning. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can use AI to weight the learning data based on the time when the incoming call history was submitted.

[0056] The learning unit can integrate information from different data sources to enrich the training data. For example, the learning unit integrates information from different data sources to enrich the training data. For example, the learning unit integrates information from different data sources to enrich the training data. The learning unit can also analyze information from different data sources and select optimal training data. The learning unit can also improve the accuracy of the training data by referring to information from different data sources. In this way, the accuracy of the training data is improved by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can use AI to integrate information from different data sources to enrich the training data.

[0057] The learning unit can customize the learning content based on the user's living situation. The learning unit customizes the learning content based on the user's living situation, for example. For example, the learning unit analyzes the user's living situation and selects optimal learning content. The learning unit can also customize the learning content based on the user's living situation. The learning unit can also maximize the effectiveness of learning by referring to the user's living situation. This enables more effective learning by customizing the learning content based on the user's living situation. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can customize the learning content based on the user's living situation using AI.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The questioning unit can select the most appropriate question by referring to the past call history. For example, the most appropriate question can be automatically selected based on questions that the user has frequently received in the past. Also, the questioning unit can ask related questions by referring to the answers the user has given in the past. Furthermore, the questioning unit can analyze the patterns of questions the user has received in the past and select the most effective question. This makes it possible to ask more effective questions by referring to the past call history. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can use AI to refer to the past call history and select the most appropriate question.

[0060] The question unit can customize the questions taking into account the recipient's attribute information. For example, if the recipient is a business associate, business-related questions can be prioritized. Also, if the recipient is a family member or friend, personal questions can be prioritized. Furthermore, appropriate questions can be selected depending on the recipient's age and gender. In this way, customizing the questions based on the recipient's attribute information enables more appropriate questions to be asked. Some or all of the above-described processing in the question unit may be performed using, for example, AI, or may be performed without using AI. For example, the question unit can customize the questions taking into account the recipient's attribute information using AI.

[0061] The questioning unit can adjust the level of detail of the questions based on the time of the call. For example, for calls received at night, simple, to-the-point questions can be asked. For calls received during the day, detailed questions can be asked to elicit more information. Furthermore, for calls received early in the morning, important questions can be prioritized to quickly grasp the requirements. In this way, adjusting the level of detail of the questions based on the time of the call enables more appropriate questions to be asked. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can use AI to adjust the level of detail of the questions based on the time of the call.

[0062] The text conversion unit can adjust the level of detail of the text based on the importance of the requirement. For example, for important requirements, it can generate text including detailed explanations. For general requirements, it can generate concise, to-the-point text. Furthermore, for urgent requirements, it can generate summarized text that can be quickly understood. In this way, by adjusting the level of detail of the text according to the importance of the requirement, more appropriate text can be generated. Some or all of the above-described processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can use AI to adjust the level of detail of the text based on the importance of the requirement.

[0063] The text conversion unit can apply different text conversion algorithms depending on the category of the requirement. For example, for business-related requirements, it can generate text containing technical terms. For personal requirements, it can generate text using familiar expressions. Furthermore, for urgent requirements, it can generate summarized text that can be understood quickly. In this way, by applying different text conversion algorithms depending on the category of the requirement, more appropriate text can be generated. Some or all of the above-mentioned processing in the text conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the text conversion unit can use AI to apply different text conversion algorithms depending on the category of the requirement.

[0064] The text conversion unit can improve the accuracy of the text by referring to past text conversion results. For example, the text conversion unit can analyze past text conversion results and correct errors to improve accuracy. The text conversion unit can also select the optimal expression method based on past text conversion results. Furthermore, the text conversion unit can generate text tailored to the user's preferences by referring to past text conversion results. In this way, the accuracy of the text is improved by referring to past text conversion results. Some or all of the above-mentioned processing in the text conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the text conversion unit can use AI to improve the accuracy of the text by referring to past text conversion results.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The questioning section asks the caller about the requirements. For example, it asks questions such as "What is your business?" or "Could you please give me more details about that?" It can also ask whether the caller will call back and for contact information. Step 2: The text generator compiles the answers obtained by the question generator into text. For example, it compiles information such as the requirements, details, whether or not a reply will be received, and contact information into text. It can also use a generation AI to summarize the answers. Step 3: The sender sends the text compiled by the text converter to a messaging service, such as SMS, email, or a chat app. Generative AI can also be used to optimize the content of the message. Step 4: The learning unit learns user information and automatically declines unwanted solicitations. For example, it can learn the user's preferences and past response history and automatically decline solicitations for products or religion. It can also use generative AI to learn user information.

[0067] (Example 2) An advanced answering machine system according to an embodiment of the present invention is a system that asks a caller about the call's requirements, compiles the response into text, and sends it to a messaging service. The advanced answering machine system asks a caller about the call's requirements, compiles the response into text, and sends it to a messaging service, providing an automatic memo function and eliminating the need to listen to voicemails. The advanced answering machine system also learns user information and provides a function for automatically refusing unwanted solicitations. For example, the advanced answering machine system automatically answers a call and asks about the call's requirements. For example, it asks a question such as, "What is it that you need?" Next, it asks for more details about the call's requirements. For example, it asks a question such as, "Can you tell me more about that?" It then asks whether a call will be returned and for contact information. For example, it asks questions such as, "Do you need a call back?" or "Please let me know your contact information." The answers to these questions are compiled into text and saved as text for the messaging service. For example, it saves text such as, "Call from Mr. / Ms. XX: The call is about XX. For details, please say XX. No call back is necessary. Contact information is XX." This saves users the trouble of listening to voicemails. In addition, by having AI learn user information, high-performance voicemail systems also provide a function that automatically declines solicitations for products or religions. For example, the AI ​​can automatically respond, "We decline solicitations for products or religions." In this way, users can avoid unwanted solicitations. This saves users the trouble of listening to voicemails, and allows them to easily check important information as text. Furthermore, by automatically declining unwanted solicitations, user convenience is improved.

[0068] An advanced answering machine system according to an embodiment includes a questioning unit, a text conversion unit, a transmission unit, and a learning unit. The questioning unit asks a caller about the content of the requirement. For example, the questioning unit asks a question such as, "What is your business?" The questioning unit can also ask for more details about the content of the requirement. For example, the questioning unit can ask a question such as, "Could you please tell me more about that?" The questioning unit can also ask whether a call will be returned and for contact information. For example, the questioning unit can ask a question such as, "Do you need a call back?" or "Please tell me your contact information." The text conversion unit compiles the answer obtained by the questioning unit into text. For example, the text conversion unit compiles information such as the content of the requirement, details, whether a call will be returned, and contact information into text. The text conversion unit can also compile the answer into text using a generation AI. For example, the generation AI receives the answer as input and outputs a summary. The transmission unit transmits the text compiled by the text conversion unit to a messaging service. For example, the transmission unit transmits the text to a messaging service such as SMS, email, or a chat app. The sending unit can also use the generation AI to optimize the content to be sent. For example, the generation AI receives the content to be sent as input and outputs the optimal sending format. The learning unit learns user information and automatically declines unwanted solicitations. For example, the learning unit learns the user's preferences and past response history and automatically declines solicitations for products or religion. The learning unit can also use the generation AI to learn user information. For example, the generation AI receives user information as input and outputs the learning results. This allows the high-performance answering machine system according to the embodiment to eliminate the need for the user to listen to voicemails and to easily check important information as text. Furthermore, automatically declining unwanted solicitations improves user convenience.

[0069] The questioning unit can ask about the content of the requirement, details about the content, whether or not a call will be made back, contact information, etc. The questioning unit, for example, asks about the content of the requirement. For example, the questioning unit asks a question such as, "What is your business?" The questioning unit can also ask about details about the content of the requirement. For example, the questioning unit can ask a question such as, "Please tell me more about that." The questioning unit can also ask about whether or not a call will be made back and contact information. For example, the questioning unit can ask a question such as, "Do you need a call back?" or "Please tell me your contact information." In this way, the questioning unit can ask for more detailed information, thereby more accurately grasping the requirements. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can use AI to obtain information such as the content of the requirement, details, whether or not a call will be made back, and contact information.

[0070] The text conversion unit can summarize the answers obtained by the question unit into text. The text conversion unit, for example, summarizes the answers obtained by the question unit into text. For example, the text conversion unit summarizes information such as the content of the requirements, details, whether or not there will be a callback, and contact information into text. The text conversion unit can also summarize the answers into text using a generation AI. For example, the generation AI receives the answers as input and outputs a summary. In this way, the text conversion unit summarizes the answers into text, making it easier to organize the information. Some or all of the above-mentioned processing in the text conversion unit may be performed using AI, or may be performed without using AI. For example, the text conversion unit can summarize the answers into text using AI.

[0071] The sending unit can send the text compiled by the text conversion unit to a messaging service. The sending unit, for example, sends the text compiled by the text conversion unit to a messaging service. For example, the sending unit sends the text to a messaging service such as SMS, email, or a chat app. The sending unit can also optimize the content to be sent using a generation AI. For example, the generation AI receives the content to be sent as input and outputs the optimal sending format. In this way, the sending unit can send the text to a messaging service, thereby saving the user the trouble of listening to voicemail. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can send the text to a messaging service using AI.

[0072] The learning unit can learn information about the user and automatically decline solicitations for products or religions. The learning unit, for example, learns information about the user and automatically declines solicitations for products or religions. For example, the learning unit can learn the user's preferences and past response history and automatically decline solicitations for products or religions. The learning unit can also use a generation AI to learn information about the user. For example, the generation AI receives user information as input and outputs the learning results. As a result, the learning unit automatically declines unnecessary solicitations, improving user convenience. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can use AI to learn information about the user and automatically decline unnecessary solicitations.

[0073] The questioning unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user emotions. For example, the questioning unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user emotions. For example, if the user is feeling stressed, the questioning unit prioritizes simple and to-the-point questions. Furthermore, if the user is relaxed, the questioning unit can ask detailed questions to elicit more information. Furthermore, if the user is in a hurry, the questioning unit can ask the most important questions first to quickly grasp the user's requirements. This allows for more appropriate questions to be asked by adjusting the order and content of questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the questioning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the questioning unit can estimate the user's emotions and adjust the order and content of questions using an AI.

[0074] The questioning unit can select the most appropriate question by referring to the past call history. The questioning unit, for example, selects the most appropriate question by referring to the past call history. For example, the questioning unit automatically selects the most appropriate question based on questions that the user has frequently received in the past. The questioning unit can also ask related questions by referring to the answers the user has given in the past. The questioning unit can also analyze patterns of questions the user has received in the past and select the most effective question. This makes it possible to ask more effective questions by referring to the past call history. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can use AI to select the most appropriate question by referring to the past call history.

[0075] The question unit can customize the content of the questions taking into account the recipient's attribute information. The question unit customizes the content of the questions taking into account the recipient's attribute information, for example. For example, if the recipient is a business associate, the question unit prioritizes business-related questions. Furthermore, if the recipient is a family member or friend, the question unit can also prioritize personal questions. Furthermore, the question unit can select appropriate questions depending on the recipient's age and gender. In this way, customizing the content of the questions based on the recipient's attribute information enables more appropriate questions. Some or all of the above-described processing in the question unit may be performed, for example, using AI, or may be performed without using AI. For example, the question unit can customize the content of the questions taking into account the recipient's attribute information using AI.

[0076] The questioning unit can adjust the level of detail of the question based on the time of the incoming call. The questioning unit adjusts the level of detail of the question based on, for example, the time of the incoming call. For example, in the case of an incoming call at night, the questioning unit asks concise and to the point questions. In addition, in the case of an incoming call during the day, the questioning unit can ask detailed questions to elicit more information. In addition, in the case of an incoming call early in the morning, the questioning unit can prioritize important questions and quickly grasp the requirements. In this way, adjusting the level of detail of the question based on the time of the incoming call enables more appropriate questions to be asked. Some or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can adjust the level of detail of the question based on the time of the incoming call using AI.

[0077] The questioning unit can estimate the user's emotions and select a question format based on the estimated user emotions. For example, the questioning unit can estimate the user's emotions and select a question format based on the estimated user emotions. For example, if the user is feeling stressed, the questioning unit can prioritize voice questions to reduce the user's burden. Furthermore, if the user is relaxed, the questioning unit can ask text questions to elicit detailed answers. Furthermore, if the user is in a hurry, the questioning unit can quickly ask questions by combining both voice and text. This enables more appropriate questions to be asked by selecting a question format based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the questioning unit may be performed using AI, or may be performed without AI. For example, the questioning unit can estimate the user's emotions and select a question format using AI.

[0078] The interrogation unit can ask highly relevant questions taking into account the geographical location information of the recipient. The interrogation unit can ask highly relevant questions taking into account, for example, the geographical location information of the recipient. For example, if the recipient is in a specific area, the interrogation unit can ask questions related to the area. Furthermore, if the recipient is traveling, the interrogation unit can ask questions related to the travel. Furthermore, if the recipient is at home, the interrogation unit can ask questions related to the home. In this way, by asking highly relevant questions based on the geographical location information of the recipient, more appropriate questions can be asked. Some or all of the above-described processing in the interrogation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interrogation unit can ask highly relevant questions taking into account the geographical location information of the recipient using AI.

[0079] The questioning unit can analyze the social media activity of the recipient and ask related questions. The questioning unit, for example, analyzes the social media activity of the recipient and asks related questions. For example, the questioning unit asks related questions based on content posted by the recipient on social media. The questioning unit can also analyze the recipient's social media activity and ask questions that the recipient might be interested in. The questioning unit can also ask related questions with reference to the activity of the recipient's friends on social media. In this way, asking related questions based on the recipient's social media activity enables more appropriate questions. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can analyze the recipient's social media activity and ask related questions using AI.

[0080] The questioning unit can customize the question content by reflecting past feedback. The questioning unit customizes the question content by reflecting past feedback, for example. For example, the questioning unit improves the question content based on feedback provided by the user in the past. The questioning unit can also avoid questions that the user has been dissatisfied with in the past and ask questions that will provide high satisfaction. The questioning unit can also analyze the user's past feedback and select optimal question content. This enables more appropriate questions to be asked by reflecting past feedback. Some or all of the above-described processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can customize the question content by reflecting past feedback using AI.

[0081] The text conversion unit can estimate the user's emotion and adjust the text expression method based on the estimated user's emotion. For example, the text conversion unit can estimate the user's emotion and adjust the text expression method based on the estimated user's emotion. For example, if the user is stressed, the text conversion unit can generate concise and to-the-point text. Furthermore, if the user is relaxed, the text conversion unit can generate text that includes detailed explanations. Furthermore, if the user is in a hurry, the text conversion unit can generate summarized text that can be quickly understood. This adjusts the text expression method according to the user's emotion, thereby generating more appropriate text. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the text conversion unit may be performed using, for example, AI, or without AI. For example, the text conversion unit can estimate the user's emotion and adjust the text expression method using AI.

[0082] The text conversion unit can adjust the level of detail of the text based on the importance of the requirement. The text conversion unit adjusts the level of detail of the text based on, for example, the importance of the requirement. For example, the text conversion unit generates text including a detailed explanation for an important requirement. The text conversion unit can also generate concise, to-the-point text for a general requirement. The text conversion unit can also generate summarized text for an urgent requirement so that it can be quickly understood. In this way, by adjusting the level of detail of the text according to the importance of the requirement, more appropriate text is generated. Some or all of the above-described processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can adjust the level of detail of the text based on the importance of the requirement using AI.

[0083] The text conversion unit can apply different text conversion algorithms depending on the category of the requirement. For example, the text conversion unit can apply different text conversion algorithms depending on the category of the requirement. For example, the text conversion unit can generate text including technical terms for business-related requirements. For personal requirements, the text conversion unit can also generate text using familiar expressions. For urgent requirements, the text conversion unit can also generate summarized text for quick understanding. In this way, applying different text conversion algorithms depending on the category of the requirement generates more appropriate text. Some or all of the above-mentioned processing in the text conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the text conversion unit can use AI to apply different text conversion algorithms depending on the category of the requirement.

[0084] The text conversion unit can improve the accuracy of the text by referring to past text conversion results. The text conversion unit, for example, improves the accuracy of the text by referring to past text conversion results. For example, the text conversion unit analyzes past text conversion results and corrects errors to improve accuracy. The text conversion unit can also select an optimal expression method based on past text conversion results. The text conversion unit can also generate text that matches the user's preferences by referring to past text conversion results. In this way, the accuracy of the text is improved by referring to past text conversion results. Some or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can use AI to improve the accuracy of the text by referring to past text conversion results.

[0085] The text conversion unit can estimate the user's emotion and adjust the length of the text based on the estimated user's emotion. For example, the text conversion unit can estimate the user's emotion and adjust the length of the text based on the estimated user's emotion. For example, if the user is stressed, the text conversion unit can generate short, to-the-point text. Also, if the user is relaxed, the text conversion unit can generate longer text with detailed explanations. Also, if the user is in a hurry, the text conversion unit can generate summarized text for quick understanding. This allows for more appropriate text to be generated by adjusting the length of the text according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the text conversion unit can be performed using, for example, AI, or without AI. For example, the text conversion unit can estimate the user's emotion and adjust the length of the text using AI.

[0086] The text conversion unit can determine the priority of text based on the time of submission of requirements. The text conversion unit determines the priority of text based on, for example, the time of submission of requirements. For example, the text conversion unit gives the highest priority to text conversion for urgent requirements. The text conversion unit can also quickly convert important requirements into text. The text conversion unit can also convert general requirements into text later than other requirements. In this way, by determining the priority of text based on the time of submission of requirements, more appropriate text is generated. Some or all of the above-described processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can use AI to determine the priority of text based on the time of submission of requirements.

[0087] The text conversion unit can adjust the order of the text based on the relevance of the requirements. The text conversion unit, for example, adjusts the order of the text based on the relevance of the requirements. For example, the text conversion unit prioritizes highly relevant requirements for text conversion and adjusts the order. The text conversion unit can also put less relevant requirements on hold for text conversion and adjust the order. The text conversion unit can also convert requirements into text in an optimal order based on the relevance of the requirements. In this way, by adjusting the order of the text according to the relevance of the requirements, more appropriate text is generated. Some or all of the above-described processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can use AI to adjust the order of the text based on the relevance of the requirements.

[0088] The text conversion unit can adjust the use of technical terms in the text according to the user's level of expertise. The text conversion unit can adjust the use of technical terms in the text according to the user's level of expertise, for example. For example, if the user has technical knowledge, the text conversion unit can generate text including technical terms. Furthermore, if the user does not have technical knowledge, the text conversion unit can generate text using easy-to-understand expressions. The text conversion unit can also select an optimal expression method according to the user's level of expertise. In this way, more appropriate text is generated by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the text conversion unit can be performed using, for example, AI, or can be performed without using AI. For example, the text conversion unit can use AI to adjust the use of technical terms in the text according to the user's level of expertise.

[0089] The transmission unit can estimate the user's emotion and adjust the timing of transmission based on the estimated user's emotion. The transmission unit, for example, estimates the user's emotion and adjusts the timing of transmission based on the estimated user's emotion. For example, if the user is feeling stressed, the transmission unit can transmit at an appropriate time. Furthermore, if the user is relaxed, the transmission unit can transmit immediately. Furthermore, if the user is in a hurry, the transmission unit can transmit quickly. This enables more appropriate transmission by adjusting the timing of transmission according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can estimate the user's emotion and adjust the timing of transmission using AI.

[0090] The transmission unit can select the optimal transmission method by referring to past transmission history. The transmission unit, for example, selects the optimal transmission method by referring to past transmission history. For example, the transmission unit selects the optimal transmission method based on transmission methods used by the user in the past. The transmission unit can also select an effective transmission method from the user's past transmission history. The transmission unit can also analyze the user's past transmission history and select the most efficient transmission method. This enables more effective transmission by referring to the past transmission history. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can use AI to refer to the past transmission history and select the optimal transmission method.

[0091] The sending unit can customize the content to be sent taking into account the attribute information of the recipient. The sending unit customizes the content to be sent taking into account, for example, the attribute information of the recipient. For example, if the recipient is a business associate, the sending unit may prioritize sending business-related content. Furthermore, if the recipient is a family member or friend, the sending unit may prioritize sending personal content. Furthermore, the sending unit can select appropriate content to be sent depending on the age and gender of the recipient. In this way, customizing the content to be sent based on the attribute information of the recipient enables more appropriate sending. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can customize the content to be sent taking into account the attribute information of the recipient using AI.

[0092] The transmitting unit can adjust the level of detail of the transmission based on the time zone of the transmission. The transmitting unit adjusts the level of detail of the transmission based on, for example, the time zone of the transmission. For example, when transmitting at night, the transmitting unit transmits concise content that is to the point. The transmitting unit can also transmit detailed content when transmitting during the day. The transmitting unit can also prioritize important content when transmitting early in the morning. In this way, adjusting the level of detail of the transmission according to the time zone of the transmission enables more appropriate transmission. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can adjust the level of detail of the transmission based on the time zone of the transmission using AI.

[0093] The transmission unit can estimate the user's emotions and select a transmission format based on the estimated user emotions. For example, the transmission unit can estimate the user's emotions and select a transmission format based on the estimated user emotions. For example, if the user is feeling stressed, the transmission unit can prioritize voice transmission to reduce the user's burden. Furthermore, if the user is relaxed, the transmission unit can also transmit text to provide detailed information. Furthermore, if the user is in a hurry, the transmission unit can combine both voice and text to quickly transmit. This enables more appropriate transmission by selecting a transmission format based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the transmission unit may be performed using AI, or may be performed without AI. For example, the transmission unit can estimate the user's emotions and select a transmission format using AI.

[0094] The transmitting unit can perform highly relevant transmission by taking into account the geographical location information of the recipient. The transmitting unit can perform highly relevant transmission by taking into account, for example, the geographical location information of the recipient. For example, if the recipient is in a specific area, the transmitting unit can transmit information related to that area. Furthermore, if the recipient is traveling, the transmitting unit can also transmit travel-related information. Furthermore, if the recipient is at home, the transmitting unit can also transmit home-related information. This enables more appropriate transmission by performing highly relevant transmission based on the geographical location information of the recipient. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can perform highly relevant transmission by using AI and taking into account the geographical location information of the recipient.

[0095] The sending unit can analyze the recipient's social media activity and perform relevant transmission. The sending unit, for example, analyzes the recipient's social media activity and performs relevant transmission. For example, the sending unit can send relevant information based on content posted by the recipient on social media. The sending unit can also analyze the recipient's social media activity and send information that the recipient is likely to be interested in. The sending unit can also send relevant information by referring to the activities of the recipient's friends on social media. This enables more appropriate transmission by performing relevant transmission based on the recipient's social media activity. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can use AI to analyze the recipient's social media activity and perform relevant transmission.

[0096] The transmission unit can customize the transmission content by reflecting past feedback. The transmission unit customizes the transmission content by reflecting past feedback, for example. For example, the transmission unit improves the transmission content based on feedback provided by the user in the past. The transmission unit can also avoid transmission content that the user was dissatisfied with in the past and transmit content that satisfies the user. The transmission unit can also analyze the user's past feedback and select optimal transmission content. This enables more appropriate transmission by reflecting past feedback. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can customize the transmission content by using AI to reflect past feedback.

[0097] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, the learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can prioritize learning data related to stress reduction. Also, if the user is relaxed, the learning unit can prioritize learning data related to relaxation. Also, if the user is in a hurry, the learning unit can prioritize learning data that allows for a quick response. This enables more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, or may be performed without using an AI. For example, the learning unit can estimate the user's emotions and select training data using an AI.

[0098] The learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, optimizes the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data and selects an optimal algorithm. The learning unit can also adjust parameters of the algorithm based on past learning data. The learning unit can also improve the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can optimize the learning algorithm by referring to past learning data using AI.

[0099] The learning unit can update the learning data by reflecting user feedback. The learning unit, for example, updates the learning data by reflecting user feedback. For example, the learning unit updates the learning data based on feedback provided by the user. The learning unit can also analyze user feedback and improve the accuracy of the learning data. The learning unit can also optimize the learning data by referring to user feedback. In this way, the accuracy of the learning data is improved by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can update the learning data by using AI to reflect user feedback.

[0100] The learning unit can analyze the user's lifestyle rhythm and select the optimal timing for studying. The learning unit, for example, analyzes the user's lifestyle rhythm and selects the optimal timing for studying. For example, the learning unit analyzes the user's lifestyle rhythm and selects the optimal timing for studying. The learning unit can also adjust the frequency of studying based on the user's lifestyle rhythm. The learning unit can also maximize the effectiveness of studying by referring to the user's lifestyle rhythm. This enables more effective studying by selecting the timing for studying based on the user's lifestyle rhythm. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can use AI to analyze the user's lifestyle rhythm and select the optimal timing for studying.

[0101] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can reduce the frequency of learning to reduce the burden. Furthermore, if the user is relaxed, the learning unit can increase the frequency of learning to improve effectiveness. Furthermore, if the user is in a hurry, the learning unit can adjust the frequency of learning to respond quickly. This enables more appropriate learning by adjusting the frequency of learning according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can estimate the user's emotions and adjust the frequency of learning using an AI.

[0102] The learning unit can weight the learning data based on the time when the incoming call history was submitted. The learning unit weights the learning data based on, for example, the time when the incoming call history was submitted. For example, the learning unit prioritizes learning and weighting recent incoming call histories. The learning unit can also prioritize learning and weighting important incoming call histories. The learning unit can also prioritize learning and weighting urgent incoming call histories. In this way, weighting the learning data based on the time when the incoming call history was submitted enables more appropriate learning. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can use AI to weight the learning data based on the time when the incoming call history was submitted.

[0103] The learning unit can integrate information from different data sources to enrich the training data. For example, the learning unit integrates information from different data sources to enrich the training data. For example, the learning unit integrates information from different data sources to enrich the training data. The learning unit can also analyze information from different data sources and select optimal training data. The learning unit can also improve the accuracy of the training data by referring to information from different data sources. In this way, the accuracy of the training data is improved by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can use AI to integrate information from different data sources to enrich the training data.

[0104] The learning unit can customize the learning content based on the user's living situation. The learning unit customizes the learning content based on the user's living situation, for example. For example, the learning unit analyzes the user's living situation and selects optimal learning content. The learning unit can also customize the learning content based on the user's living situation. The learning unit can also maximize the effectiveness of learning by referring to the user's living situation. This enables more effective learning by customizing the learning content based on the user's living situation. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can customize the learning content based on the user's living situation using AI. === Hard Collateral 1-1 === Each of the multiple elements including the interrogator, text converter, transmitter, and learning unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the interrogator is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the text converter is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the transmitter is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the interrogator, text converter, transmitter, and learning unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the interrogator is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the text converter is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the transmitter is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the interrogator, text converter, transmitter, and learning unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the interrogator is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the text converter is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the transmitter is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the interrogator, text converter, transmitter, and learner described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the interrogator is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the text converter is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the transmitter is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the learner is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The questioning unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize simple and to-the-point questions. Also, if the user is relaxed, it can ask detailed questions to elicit more information. Furthermore, if the user is in a hurry, it can ask the most important questions first to quickly grasp the user's requirements. This allows for more appropriate questions to be asked by adjusting the order and content of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the questioning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the questioning unit can estimate the user's emotions and adjust the order and content of questions using an AI.

[0107] The questioning unit can select the most appropriate question by referring to the past call history. For example, the most appropriate question can be automatically selected based on questions that the user has frequently received in the past. Also, the questioning unit can ask related questions by referring to the answers the user has given in the past. Furthermore, the questioning unit can analyze the patterns of questions the user has received in the past and select the most effective question. This makes it possible to ask more effective questions by referring to the past call history. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can use AI to refer to the past call history and select the most appropriate question.

[0108] The question unit can customize the questions taking into account the recipient's attribute information. For example, if the recipient is a business associate, business-related questions can be prioritized. Also, if the recipient is a family member or friend, personal questions can be prioritized. Furthermore, appropriate questions can be selected depending on the recipient's age and gender. In this way, customizing the questions based on the recipient's attribute information enables more appropriate questions to be asked. Some or all of the above-described processing in the question unit may be performed using, for example, AI, or may be performed without using AI. For example, the question unit can customize the questions taking into account the recipient's attribute information using AI.

[0109] The questioning unit can adjust the level of detail of the questions based on the time of the call. For example, for calls received at night, simple, to-the-point questions can be asked. For calls received during the day, detailed questions can be asked to elicit more information. Furthermore, for calls received early in the morning, important questions can be prioritized to quickly grasp the requirements. In this way, adjusting the level of detail of the questions based on the time of the call enables more appropriate questions to be asked. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can use AI to adjust the level of detail of the questions based on the time of the call.

[0110] The questioning unit can estimate the user's emotions and select the question format based on the estimated user emotions. For example, if the user is stressed, voice questions can be prioritized to reduce the user's burden. Alternatively, if the user is relaxed, text questions can be posed to elicit detailed answers. Furthermore, if the user is in a hurry, a question can be posed quickly by combining both voice and text. This allows for more appropriate questions to be posed by selecting the question format according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or without AI. For example, the questioning unit can estimate the user's emotions and select the question format using AI.

[0111] The text conversion unit can estimate the user's emotions and adjust the way the text is expressed based on the estimated user's emotions. For example, if the user is stressed, it can generate concise, to-the-point text. If the user is relaxed, it can generate text that includes detailed explanations. Furthermore, if the user is in a hurry, it can generate summarized text that can be quickly understood. This allows for more appropriate text to be generated by adjusting the way the text is expressed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the text conversion unit can be performed using, for example, AI, or without AI. For example, the text conversion unit can estimate the user's emotions and adjust the way the text is expressed using AI.

[0112] The text conversion unit can adjust the level of detail of the text based on the importance of the requirement. For example, for important requirements, it can generate text including detailed explanations. For general requirements, it can generate concise, to-the-point text. Furthermore, for urgent requirements, it can generate summarized text that can be quickly understood. In this way, by adjusting the level of detail of the text according to the importance of the requirement, more appropriate text can be generated. Some or all of the above-described processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can use AI to adjust the level of detail of the text based on the importance of the requirement.

[0113] The text conversion unit can apply different text conversion algorithms depending on the category of the requirement. For example, for business-related requirements, it can generate text containing technical terms. For personal requirements, it can generate text using familiar expressions. Furthermore, for urgent requirements, it can generate summarized text that can be understood quickly. In this way, by applying different text conversion algorithms depending on the category of the requirement, more appropriate text can be generated. Some or all of the above-mentioned processing in the text conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the text conversion unit can use AI to apply different text conversion algorithms depending on the category of the requirement.

[0114] The text conversion unit can improve the accuracy of the text by referring to past text conversion results. For example, the text conversion unit can analyze past text conversion results and correct errors to improve accuracy. The text conversion unit can also select the optimal expression method based on past text conversion results. Furthermore, the text conversion unit can generate text tailored to the user's preferences by referring to past text conversion results. In this way, the accuracy of the text is improved by referring to past text conversion results. Some or all of the above-mentioned processing in the text conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the text conversion unit can use AI to improve the accuracy of the text by referring to past text conversion results.

[0115] The transmission unit can estimate the user's emotions and adjust the timing of transmission based on the estimated user emotions. For example, if the user is feeling stressed, the transmission can be performed at an appropriate time. Also, if the user is relaxed, the transmission can be performed immediately. Furthermore, if the user is in a hurry, the transmission can be performed quickly. This allows for more appropriate transmission by adjusting the timing of transmission according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transmission unit can be performed using AI, for example, or without AI. For example, the transmission unit can estimate the user's emotions and adjust the timing of transmission using AI.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The questioning section asks the caller about the requirements. For example, it asks questions such as "What is your business?" or "Could you please give me more details about that?" It can also ask whether the caller will call back and for contact information. Step 2: The text generator compiles the answers obtained by the question generator into text. For example, it compiles information such as the requirements, details, whether or not a reply will be received, and contact information into text. It can also use a generation AI to summarize the answers. Step 3: The sender sends the text compiled by the text converter to a messaging service, such as SMS, email, or a chat app. Generative AI can also be used to optimize the content of the message. Step 4: The learning unit learns user information and automatically declines unwanted solicitations. For example, it can learn the user's preferences and past response history and automatically decline solicitations for products or religion. It can also use generative AI to learn user information.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a questioning section that asks about the content of the requirements for the incoming call; a text generation unit that compiles the answers obtained by the question generation unit into text; a sending unit that sends the text compiled by the text conversion unit to a messaging service; A learning unit that learns user information and automatically declines unnecessary solicitations. A system characterized by:

2. The interrogation unit Ask for details about the requirements, whether they will call you back, and contact information.

2. The system of claim 1.

3. The text conversion unit The answers obtained by the questioning section are compiled into a text.

2. The system of claim 1.

4. The transmission unit Sending the text compiled by the text converter to a messaging service 2. The system of claim 1.

5. The learning unit Learns user information and automatically refuses solicitations for products or religion 2. The system of claim 1.

6. The interrogation unit Infer user sentiment and adjust the order and content of questions based on the estimated sentiment 2. The system of claim 1.

7. The interrogation unit Select the best question by looking at past call history 2. The system of claim 1.

8. The interrogation unit Customize questions based on the caller's attributes 2. The system of claim 1.

Images