system

Abstract

The system according to the embodiment aims to lower the barriers for users to make calls, and in particular to enable people with hearing impairments to make calls easily. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a call unit, and a feedback unit. The reception unit receives a message from a user and information about the call partner. The generation unit generates prompts for conducting natural conversations based on the information received by the reception unit. The call unit conducts a call based on the prompts generated by the generation unit. The feedback unit provides feedback to the user about the content and results of the call conducted by the call unit.

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 poses a high hurdle for users when making calls, making calls particularly difficult for those with hearing impairments.

[0005] The system according to the embodiment aims to lower the barriers for users to make calls, and in particular to enable people with hearing impairments to make calls easily. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a call unit, and a feedback unit. The reception unit receives a message and information about the call partner from a user. The generation unit generates a prompt for conducting a natural conversation based on the information received by the reception unit. The call unit conducts a call based on the prompt generated by the generation unit. The feedback unit provides feedback to the user on the content and results of the call conducted by the call unit. [Effects of the Invention]

[0007] The system according to the embodiment can lower the barriers for users to make calls, making it easier for even those with hearing impairments to make calls. [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) A call answering system according to an embodiment of the present invention uses a generation AI to conduct natural conversations based on the user's designated purpose and caller information. In a call answering system, the user specifies the purpose and caller, and the generation AI conducts natural conversations based on that information and handles the call. After the call ends, the generation AI provides feedback on the call content and results to the user. For example, in a call answering system, a user inputs specific matters, such as making a restaurant reservation or calling an overseas business partner. This information is input into the generation AI. The generation AI then analyzes the input information and generates prompts for natural conversation. The generation AI designs an appropriate conversation flow based on the user's purpose and caller information. The call begins based on the conversation flow designed by the generation AI. The generation AI makes the call on behalf of the user and engages in a natural conversation with the other party. After the call ends, the generation AI provides feedback on the call content and results to the user. This allows the call answering system to handle difficult calls, such as making restaurant reservations or calling overseas. It also enables hearing-impaired individuals to make phone calls without the need for a proxy. This allows the call answering system to use the generation AI to have a natural conversation and take over the call based on the purpose and caller information specified by the user. For example, even users who are not good at foreign languages ​​can make calls smoothly by having the generation AI take over the call. It also enables people with hearing impairments to make calls without the need for a proxy. This lowers the barrier to making calls and makes the service accessible to more people.

[0029] A call answering system according to an embodiment includes a reception unit, a generation unit, a call unit, and a feedback unit. The reception unit receives a user's request and information about the other party. The user inputs information such as the purpose of the call and the other party's contact information. The reception unit receives a user's specific request, such as "I would like to make a restaurant reservation" or "I would like to contact a business partner overseas." The generation unit uses a generation AI to generate prompts for a natural conversation based on the information received by the reception unit. For example, the generation AI designs an appropriate conversation flow based on the user's request and the other party's information. For example, in the case of a restaurant reservation, the generation AI designs a conversation flow including steps such as "confirm the reservation," "confirm the desired date and time," and "confirm the number of people." The call unit makes a call based on the prompts generated by the generation unit. For example, in the call unit, the generation AI makes a call on behalf of the user and conducts a natural conversation with the other party. For example, in the case of a restaurant reservation, the generation AI conducts a natural conversation such as "Hello, I would like to make a reservation." The feedback unit provides feedback to the user on the content and results of the call made by the call unit. The feedback unit reports specific results to the user, such as "The reservation has been completed" or "Contact with the business partner has been established." As a result, the call answering system according to the embodiment allows the generation AI to have a natural conversation based on the user's purpose and the call recipient's information, handle the call on behalf of the user, and provide feedback on the content and results of the call.

[0030] The generation unit generates prompts using a generation AI. The generation unit uses the generation AI to generate prompts for natural conversation based on the information received by the reception unit. The generation AI generates prompts using a model such as GPT-4 (registered trademark). The generation AI designs an appropriate conversation flow based on the user's purpose and information about the other party. For example, in the case of a restaurant reservation, the generation AI designs a conversation flow including steps such as "confirm the reservation," "confirm the desired date and time," and "confirm the number of people." The generation AI can generate a prompt such as "Please confirm the reservation." As a result, the use of the generation AI improves the accuracy of prompt generation.

[0031] The call unit analyzes the reaction of the other party during the call and generates a response. The call unit analyzes the reaction of the other party during the call and generates an appropriate response. The call unit analyzes the reaction of the other party using, for example, voice analysis technology. For example, the call unit can analyze the tone and speed of the other party's voice to infer the other party's emotions and intentions. The call unit can also analyze the other party's facial expression using facial expression analysis technology to infer the other party's reaction. For example, the call unit can infer the other party's emotions based on changes in the other party's facial expression. Furthermore, the call unit can analyze the other party's text message using text analysis technology and generate an appropriate response. For example, the call unit can analyze the content of the other party's text message and generate an appropriate response. In this way, the call unit realizes a natural conversation by generating an appropriate response according to the other party's reaction during the call.

[0032] The feedback unit records the contents of the call and provides feedback to the user. The feedback unit provides feedback to the user on the contents and results of the call made by the call unit. The feedback unit can, for example, save the contents of the call as an audio recording or a text recording. For example, the feedback unit can record the contents of the call as an audio recording and provide it to the user. The feedback unit can also record the contents of the call as text and provide it to the user. For example, the feedback unit can summarize the contents of the call and report it to the user. Furthermore, the feedback unit can report the result of the call to the user. For example, the feedback unit can report to the user specific results such as "The reservation has been completed" or "Contact has been made with the business partner." In this way, the feedback unit can check the result of the call by recording the contents of the call and providing feedback to the user.

[0033] The reception unit accepts the user's purpose for the call and the other party's contact information. The reception unit accepts the user's business and information about the other party. The user inputs information such as the purpose of the call and the other party's contact information. The reception unit accepts the user's input of a specific business purpose, such as "I would like to make a restaurant reservation" or "I would like to contact a business partner overseas." The reception unit prepares for the call by accepting the user's purpose for the call and the other party's contact information. The reception unit then prepares for the call by accepting the user's purpose for the call and the other party's contact information.

[0034] The generation unit generates prompts according to specific matters, such as making a restaurant reservation or calling an overseas business partner. The generation unit uses a generation AI to generate prompts for natural conversation based on the information received by the reception unit. For example, the generation unit uses the generation AI to design an appropriate conversation flow based on the user's matter and information about the call recipient. For example, in the case of a restaurant reservation, the generation AI designs a conversation flow including steps such as "confirm the reservation," "confirm the desired date and time," and "confirm the number of people." For example, the generation AI can generate a prompt such as "Please confirm the reservation." In addition, in the case of a call with an overseas business partner, the generation unit can generate prompts including business terms. For example, the generation AI can generate a prompt such as "I would like to discuss the details of the transaction." In this way, the generation unit generates prompts according to specific matters, enabling an appropriate conversation according to the purpose of the call.

[0035] The reception unit can analyze the user's past call history and select the reception method. The reception unit analyzes the user's past call history and selects the optimal reception method. For example, the reception unit automatically displays as candidates messages that the user has frequently used in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest messages that will be used during a specific time period based on the user's past call history. In this way, the reception unit can provide the optimal reception method by analyzing the user's past call history.

[0036] The reception unit can filter messages based on the user's current situation or areas of interest when receiving the messages. The reception unit filters messages based on the user's current situation or areas of interest when receiving the messages. For example, when the user is in his / her current location, the reception unit preferentially displays messages related to the area. The reception unit can also filter and display related messages based on the user's areas of interest. Furthermore, the reception unit can suggest optimal messages based on the user's current situation (time of day, weather, etc.). In this way, the reception unit can preferentially receive highly relevant messages by filtering based on the user's current situation and areas of interest.

[0037] The reception unit can select the reception means according to the user's input method when receiving a message. The reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving a message. For example, when the user inputs the message by voice, the reception unit uses voice recognition technology to receive the message. Furthermore, when the user inputs the message in text, the reception unit can also use text analysis technology to receive the message. Furthermore, when the user inputs the message in image form, the reception unit can also use image recognition technology to receive the message. This allows the reception unit to select the optimal reception means according to the user's input method, thereby improving user convenience.

[0038] When accepting a message, the reception unit can prioritize accepting highly relevant messages by taking into consideration the user's geographical location information. When accepting a message, the reception unit prioritizes accepting highly relevant messages by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize accepting messages related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize accepting messages related to the user's travel destination. Furthermore, when the user is at home, the reception unit can also prioritize accepting messages related to the area around the user's home. In this way, the reception unit can prioritize accepting highly relevant messages by taking into consideration the user's geographical location information.

[0039] The reception unit can analyze the user's social media posts when receiving a message and accept related messages. The reception unit can analyze the user's social media activities when receiving a message and accept related messages. For example, the reception unit can accept messages related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and accept related messages. Furthermore, the reception unit can accept related messages by referring to the activities of the user's friends on social media. In this way, the reception unit can prioritize accepting related messages by analyzing the user's social media activities.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a message. The reception unit customizes the reception method by reflecting the user's past feedback when receiving a message. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method. In this way, the reception unit can provide an optimal reception method by reflecting the user's past feedback.

[0041] The generation unit can adjust the level of detail of the prompt based on the importance of the matter when generating the prompt. The generation unit uses the generation AI to adjust the level of detail of the prompt based on the importance of the matter when generating the prompt. For example, the generation unit generates a detailed prompt for an important matter. The generation unit can also generate a standard prompt for an ordinary matter. Furthermore, the generation unit can generate a concise prompt that can be quickly responded to for an urgent matter. In this way, the generation unit can provide an appropriate prompt by adjusting the level of detail of the prompt based on the importance of the matter.

[0042] The generation unit can apply different generation algorithms depending on the message category when generating a prompt. The generation unit uses a generation AI to apply different generation algorithms depending on the message category when generating a prompt. For example, in the case of a restaurant reservation, the generation unit generates a prompt that includes steps such as confirming the reservation and confirming the desired date and time. In addition, in the case of a call with an overseas business partner, the generation unit can also generate a prompt that includes business terms. Furthermore, in the case of a personal message, the generation unit can generate a prompt with casual expression. In this way, the generation unit can provide an appropriate prompt by applying different generation algorithms depending on the message category.

[0043] When generating a prompt, the generation unit can improve the accuracy of generation by referring to the user's past prompt results. When generating a prompt, the generation unit uses the generation AI to improve the accuracy of generation by referring to the user's past prompt results. The generation unit generates an optimal prompt, for example, based on prompts that the user has used in the past. The generation unit can also generate prompts with a high success rate from the user's past prompt results. Furthermore, the generation unit can analyze the user's past prompt results and improve the accuracy of generation. In this way, the generation unit improves the accuracy of generation by referring to the user's past prompt results.

[0044] The generation unit can determine the priority of prompts based on the time of submission of the message when generating the prompts. The generation unit uses the generation AI to determine the priority of prompts based on the time of submission of the message when generating the prompts. For example, in the case of an urgent message, the generation unit can generate a prompt with the highest priority. In addition, the generation unit can also generate a prompt with a standard priority for an ordinary message. Furthermore, in the case of a future message, the generation unit can generate a prompt at a later date. In this way, the generation unit can provide appropriate prompts by determining the priority of prompts based on the time of submission of the message.

[0045] The generation unit can adjust the order of prompts based on the relevance of the subject when generating prompts. The generation unit uses the generation AI to adjust the order of prompts based on the relevance of the subject when generating prompts. The generation unit, for example, generates an important subject as the first prompt. The generation unit can also generate an ordinary subject as the next prompt. Furthermore, the generation unit can generate a less relevant subject as the last prompt. In this way, the generation unit can provide appropriate prompts by adjusting the order of prompts based on the relevance of the subject.

[0046] The generation unit can adjust the use of technical terms in the prompt according to the user's level of expertise when generating the prompt. The generation unit uses the generation AI to adjust the use of technical terms in the prompt according to the user's level of expertise when generating the prompt. For example, if the user has technical knowledge, the generation unit generates a prompt including technical terms. Furthermore, if the user has general knowledge, the generation unit can also generate a prompt using standard terms. Furthermore, if the user is a novice, the generation unit can generate a prompt using simple terms. In this way, the generation unit can provide an appropriate prompt by adjusting the use of technical terms in the prompt according to the user's level of expertise.

[0047] The call unit can analyze the other party's reaction in real time during a call and generate an optimal response. The call unit can analyze the other party's reaction in real time during a call and generate an optimal response. For example, if the other party asks a question, the call unit generates an appropriate answer in real time. The call unit can also generate a response that moves the other party to the next step if the other party agrees. Furthermore, the call unit can generate a response that proposes an alternative if the other party disagrees. This allows the call unit to generate an appropriate response by analyzing the other party's reaction in real time.

[0048] The call unit can customize the call content during a call by taking into account the attribute information of the other party. The call unit customizes the call content during a call by taking into account the attribute information of the other party. For example, if the other party is a business partner, the call unit generates call content using business terms. The call unit can also generate casual call content if the other party is a friend. Furthermore, the call unit can generate call content at a slower pace if the other party is elderly. In this way, the call unit can provide appropriate call content by taking into account the attribute information of the other party.

[0049] The call unit can improve the accuracy of the call by referring to the other party's past call history during the call. The call unit can improve the accuracy of the call by referring to the other party's past call history during the call. The call unit, for example, generates relevant call content based on what the other party has said in the past. The call unit can also reflect the other party's preferred call style from the other party's past call history. Furthermore, the call unit can analyze the other party's past call history and generate optimal call content. In this way, the call unit improves the accuracy of the call by referring to the other party's past call history.

[0050] The call unit can adjust the call content during a call by taking into account the geographical location information of the other party. The call unit adjusts the call content during a call by taking into account the geographical location information of the other party. For example, if the other party is in a specific area, the call unit generates call content related to that area. Also, if the other party is traveling, the call unit can generate call content related to the travel destination. Furthermore, if the other party is at home, the call unit can generate call content related to the area around the home. In this way, the call unit can provide appropriate call content by taking into account the geographical location information of the other party.

[0051] The call unit can analyze the social media activity of the other party during a call and reflect related information in the call. The call unit can analyze the social media activity of the other party during a call and reflect related information in the call. For example, the call unit can reflect information about places where the other party has checked in on social media in the call. The call unit can also analyze the content posted by the other party on social media and generate related call content. Furthermore, the call unit can generate related call content by referring to the activity of the other party's friends on social media. In this way, the call unit can provide appropriate call content by analyzing the other party's social media activity.

[0052] The call unit can adjust the content of the call during the call, taking into account the market value of the other party. The call unit adjusts the content of the call during the call, taking into account the market value of the other party. For example, if the other party has a high market value, the call unit generates polite and detailed content of the call. Also, if the other party has a medium market value, the call unit can generate standard content of the call. Furthermore, if the other party has a low market value, the call unit can generate concise content of the call. In this way, the call unit can provide appropriate content of the call by taking into account the market value of the other party.

[0053] The feedback unit can adjust the level of detail of the feedback based on the importance of the call when providing feedback. The feedback unit adjusts the level of detail of the feedback based on the importance of the call when providing feedback. For example, the feedback unit provides detailed feedback in the case of an important call. The feedback unit can also provide standard feedback in the case of a normal call. Furthermore, the feedback unit can also provide quick, to-the-point feedback in the case of an urgent call. In this way, the feedback unit can provide appropriate feedback by adjusting the level of detail of the feedback based on the importance of the call.

[0054] The feedback unit can apply different feedback algorithms depending on the call category when providing feedback. The feedback unit can apply different feedback algorithms depending on the call category when providing feedback. For example, in the case of a restaurant reservation, the feedback unit provides feedback such as reservation confirmation or confirmation of the desired date and time. In addition, in the case of a call with an overseas business partner, the feedback unit can provide feedback including business terms. Furthermore, in the case of a personal matter, the feedback unit can provide feedback in casual language. In this way, the feedback unit can provide appropriate feedback by applying different feedback algorithms depending on the call category.

[0055] The feedback unit can improve the accuracy of the feedback when providing feedback by referring to the user's past feedback results. The feedback unit improves the accuracy of the feedback when providing feedback by referring to the user's past feedback results. The feedback unit provides optimal feedback, for example, based on feedback provided by the user in the past. The feedback unit can also provide feedback with a high success rate based on the user's past feedback results. Furthermore, the feedback unit can analyze the user's past feedback results and improve the accuracy of the feedback. In this way, the feedback unit improves the accuracy of the feedback by referring to the user's past feedback results.

[0056] The feedback unit can determine the priority of the feedback based on the time of submission of the call when providing feedback. The feedback unit determines the priority of the feedback based on the time of submission of the call when providing feedback. For example, in the case of an urgent call, the feedback unit provides feedback with the highest priority. In addition, the feedback unit can also provide feedback with a standard priority for a normal call. Furthermore, the feedback unit can also provide feedback at a later date for a future call. In this way, the feedback unit can provide appropriate feedback by determining the priority of the feedback based on the time of submission of the call.

[0057] The feedback unit can adjust the order of feedback based on the relevance of the calls when providing feedback. The feedback unit adjusts the order of feedback based on the relevance of the calls when providing feedback. For example, the feedback unit provides feedback for important calls first. The feedback unit can also provide feedback for normal calls next. Furthermore, the feedback unit can also provide feedback for less relevant calls last. In this way, the feedback unit can provide appropriate feedback by adjusting the order of feedback based on the relevance of the calls.

[0058] The feedback unit may adjust the use of technical terms in the feedback depending on the user's level of expertise when providing feedback. The feedback unit may adjust the use of technical terms in the feedback depending on the user's level of expertise when providing feedback. For example, if the user has technical knowledge, the feedback unit may provide feedback including technical terms. Furthermore, if the user has general knowledge, the feedback unit may provide feedback using standard terms. Furthermore, if the user is a beginner, the feedback unit may provide feedback using simple terms. In this way, the feedback unit can provide appropriate feedback by adjusting the use of technical terms in the feedback depending on the user's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past call history and select the optimal reception method. For example, it can automatically display the user's frequently used calls as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest calls that will be made during a specific time period based on the user's past call history. In this way, the reception unit can provide the optimal reception method by analyzing the user's past call history.

[0061] When generating a prompt, the generation unit can adjust the level of detail of the prompt based on the importance of the matter. For example, in the case of an important matter, a detailed prompt is generated. The generation unit can also generate a standard prompt in the case of an ordinary matter. Furthermore, in the case of an urgent matter, the generation unit can generate a concise prompt that can be quickly responded to. In this way, the generation unit can provide an appropriate prompt by adjusting the level of detail of the prompt based on the importance of the matter.

[0062] The call unit can analyze the other party's reaction in real time during a call and generate an optimal response. For example, if the other party asks a question, an appropriate answer is generated in real time. The call unit can also generate a response that moves the other party to the next step if the other party agrees. Furthermore, the call unit can generate a response that proposes an alternative if the other party disagrees. This allows the call unit to generate an appropriate response by analyzing the other party's reaction in real time.

[0063] When providing feedback, the feedback unit can adjust the level of detail of the feedback based on the importance of the call. For example, in the case of an important call, detailed feedback is provided. In addition, the feedback unit can provide standard feedback in the case of a normal call. Furthermore, in the case of an urgent call, the feedback unit can provide quick, concise feedback. In this way, the feedback unit can provide appropriate feedback by adjusting the level of detail of the feedback based on the importance of the call.

[0064] When accepting messages, the reception unit can filter based on the user's current situation and areas of interest. For example, if the user is in their current location, messages related to that area are preferentially displayed. The reception unit can also filter and display related messages based on the user's areas of interest. Furthermore, the reception unit can also suggest the most appropriate message based on the user's current situation (time of day, weather, etc.). This allows the reception unit to preferentially accept highly relevant messages by filtering based on the user's current situation and areas of interest.

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

[0066] Step 1: The reception unit accepts the user's business and information about the other party. The user inputs information such as the purpose of the call and the other party's contact information. The reception unit accepts the user's input of specific business, such as "I would like to make a restaurant reservation" or "I would like to contact a business partner overseas." Step 2: The generation unit uses the generation AI to generate prompts for natural conversation based on the information received by the reception unit. For example, the generation AI designs an appropriate conversation flow based on the user's purpose and information about the person on the other end of the call. For example, in the case of a restaurant reservation, the generation AI designs a conversation flow that includes steps such as "confirming the reservation," "confirming the desired date and time," and "confirming the number of people." Step 3: The calling unit makes a call based on the prompt generated by the generating unit. In the calling unit, for example, the generating AI makes a call on behalf of the user and carries out a natural conversation with the other party. For example, in the case of making a restaurant reservation, the generating AI will have a natural conversation such as, "Hello, I'd like to make a reservation." Step 4: The feedback unit provides the user with feedback on the content and results of the call made by the call unit. The feedback unit reports specific results to the user, such as "The reservation has been completed" or "Contact with the business partner has been established."

[0067] (Example 2) A call answering system according to an embodiment of the present invention uses a generation AI to conduct natural conversations based on the user's designated purpose and caller information. In a call answering system, the user specifies the purpose and caller, and the generation AI conducts natural conversations based on that information and handles the call. After the call ends, the generation AI provides feedback on the call content and results to the user. For example, in a call answering system, a user inputs specific matters, such as making a restaurant reservation or calling an overseas business partner. This information is input into the generation AI. The generation AI then analyzes the input information and generates prompts for natural conversation. The generation AI designs an appropriate conversation flow based on the user's purpose and caller information. The call begins based on the conversation flow designed by the generation AI. The generation AI makes the call on behalf of the user and engages in a natural conversation with the other party. After the call ends, the generation AI provides feedback on the call content and results to the user. This allows the call answering system to handle difficult calls, such as making restaurant reservations or calling overseas. It also enables hearing-impaired individuals to make phone calls without the need for a proxy. This allows the call answering system to use the generation AI to have a natural conversation and take over the call based on the purpose and caller information specified by the user. For example, even users who are not good at foreign languages ​​can make calls smoothly by having the generation AI take over the call. It also enables people with hearing impairments to make calls without the need for a proxy. This lowers the barrier to making calls and makes the service accessible to more people.

[0068] A call answering system according to an embodiment includes a reception unit, a generation unit, a call unit, and a feedback unit. The reception unit receives a user's request and information about the other party. The user inputs information such as the purpose of the call and the other party's contact information. The reception unit receives a user's specific request, such as "I would like to make a restaurant reservation" or "I would like to contact a business partner overseas." The generation unit uses a generation AI to generate prompts for a natural conversation based on the information received by the reception unit. For example, the generation AI designs an appropriate conversation flow based on the user's request and the other party's information. For example, in the case of a restaurant reservation, the generation AI designs a conversation flow including steps such as "confirm the reservation," "confirm the desired date and time," and "confirm the number of people." The call unit makes a call based on the prompts generated by the generation unit. For example, in the call unit, the generation AI makes a call on behalf of the user and conducts a natural conversation with the other party. For example, in the case of a restaurant reservation, the generation AI conducts a natural conversation such as "Hello, I would like to make a reservation." The feedback unit provides feedback to the user on the content and results of the call made by the call unit. The feedback unit reports specific results to the user, such as "The reservation has been completed" or "Contact with the business partner has been established." As a result, the call answering system according to the embodiment allows the generation AI to have a natural conversation based on the user's purpose and the call recipient's information, handle the call on behalf of the user, and provide feedback on the content and results of the call.

[0069] The generation unit generates prompts using a generation AI. The generation unit uses the generation AI to generate prompts for natural conversation based on the information received by the reception unit. The generation AI generates prompts using a model such as GPT-4 (registered trademark). The generation AI designs an appropriate conversation flow based on the user's purpose and information about the other party. For example, in the case of a restaurant reservation, the generation AI designs a conversation flow including steps such as "confirm the reservation," "confirm the desired date and time," and "confirm the number of people." The generation AI can generate a prompt such as "Please confirm the reservation." As a result, the use of the generation AI improves the accuracy of prompt generation.

[0070] The call unit analyzes the reaction of the other party during the call and generates a response. The call unit analyzes the reaction of the other party during the call and generates an appropriate response. The call unit analyzes the reaction of the other party using, for example, voice analysis technology. For example, the call unit can analyze the tone and speed of the other party's voice to infer the other party's emotions and intentions. The call unit can also analyze the other party's facial expression using facial expression analysis technology to infer the other party's reaction. For example, the call unit can infer the other party's emotions based on changes in the other party's facial expression. Furthermore, the call unit can analyze the other party's text message using text analysis technology and generate an appropriate response. For example, the call unit can analyze the content of the other party's text message and generate an appropriate response. In this way, the call unit realizes a natural conversation by generating an appropriate response according to the other party's reaction during the call.

[0071] The feedback unit records the contents of the call and provides feedback to the user. The feedback unit provides feedback to the user on the contents and results of the call made by the call unit. The feedback unit can, for example, save the contents of the call as an audio recording or a text recording. For example, the feedback unit can record the contents of the call as an audio recording and provide it to the user. The feedback unit can also record the contents of the call as text and provide it to the user. For example, the feedback unit can summarize the contents of the call and report it to the user. Furthermore, the feedback unit can report the result of the call to the user. For example, the feedback unit can report to the user specific results such as "The reservation has been completed" or "Contact has been made with the business partner." In this way, the feedback unit can check the result of the call by recording the contents of the call and providing feedback to the user.

[0072] The reception unit accepts the user's purpose for the call and the other party's contact information. The reception unit accepts the user's business and information about the other party. The user inputs information such as the purpose of the call and the other party's contact information. The reception unit accepts the user's input of a specific business purpose, such as "I would like to make a restaurant reservation" or "I would like to contact a business partner overseas." The reception unit prepares for the call by accepting the user's purpose for the call and the other party's contact information. The reception unit then prepares for the call by accepting the user's purpose for the call and the other party's contact information.

[0073] The generation unit generates prompts according to specific matters, such as making a restaurant reservation or calling an overseas business partner. The generation unit uses a generation AI to generate prompts for natural conversation based on the information received by the reception unit. For example, the generation unit uses the generation AI to design an appropriate conversation flow based on the user's matter and information about the call recipient. For example, in the case of a restaurant reservation, the generation AI designs a conversation flow including steps such as "confirm the reservation," "confirm the desired date and time," and "confirm the number of people." For example, the generation AI can generate a prompt such as "Please confirm the reservation." In addition, in the case of a call with an overseas business partner, the generation unit can generate prompts including business terms. For example, the generation AI can generate a prompt such as "I would like to discuss the details of the transaction." In this way, the generation unit generates prompts according to specific matters, enabling an appropriate conversation according to the purpose of the call.

[0074] The reception unit can estimate the user's emotions and adjust the method of receiving messages based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the method of receiving messages based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept messages. This allows the reception unit to adjust the method of receiving messages according to the user's emotions, thereby enabling more appropriate reception.

[0075] The reception unit can analyze the user's past call history and select the reception method. The reception unit analyzes the user's past call history and selects the optimal reception method. For example, the reception unit automatically displays as candidates messages that the user has frequently used in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest messages that will be used during a specific time period based on the user's past call history. In this way, the reception unit can provide the optimal reception method by analyzing the user's past call history.

[0076] The reception unit can filter messages based on the user's current situation or areas of interest when receiving the messages. The reception unit filters messages based on the user's current situation or areas of interest when receiving the messages. For example, when the user is in his / her current location, the reception unit preferentially displays messages related to the area. The reception unit can also filter and display related messages based on the user's areas of interest. Furthermore, the reception unit can suggest optimal messages based on the user's current situation (time of day, weather, etc.). In this way, the reception unit can preferentially receive highly relevant messages by filtering based on the user's current situation and areas of interest.

[0077] The reception unit can select the reception means according to the user's input method when receiving a message. The reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving a message. For example, when the user inputs the message by voice, the reception unit uses voice recognition technology to receive the message. Furthermore, when the user inputs the message in text, the reception unit can also use text analysis technology to receive the message. Furthermore, when the user inputs the message in image form, the reception unit can also use image recognition technology to receive the message. This allows the reception unit to select the optimal reception means according to the user's input method, thereby improving user convenience.

[0078] The reception unit can estimate the user's emotions and determine the priority of messages to be received based on the estimated user's emotions. The reception unit estimates the user's emotions and determines the priority of messages to be received based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit can prioritize receiving important messages. Furthermore, when the user is relaxed, the reception unit can also prioritize receiving ordinary messages. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving urgent messages. In this way, the reception unit can prioritize receiving important messages by determining the priority of messages according to the user's emotions.

[0079] When accepting a message, the reception unit can prioritize accepting highly relevant messages by taking into consideration the user's geographical location information. When accepting a message, the reception unit prioritizes accepting highly relevant messages by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize accepting messages related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize accepting messages related to the user's travel destination. Furthermore, when the user is at home, the reception unit can also prioritize accepting messages related to the area around the user's home. In this way, the reception unit can prioritize accepting highly relevant messages by taking into consideration the user's geographical location information.

[0080] The reception unit can analyze the user's social media posts when receiving a message and accept related messages. The reception unit can analyze the user's social media activities when receiving a message and accept related messages. For example, the reception unit can accept messages related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and accept related messages. Furthermore, the reception unit can accept related messages by referring to the activities of the user's friends on social media. In this way, the reception unit can prioritize accepting related messages by analyzing the user's social media activities.

[0081] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a message. The reception unit customizes the reception method by reflecting the user's past feedback when receiving a message. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method. In this way, the reception unit can provide an optimal reception method by reflecting the user's past feedback.

[0082] The generation unit can estimate the user's emotions and adjust the way the prompts are expressed based on the estimated user emotions. The generation unit uses a generation AI to estimate the user's emotions and adjust the way the prompts are expressed based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate prompts with gentle expressions. Also, if the user is nervous, the generation unit can generate prompts with concise and clear expressions. Furthermore, if the user is in a hurry, the generation unit can generate prompts that allow for a quick response. In this way, the generation unit can provide more appropriate prompts by adjusting the way the prompts are expressed according to the user's emotions.

[0083] The generation unit can adjust the level of detail of the prompt based on the importance of the matter when generating the prompt. The generation unit uses the generation AI to adjust the level of detail of the prompt based on the importance of the matter when generating the prompt. For example, the generation unit generates a detailed prompt for an important matter. The generation unit can also generate a standard prompt for an ordinary matter. Furthermore, the generation unit can generate a concise prompt that can be quickly responded to for an urgent matter. In this way, the generation unit can provide an appropriate prompt by adjusting the level of detail of the prompt based on the importance of the matter.

[0084] The generation unit can apply different generation algorithms depending on the message category when generating a prompt. The generation unit uses a generation AI to apply different generation algorithms depending on the message category when generating a prompt. For example, in the case of a restaurant reservation, the generation unit generates a prompt that includes steps such as confirming the reservation and confirming the desired date and time. In addition, in the case of a call with an overseas business partner, the generation unit can also generate a prompt that includes business terms. Furthermore, in the case of a personal message, the generation unit can generate a prompt with casual expression. In this way, the generation unit can provide an appropriate prompt by applying different generation algorithms depending on the message category.

[0085] When generating a prompt, the generation unit can improve the accuracy of generation by referring to the user's past prompt results. When generating a prompt, the generation unit uses the generation AI to improve the accuracy of generation by referring to the user's past prompt results. The generation unit generates an optimal prompt, for example, based on prompts that the user has used in the past. The generation unit can also generate prompts with a high success rate from the user's past prompt results. Furthermore, the generation unit can analyze the user's past prompt results and improve the accuracy of generation. In this way, the generation unit improves the accuracy of generation by referring to the user's past prompt results.

[0086] The generation unit can estimate the user's emotions and adjust the length of the prompt based on the estimated user's emotions. The generation unit uses a generation AI to estimate the user's emotions and adjust the length of the prompt based on the estimated user's emotions. For example, the generation unit generates a detailed prompt when the user is relaxed. The generation unit can also generate a concise prompt when the user is nervous. Furthermore, the generation unit can generate a short prompt that allows a quick response when the user is in a hurry. This allows the generation unit to provide more appropriate prompts by adjusting the length of the prompt according to the user's emotions.

[0087] The generation unit can determine the priority of prompts based on the time of submission of the message when generating the prompts. The generation unit uses the generation AI to determine the priority of prompts based on the time of submission of the message when generating the prompts. For example, in the case of an urgent message, the generation unit can generate a prompt with the highest priority. In addition, the generation unit can also generate a prompt with a standard priority for an ordinary message. Furthermore, in the case of a future message, the generation unit can generate a prompt at a later date. In this way, the generation unit can provide appropriate prompts by determining the priority of prompts based on the time of submission of the message.

[0088] The generation unit can adjust the order of prompts based on the relevance of the subject when generating prompts. The generation unit uses the generation AI to adjust the order of prompts based on the relevance of the subject when generating prompts. The generation unit, for example, generates an important subject as the first prompt. The generation unit can also generate an ordinary subject as the next prompt. Furthermore, the generation unit can generate a less relevant subject as the last prompt. In this way, the generation unit can provide appropriate prompts by adjusting the order of prompts based on the relevance of the subject.

[0089] The generation unit can adjust the use of technical terms in the prompt according to the user's level of expertise when generating the prompt. The generation unit uses the generation AI to adjust the use of technical terms in the prompt according to the user's level of expertise when generating the prompt. For example, if the user has technical knowledge, the generation unit generates a prompt including technical terms. Furthermore, if the user has general knowledge, the generation unit can also generate a prompt using standard terms. Furthermore, if the user is a novice, the generation unit can generate a prompt using simple terms. In this way, the generation unit can provide an appropriate prompt by adjusting the use of technical terms in the prompt according to the user's level of expertise.

[0090] The call unit can estimate the user's emotions and adjust the method of proceeding with the call based on the estimated user's emotions. The call unit can estimate the user's emotions and adjust the method of proceeding with the call based on the estimated user's emotions. For example, if the user is nervous, the call unit can proceed with the call at a slow pace. Also, if the user is relaxed, the call unit can proceed with the call at a natural pace. Furthermore, if the user is in a hurry, the call unit can proceed with the call quickly. In this way, the call unit can adjust the method of proceeding with the call according to the user's emotions, thereby enabling a more appropriate call.

[0091] The call unit can analyze the other party's reaction in real time during a call and generate an optimal response. The call unit can analyze the other party's reaction in real time during a call and generate an optimal response. For example, if the other party asks a question, the call unit generates an appropriate answer in real time. The call unit can also generate a response that moves the other party to the next step if the other party agrees. Furthermore, the call unit can generate a response that proposes an alternative if the other party disagrees. This allows the call unit to generate an appropriate response by analyzing the other party's reaction in real time.

[0092] The call unit can customize the call content during a call by taking into account the attribute information of the other party. The call unit customizes the call content during a call by taking into account the attribute information of the other party. For example, if the other party is a business partner, the call unit generates call content using business terms. The call unit can also generate casual call content if the other party is a friend. Furthermore, the call unit can generate call content at a slower pace if the other party is elderly. In this way, the call unit can provide appropriate call content by taking into account the attribute information of the other party.

[0093] The call unit can improve the accuracy of the call by referring to the other party's past call history during the call. The call unit can improve the accuracy of the call by referring to the other party's past call history during the call. The call unit, for example, generates relevant call content based on what the other party has said in the past. The call unit can also reflect the other party's preferred call style from the other party's past call history. Furthermore, the call unit can analyze the other party's past call history and generate optimal call content. In this way, the call unit improves the accuracy of the call by referring to the other party's past call history.

[0094] The call unit can estimate the user's emotions and determine the priority of calls based on the estimated user's emotions. The call unit can estimate the user's emotions and determine the priority of calls based on the estimated user's emotions. For example, when the user is feeling stressed, the call unit can prioritize important calls. Furthermore, when the user is relaxed, the call unit can also prioritize normal calls. Furthermore, when the user is in a hurry, the call unit can also prioritize emergency calls. In this way, the call unit can prioritize important calls by determining the priority of calls according to the user's emotions.

[0095] The call unit can adjust the call content during a call by taking into account the geographical location information of the other party. The call unit adjusts the call content during a call by taking into account the geographical location information of the other party. For example, if the other party is in a specific area, the call unit generates call content related to that area. Also, if the other party is traveling, the call unit can generate call content related to the travel destination. Furthermore, if the other party is at home, the call unit can generate call content related to the area around the home. In this way, the call unit can provide appropriate call content by taking into account the geographical location information of the other party.

[0096] The call unit can analyze the social media activity of the other party during a call and reflect related information in the call. The call unit can analyze the social media activity of the other party during a call and reflect related information in the call. For example, the call unit can reflect information about places where the other party has checked in on social media in the call. The call unit can also analyze the content posted by the other party on social media and generate related call content. Furthermore, the call unit can generate related call content by referring to the activity of the other party's friends on social media. In this way, the call unit can provide appropriate call content by analyzing the other party's social media activity.

[0097] The call unit can adjust the content of the call during the call, taking into account the market value of the other party. The call unit adjusts the content of the call during the call, taking into account the market value of the other party. For example, if the other party has a high market value, the call unit generates polite and detailed content of the call. Also, if the other party has a medium market value, the call unit can generate standard content of the call. Furthermore, if the other party has a low market value, the call unit can generate concise content of the call. In this way, the call unit can provide appropriate content of the call by taking into account the market value of the other party.

[0098] The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. For example, the feedback unit can provide concise and clear feedback when the user is nervous. The feedback unit can also provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can provide quick and to the point feedback when the user is in a hurry. In this way, the feedback unit can provide more appropriate feedback by adjusting the feedback expression method according to the user's emotion.

[0099] The feedback unit can adjust the level of detail of the feedback based on the importance of the call when providing feedback. The feedback unit adjusts the level of detail of the feedback based on the importance of the call when providing feedback. For example, the feedback unit provides detailed feedback in the case of an important call. The feedback unit can also provide standard feedback in the case of a normal call. Furthermore, the feedback unit can also provide quick, to-the-point feedback in the case of an urgent call. In this way, the feedback unit can provide appropriate feedback by adjusting the level of detail of the feedback based on the importance of the call.

[0100] The feedback unit can apply different feedback algorithms depending on the call category when providing feedback. The feedback unit can apply different feedback algorithms depending on the call category when providing feedback. For example, in the case of a restaurant reservation, the feedback unit provides feedback such as reservation confirmation or confirmation of the desired date and time. In addition, in the case of a call with an overseas business partner, the feedback unit can provide feedback including business terms. Furthermore, in the case of a personal matter, the feedback unit can provide feedback in casual language. In this way, the feedback unit can provide appropriate feedback by applying different feedback algorithms depending on the call category.

[0101] The feedback unit can improve the accuracy of the feedback when providing feedback by referring to the user's past feedback results. The feedback unit improves the accuracy of the feedback when providing feedback by referring to the user's past feedback results. The feedback unit provides optimal feedback, for example, based on feedback provided by the user in the past. The feedback unit can also provide feedback with a high success rate based on the user's past feedback results. Furthermore, the feedback unit can analyze the user's past feedback results and improve the accuracy of the feedback. In this way, the feedback unit improves the accuracy of the feedback by referring to the user's past feedback results.

[0102] The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. For example, the feedback unit can provide detailed feedback when the user is relaxed. The feedback unit can also provide concise feedback when the user is nervous. Furthermore, the feedback unit can provide short feedback that quickly gets to the point when the user is in a hurry. In this way, the feedback unit can provide more appropriate feedback by adjusting the length of the feedback according to the user's emotion.

[0103] The feedback unit can determine the priority of the feedback based on the time of submission of the call when providing feedback. The feedback unit determines the priority of the feedback based on the time of submission of the call when providing feedback. For example, in the case of an urgent call, the feedback unit provides feedback with the highest priority. In addition, the feedback unit can also provide feedback with a standard priority for a normal call. Furthermore, the feedback unit can also provide feedback at a later date for a future call. In this way, the feedback unit can provide appropriate feedback by determining the priority of the feedback based on the time of submission of the call.

[0104] The feedback unit can adjust the order of feedback based on the relevance of the calls when providing feedback. The feedback unit adjusts the order of feedback based on the relevance of the calls when providing feedback. For example, the feedback unit provides feedback for important calls first. The feedback unit can also provide feedback for normal calls next. Furthermore, the feedback unit can also provide feedback for less relevant calls last. In this way, the feedback unit can provide appropriate feedback by adjusting the order of feedback based on the relevance of the calls.

[0105] The feedback unit may adjust the use of technical terms in the feedback depending on the user's level of expertise when providing feedback. The feedback unit may adjust the use of technical terms in the feedback depending on the user's level of expertise when providing feedback. For example, if the user has technical knowledge, the feedback unit may provide feedback including technical terms. Furthermore, if the user has general knowledge, the feedback unit may provide feedback using standard terms. Furthermore, if the user is a beginner, the feedback unit may provide feedback using simple terms. In this way, the feedback unit can provide appropriate feedback by adjusting the use of technical terms in the feedback depending on the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, call unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a message from the user and information about the other party of the call. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a prompt using a generation AI. The call unit is realized, for example, by the control unit 46A of the smart device 14 and analyzes the reaction of the other party during the call and generates a response. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the content of the call and provides feedback to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, call unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a message and call partner information from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a prompt using a generation AI. The call unit is realized, for example, by the control unit 46A of the smart glasses 214 and analyzes the reaction of the other party during the call and generates a response. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the call content and provides feedback to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, call unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives a message from the user and information about the other party of the call. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a prompt using a generation AI. The call unit is realized, for example, by the control unit 46A of the headset type terminal 314 and analyzes the reaction of the other party during the call and generates a response. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the content of the call and provides feedback to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, call unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a message from the user and information about the other party of the call. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a prompt using a generation AI. The call unit is realized, for example, by the control unit 46A of the robot 414 and analyzes the reaction of the other party during the call and generates a response. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the content of the call and provides feedback to the user.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can analyze the user's past call history and select the optimal reception method. For example, it can automatically display the user's frequently used calls as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest calls that will be made during a specific time period based on the user's past call history. In this way, the reception unit can provide the optimal reception method by analyzing the user's past call history.

[0108] When generating a prompt, the generation unit can adjust the level of detail of the prompt based on the importance of the matter. For example, in the case of an important matter, a detailed prompt is generated. The generation unit can also generate a standard prompt in the case of an ordinary matter. Furthermore, in the case of an urgent matter, the generation unit can generate a concise prompt that can be quickly responded to. In this way, the generation unit can provide an appropriate prompt by adjusting the level of detail of the prompt based on the importance of the matter.

[0109] The call unit can analyze the other party's reaction in real time during a call and generate an optimal response. For example, if the other party asks a question, an appropriate answer is generated in real time. The call unit can also generate a response that moves the other party to the next step if the other party agrees. Furthermore, the call unit can generate a response that proposes an alternative if the other party disagrees. This allows the call unit to generate an appropriate response by analyzing the other party's reaction in real time.

[0110] When providing feedback, the feedback unit can adjust the level of detail of the feedback based on the importance of the call. For example, in the case of an important call, detailed feedback is provided. In addition, the feedback unit can provide standard feedback in the case of a normal call. Furthermore, in the case of an urgent call, the feedback unit can provide quick, concise feedback. In this way, the feedback unit can provide appropriate feedback by adjusting the level of detail of the feedback based on the importance of the call.

[0111] The reception unit can estimate the user's emotions and adjust the method of receiving messages based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept messages. This allows the reception unit to adjust the method of receiving messages according to the user's emotions, enabling more appropriate reception.

[0112] The generation unit can estimate the user's emotions and adjust the prompt expression method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a prompt with a gentle expression. If the user is nervous, the generation unit can also generate a prompt with a concise and clear expression. Furthermore, if the user is in a hurry, the generation unit can also generate a prompt that allows a quick response. In this way, the generation unit can provide more appropriate prompts by adjusting the prompt expression method according to the user's emotions.

[0113] The call unit can estimate the user's emotions and adjust the way the call proceeds based on the estimated user's emotions. For example, if the user is nervous, the call unit can proceed with the call at a slow pace. If the user is relaxed, the call unit can also proceed with the call at a natural pace. Furthermore, if the user is in a hurry, the call unit can also proceed with the call quickly. In this way, the call unit can adjust the way the call proceeds based on the user's emotions, thereby enabling a more appropriate call.

[0114] The feedback unit can estimate the user's emotion and adjust the way the feedback is expressed based on the estimated user's emotion. For example, if the user is nervous, the feedback unit can provide concise and clear feedback. If the user is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can also provide quick and to-the-point feedback. In this way, the feedback unit can provide more appropriate feedback by adjusting the way the feedback is expressed based on the user's emotion.

[0115] The call unit can estimate the user's emotions and determine the priority of calls based on the estimated user's emotions. For example, if the user is feeling stressed, important calls can be prioritized. The call unit can also prioritize normal calls when the user is relaxed. Furthermore, the call unit can also prioritize emergency calls when the user is in a hurry. In this way, the call unit can prioritize important calls by determining the priority of calls according to the user's emotions.

[0116] When accepting messages, the reception unit can filter based on the user's current situation and areas of interest. For example, if the user is in their current location, messages related to that area are preferentially displayed. The reception unit can also filter and display related messages based on the user's areas of interest. Furthermore, the reception unit can also suggest the most appropriate message based on the user's current situation (time of day, weather, etc.). This allows the reception unit to preferentially accept highly relevant messages by filtering based on the user's current situation and areas of interest.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit accepts the user's business and information about the other party. The user inputs information such as the purpose of the call and the other party's contact information. The reception unit accepts the user's input of specific business, such as "I would like to make a restaurant reservation" or "I would like to contact a business partner overseas." Step 2: The generation unit uses the generation AI to generate prompts for natural conversation based on the information received by the reception unit. For example, the generation AI designs an appropriate conversation flow based on the user's purpose and information about the person on the other end of the call. For example, in the case of a restaurant reservation, the generation AI designs a conversation flow that includes steps such as "confirming the reservation," "confirming the desired date and time," and "confirming the number of people." Step 3: The calling unit makes a call based on the prompt generated by the generating unit. In the calling unit, for example, the generating AI makes a call on behalf of the user and carries out a natural conversation with the other party. For example, in the case of making a restaurant reservation, the generating AI will have a natural conversation such as, "Hello, I'd like to make a reservation." Step 4: The feedback unit provides the user with feedback on the content and results of the call made by the call unit. The feedback unit reports specific results to the user, such as "The reservation has been completed" or "Contact with the business partner has been established."

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 reception unit that receives a message from a user and information about a call partner; a generating unit that generates a prompt for natural conversation based on the information received by the receiving unit; a calling unit that makes a call based on the prompt generated by the generating unit; a feedback unit that feeds back to the user the contents and results of the call made by the call unit. A system characterized by:

2. The generation unit Generate prompts using generation AI 2. The system of claim 1.

3. The call unit is Analyze the other person's reactions during the call and generate a response 2. The system of claim 1.

4. The feedback unit Record the call and provide feedback to the user 2. The system of claim 1.

5. The reception unit Accepts the user's purpose for calling and the other party's contact information 2. The system of claim 1.

6. The generation unit Generate prompts tailored to specific needs, such as making a restaurant reservation or calling a business partner overseas 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the method of accepting messages based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past call history and select the reception method 2. The system of claim 1.

9. The reception unit Filter incoming messages based on the user's current situation or interests 2. The system of claim 1.

10. The reception unit When accepting a message, the acceptance method is selected according to the user's input method.

2. The system of claim 1.

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