system

Abstract

The system according to this embodiment aims to respond efficiently and appropriately to incoming calls from telephones and intercoms. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a call forwarding unit, and a response unit. The reception unit receives incoming calls from telephones and intercoms. The analysis unit analyzes the content of the call based on the incoming call received by the reception unit. The call forwarding unit forwards the call based on the content analyzed by the analysis unit. The response unit responds appropriately to nuisance calls based on the content forwarded by the call forwarding unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the response to incoming calls of a telephone or an intercom is complicated, and it is difficult to appropriately respond to nuisance calls.

[0005] The system according to the embodiment aims to efficiently and appropriately respond to incoming calls of a telephone or an intercom.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a call forwarding unit, and a response unit. The reception unit receives incoming calls from telephones and intercoms. The analysis unit analyzes the content of the call based on the incoming call received by the reception unit. The call forwarding unit forwards the call based on the content analyzed by the analysis unit. The response unit appropriately handles nuisance calls based on the content forwarded by the call forwarding unit. [Effects of the Invention]

[0007] The system according to this embodiment can respond efficiently and appropriately to incoming calls from telephones and intercoms. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The telephone answering system according to an embodiment of the present invention is a system that uses AI to handle and forward calls and inquiries from around the world. This telephone answering system connects all incoming calls to registered landlines, mobile phones, and intercoms to an AI center. The AI ​​answers the call on behalf of the user and, if necessary, forwards it to a smartphone. For example, the AI ​​can pretend to be a family member when answering the call. Next, the AI ​​analyzes the content of the call and decides whether to forward it or share it on social media. For example, if it is an important call, it will be forwarded to the user; otherwise, it will be shared on social media. Also, if the user cannot answer immediately, the AI ​​will respond in a polite manner. Furthermore, if the AI ​​determines that it is a spam call, it will take appropriate action. For example, it will brush off the spam call and share it on social media. In some cases, it can also report it to the police. As a result, the user is freed from troublesome problems related to telephones. Because the AI ​​analyzes the content of the call and takes appropriate action, the user can entrust their calls to the system with peace of mind. Also, because spam calls are handled appropriately, the user's safety is ensured. In this way, the telephone answering system solves troublesome problems related to telephones for the user and allows them to entrust their calls to the system with peace of mind.

[0029] The telephone reception system according to this embodiment comprises a reception unit, an analysis unit, a call forwarding unit, and a response unit. The reception unit receives incoming calls from telephones and intercoms. The reception unit can receive incoming calls from, for example, landlines, mobile phones, and video intercoms. The analysis unit analyzes the content of the call based on the incoming call received by the reception unit. The analysis unit analyzes the content of the call using, for example, speech recognition technology or natural language processing technology. The call forwarding unit forwards the call based on the content analyzed by the analysis unit. The call forwarding unit can, for example, forward important calls to users and share unimportant calls on social media. The response unit responds appropriately to nuisance calls based on the content forwarded by the call forwarding unit. The response unit can, for example, brush off nuisance calls appropriately and share them on social media. In some cases, it can also report to the police. As a result, the telephone reception system can efficiently receive, analyze, forward, and appropriately respond to nuisance calls from telephones and intercoms.

[0030] The reception unit receives incoming calls from telephones and intercoms. For example, it can receive calls from landlines, mobile phones, and video intercoms. Specifically, the reception unit supports various communication methods and is equipped with hardware and software to receive signals from landline telephones, mobile phone networks, and video intercoms via the internet. For landlines, the reception unit is connected to the telephone line and automatically answers upon detecting an incoming call. For mobile phones, the reception unit is equipped with a SIM card and receives calls via the mobile phone network. For video intercoms, the reception unit receives video and audio signals via an internet connection and can respond in real time. This allows the reception unit to centrally manage and efficiently handle calls from various devices. Furthermore, the reception unit can be configured to use different response methods depending on the type and origin of the call. For example, it can answer important calls immediately and play an automated response message for spam calls or calls from unknown sources. The reception unit also has a function to record call history for later review. This allows the reception unit to efficiently manage telephones and intercoms without the user missing important calls.

[0031] The analysis unit analyzes the content of incoming calls based on those received by the reception unit. The analysis unit uses technologies such as speech recognition and natural language processing to analyze the call content. Specifically, it uses speech recognition to convert incoming audio into text data, and then analyzes that text data using natural language processing. The speech recognition technology uses a highly accurate speech recognition engine to convert the speaker's voice into text in real time. Natural language processing extracts important keywords and phrases from the text data and performs analysis to understand the call content. For example, the analysis unit identifies the caller's intentions and requirements from the call content and extracts important information. The analysis unit can also refer to past call content and caller history data to identify callers and evaluate the importance of calls. This allows the analysis unit to quickly and accurately analyze the content of received calls and provide the information necessary for the next processing step. Furthermore, the analysis unit can utilize AI technology to automatically classify the call content and suggest appropriate responses. For example, it can classify calls into business-related calls, personal calls, spam calls, etc., and provide information for appropriate responses to each. This allows the analysis unit to efficiently analyze the content of phone calls, improving the overall response accuracy and efficiency of the system.

[0032] The call forwarding unit forwards calls based on the analysis performed by the analysis unit. For example, the call forwarding unit can forward important calls to users and share unimportant calls on social media. Specifically, based on the information provided by the analysis unit, the call forwarding unit selects the appropriate forwarding method according to the importance and content of the call. In the case of an important call, the call forwarding unit notifies the user directly so that they can respond immediately. For example, it can send a notification to the user's smartphone or computer to let them know that an important call has come in. The call forwarding unit can also select the optimal forwarding method considering the user's schedule and current situation. For example, if the user is in a meeting, it can send a notification after the meeting has ended. On the other hand, in the case of unimportant or spam calls, the call forwarding unit can share them on social media. For example, by posting the content of a spam call on social media and sharing the information with other users, measures can be taken to combat spam calls. The call forwarding unit also has a function to record the content of calls so that they can be reviewed later. This allows the call forwarding unit to forward calls appropriately according to their content, enabling users to respond efficiently without missing important calls. Furthermore, the intermediary unit can collect user feedback and continuously improve the accuracy and effectiveness of its intermediary methods. This allows the intermediary unit to provide users with the best possible intermediary service and improve the overall reliability and convenience of the system.

[0033] The response department will appropriately address nuisance calls based on the information relayed by the call forwarding department. For example, the response department may brush off nuisance calls appropriately and share the information on social media. In some cases, it may also report the call to the police. Specifically, the response department will analyze the content of the nuisance call and select an appropriate response method. For example, it may play an automated message to the caller urging them to hang up. It can also record the content of the nuisance call and share it on social media to share information with other users and take measures against nuisance calls. Furthermore, the response department can identify the source of the nuisance call and report it to the police if necessary. For example, if nuisance calls occur repeatedly or contain threatening content, the response department can report it to the police and request appropriate action. In this way, the response department can respond to nuisance calls quickly and appropriately, ensuring the safety and security of users. Furthermore, the response department can accumulate data on nuisance calls and use it for future countermeasures. For example, it can analyze patterns of nuisance calls and trends of callers to take preventative measures. This allows the response unit to strengthen its ability to handle nuisance calls and improve the overall reliability and security of the system.

[0034] The telephone reception system according to this embodiment includes a response unit that responds by pretending to be a family member. The response unit can respond by pretending to be a family member. For example, the response unit can respond by imitating the voice and manner of speaking of the user's family members. For example, the response unit can record the voice of the user's family members and use that voice to respond. The response unit can also learn the manner of speaking of the user's family members and respond by imitating that manner. For example, the response unit can use a generative AI to learn the manner of speaking of the user's family members. The generative AI learns the manner of speaking of the user's family members and responds by imitating that manner. As a result, the response unit can respond more naturally by pretending to be a family member.

[0035] The telephone reception system according to this embodiment includes a sharing unit that performs SNS sharing. The sharing unit can perform SNS sharing. For example, the sharing unit can share the content of a phone call on SNS. For example, the sharing unit can convert the content of a phone call into text and share that text on SNS. The sharing unit can also share the content of a phone call as an audio file on SNS. For example, the sharing unit can save the content of a phone call as an audio file and share that audio file on SNS. For example, the sharing unit can use a generation AI to convert the content of a phone call into text. The generation AI converts the content of a phone call into text and shares that text on SNS. As a result, the sharing unit makes it easy to share information by performing SNS sharing.

[0036] The telephone reception system according to this embodiment includes a reporting unit that makes reports to the police. The reporting unit can make reports to the police. For example, the reporting unit can report nuisance calls to the police. For example, the reporting unit can record the content of nuisance calls and send the recording to the police. The reporting unit can also convert the content of nuisance calls into text and send that text to the police. For example, the reporting unit can use a generation AI to convert the content of nuisance calls into text. The generation AI converts the content of nuisance calls into text and sends that text to the police. As a result, the reporting unit strengthens its response to nuisance calls by making reports to the police.

[0037] The analysis unit can analyze the content of a phone call and decide whether to transfer the call or share it on social media. For example, the analysis unit can analyze the content of a call and transfer it to the user if it is important, or share it on social media otherwise. For example, the analysis unit can use generative AI to analyze the content of a phone call. The generative AI analyzes the content of a phone call and transfers it to the user if it is important, or shares it on social media otherwise. This allows the analysis unit to analyze the content of a call and determine the appropriate response, enabling efficient call transfers.

[0038] The response unit can appropriately handle spam calls and share the information on social media. For example, the response unit can appropriately handle spam calls and share the content on social media. For example, the response unit can record the content of spam calls and share the recording on social media. The response unit can also convert the content of spam calls into text and share that text on social media. For example, the response unit can use generative AI to convert the content of spam calls into text. The generative AI converts the content of spam calls into text and shares that text on social media. In this way, the response unit can appropriately handle spam calls and reduce their impact by sharing the information on social media.

[0039] The reception desk can select the optimal reception method when receiving an incoming call by referring to the user's past interaction history. For example, the reception desk may prioritize incoming calls from people the user has frequently interacted with in the past. For example, the reception desk may prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk may predict and suggest who the user should interact with during a specific time period based on the user's past interaction history. In this way, the reception desk can select the optimal reception method by referring to past interaction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the user's past interaction history data into a generating AI and have the generating AI select the optimal reception method.

[0040] The reception unit can filter incoming calls based on the user's current situation and areas of interest. For example, if the user is in a meeting, the reception unit will only accept important calls and notify the user of other calls later. For example, the reception unit will prioritize incoming calls related to the user's specific areas of interest. For example, if the user is driving, the reception unit will prioritize voice input and allow hands-free call acceptance. This allows the reception unit to accept calls more appropriately by filtering based on the user's situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0041] The reception unit can prioritize incoming calls by considering the user's geographical location when receiving incoming calls. For example, if the user is in a specific location, the reception unit will prioritize incoming calls related to that location. For example, if the user is traveling, the reception unit will prioritize incoming calls related to the travel destination. For example, if the user is at home, the reception unit will prioritize incoming calls from family and friends. In this way, the reception unit can prioritize incoming calls by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select the most relevant incoming calls.

[0042] The reception unit can analyze the user's social media activity when receiving an incoming call and accept relevant calls. For example, the reception unit may prioritize incoming calls from people the user frequently interacts with on social media. For example, the reception unit may prioritize incoming calls related to topics the user has shown interest in on social media. For example, the reception unit may prioritize incoming calls from accounts the user follows on social media. In this way, the reception unit can prioritize accepting relevant calls by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit may input the user's social media activity data into a generating AI and have the generating AI select relevant calls.

[0043] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing the content of a phone call. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. For example, the analysis unit can extract specific patterns from past analysis data and optimize the analysis algorithm. For example, the analysis unit can analyze past analysis data to improve analysis accuracy. In this way, the analysis unit can optimize its analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0044] The analysis unit can apply different analysis methods depending on the category of the call when analyzing the content of a call. For example, the analysis unit applies a business analysis method for business calls. For example, the analysis unit applies a private analysis method for private calls. For example, the analysis unit applies a rapid analysis method for emergency calls. This allows the analysis unit to perform more appropriate analysis by applying an analysis method according to the category of the call. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input call category data into a generating AI and have the generating AI execute the application of an appropriate analysis method.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the phone call when analyzing the content of the call. For example, the analysis unit will perform the analysis of an urgent call with the highest priority. For example, the analysis unit will postpone the analysis of an older phone call. For example, the analysis unit will prioritize the analysis of a newer phone call. In this way, the analysis unit can prioritize the analysis of important phone calls by determining the priority of analysis based on the submission date of the phone call. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input phone call submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the calls when analyzing the content of the calls. For example, the analysis unit will prioritize the analysis of important calls. For example, the analysis unit will postpone the analysis of less relevant calls. For example, the analysis unit will prioritize the analysis of highly relevant calls. In this way, the analysis unit can prioritize the analysis of important calls by adjusting the order of analysis based on the relevance of the calls. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the calls into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0047] The call forwarding unit can adjust the level of detail in the call forwarding based on the importance of the call. For example, the call forwarding unit will provide detailed forwarding for important calls. For example, the call forwarding unit will provide concise forwarding for less important calls. For example, the call forwarding unit will provide rapid forwarding for urgent calls. In this way, the call forwarding unit can appropriately forward important calls by adjusting the level of detail in the call forwarding based on the importance of the call. Some or all of the above processing in the call forwarding unit may be performed using AI, for example, or without AI. For example, the call forwarding unit can input call importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the call forwarding.

[0048] The call forwarding unit can apply different call forwarding algorithms depending on the category of the call. For example, the call forwarding unit applies a business call forwarding algorithm for business calls. For example, the call forwarding unit applies a private call forwarding algorithm for private calls. For example, the call forwarding unit applies a rapid call forwarding algorithm for emergency calls. This allows the call forwarding unit to provide more appropriate forwarding by applying a call forwarding algorithm according to the category of the call. Some or all of the above processing in the call forwarding unit may be performed using AI, for example, or without AI. For example, the call forwarding unit can input call category data into a generating AI and have the generating AI apply an appropriate call forwarding algorithm.

[0049] The call forwarding unit can adjust the order of calls based on when the calls were submitted. For example, the call forwarding unit will prioritize urgent calls. For example, the call forwarding unit will postpone calls that were submitted a long time ago. For example, the call forwarding unit will prioritize calls that were submitted a long time ago. In this way, the call forwarding unit can prioritize important calls by adjusting the order of calls based on when they were submitted. Some or all of the above processing in the call forwarding unit may be performed using AI, for example, or not using AI. For example, the call forwarding unit can input call submission data into a generating AI and have the generating AI perform the adjustment of the order of calls.

[0050] The call forwarding unit can adjust the order of calls based on their relevance. For example, it can prioritize important calls, postpone less relevant calls, and prioritize highly relevant calls. This allows the call forwarding unit to prioritize important calls by adjusting the order of calls based on their relevance. Some or all of the above processing in the call forwarding unit may be performed using AI, for example, or without AI. For example, the call forwarding unit can input call relevance data into a generating AI and have the generating AI perform the adjustment of the call forwarding order.

[0051] The response unit can optimize its response algorithm by referring to past response data when dealing with spam calls. For example, the response unit can select the optimal response algorithm based on past response data. For example, the response unit can extract specific patterns from past response data and optimize the response algorithm. For example, the response unit can analyze past response data to improve response accuracy. In this way, the response unit can optimize its response algorithm by referring to past response data. Some or all of the above processes in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input past response data into a generating AI and have the generating AI perform the optimization of the response algorithm.

[0052] The response unit can apply different response methods depending on the category of the call when dealing with unwanted calls. For example, in the case of a sales call, the response unit applies a sales response method. For example, in the case of a fraudulent call, the response unit applies a fraud response method. For example, in the case of a spam call, the response unit applies a spam response method. This allows the response unit to provide a more appropriate response by applying a response method according to the category of the call. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input call category data into a generating AI and have the generating AI execute the application of an appropriate response method.

[0053] The response unit can determine the priority of responses to spam calls based on when the call was submitted. For example, the response unit will prioritize urgent calls. For example, the response unit will postpone responses to older calls. For example, the response unit will prioritize responses to newer calls. In this way, the response unit can prioritize important calls by determining the priority of responses based on when the call was submitted. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input call submission data into a generating AI and have the generating AI perform the determination of response priorities.

[0054] The response unit can adjust the order of responses based on the relevance of the calls when dealing with spam calls. For example, the response unit will prioritize important calls. For example, it will postpone responding to less relevant calls. For example, it will prioritize responding to highly relevant calls. In this way, the response unit can prioritize important calls by adjusting the order of responses based on the relevance of the calls. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input call relevance data into a generating AI and have the generating AI perform the adjustment of the response order.

[0055] The response unit can optimize its response algorithm by referring to past response data during a response. For example, the response unit can select the optimal response algorithm based on past response data. For example, the response unit can extract specific patterns from past response data and optimize the response algorithm. For example, the response unit can analyze past response data to improve response accuracy. In this way, the response unit can optimize its response algorithm by referring to past response data. Some or all of the above processes in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input past response data into a generating AI and have the generating AI perform the optimization of the response algorithm.

[0056] The call handling unit can adjust the order of calls based on when the calls were submitted. For example, the call handling unit will prioritize urgent calls. For example, the call handling unit will postpone calls that are older. For example, the call handling unit will prioritize calls that are newer. In this way, the call handling unit can prioritize important calls by adjusting the order of calls based on when they were submitted. Some or all of the above processing in the call handling unit may be performed using AI, for example, or not using AI. For example, the call handling unit can input call submission data into a generating AI and have the generating AI perform the adjustment of the order of calls.

[0057] The sharing unit can optimize the sharing algorithm by referring to past sharing data when sharing on social media. For example, the sharing unit can select the optimal sharing algorithm based on past sharing data. For example, the sharing unit can extract specific patterns from past sharing data and optimize the sharing algorithm. For example, the sharing unit can analyze past sharing data and improve sharing accuracy. In this way, the sharing unit can optimize the sharing algorithm by referring to past sharing data. Some or all of the above processes in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input past sharing data into a generating AI and have the generating AI perform the optimization of the sharing algorithm.

[0058] The sharing function can adjust the sharing order based on the submission date of phone calls when sharing on social media. For example, the sharing function will prioritize sharing urgent phone calls. For example, it will postpone sharing older phone calls. For example, it will prioritize sharing newer phone calls. In this way, the sharing function can prioritize sharing important phone calls by adjusting the sharing order based on the submission date of the phone calls. Some or all of the above processing in the sharing function may be performed using AI, for example, or not using AI. For example, the sharing function can input phone submission date data into a generating AI and have the generating AI perform the adjustment of the sharing order.

[0059] The reporting unit can optimize its reporting algorithm by referring to past reporting data when a report is made. For example, the reporting unit can select the optimal reporting algorithm based on past reporting data. For example, the reporting unit can extract specific patterns from past reporting data and optimize the reporting algorithm. For example, the reporting unit can analyze past reporting data to improve reporting accuracy. In this way, the reporting unit can optimize its reporting algorithm by referring to past reporting data. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past reporting data into a generating AI and have the generating AI perform the optimization of the reporting algorithm.

[0060] The reporting unit can adjust the order of reports based on when the phone call was submitted. For example, the reporting unit will prioritize reporting urgent calls. For example, the reporting unit will postpone reporting older calls. For example, the reporting unit will prioritize reporting newer calls. In this way, the reporting unit can prioritize reporting important calls by adjusting the order of reports based on when the phone call was submitted. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input phone submission date data into a generating AI and have the generating AI perform the adjustment of the reporting order.

[0061] The reporting unit can adjust the order of reports based on the relevance of the calls when a report is made. For example, the reporting unit will prioritize reporting important calls. For example, the reporting unit will postpone reporting less relevant calls. For example, the reporting unit will prioritize reporting highly relevant calls. In this way, the reporting unit can prioritize reporting important calls by adjusting the order of reports based on the relevance of the calls. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input call relevance data into a generating AI and have the generating AI perform the adjustment of the order of reports.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The reception desk can refer to the user's schedule and adjust how incoming calls are received based on that schedule. For example, if the user is in a meeting, only important calls will be received, and other calls will be notified later. If the user is on vacation, all incoming calls will be automatically shared on social media. If the user is exercising, voice input will be prioritized, allowing for hands-free call reception. This allows the reception desk to provide more appropriate reception by adjusting how calls are received according to the user's schedule.

[0064] The analysis unit can learn from the user's past phone calls and apply the most suitable analysis method to similar calls. For example, it performs a detailed analysis on calls previously deemed important, a concise analysis on calls previously deemed spam, and an analysis suitable for social media sharing on content previously shared on social media. This allows the analysis unit to perform more efficient analysis by referring to past phone calls.

[0065] The call forwarding unit can detect the user's current activity status and adjust the call forwarding method based on that status. For example, if the user is driving, voice input will be prioritized and the call forwarding will be done hands-free. If the user is in a meeting, only important calls will be forwarded, and other calls will be notified later. If the user is relaxed, all calls will be forwarded equally. In this way, the call forwarding unit can provide more appropriate calls forwarding by adjusting the call forwarding method according to the user's activity status.

[0066] The response unit can refer to the user's past response history and select the optimal response method. For example, it can prioritize applying response methods that were effective in the past, and avoid response methods that caused problems in the past. It can also extract specific patterns from past response history and optimize the response algorithm. As a result, the response unit can provide more effective responses by referring to past response history.

[0067] The sharing function can analyze a user's social media activity and suggest relevant sharing methods. For example, it can provide sharing methods tailored to the social media platforms the user frequently uses, prioritize sharing content related to topics the user is interested in, and prioritize sharing information from accounts the user follows. In this way, the sharing function can suggest more appropriate sharing methods by analyzing the user's social media activity.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives incoming calls via telephone and intercom. The reception desk can receive calls from, for example, landlines, mobile phones, and video intercoms. Step 2: The analysis unit analyzes the content of the call based on the incoming call received by the reception unit. The analysis unit analyzes the content of the call using, for example, speech recognition technology or natural language processing technology. Step 3: The call forwarding unit forwards calls based on the analysis performed by the analysis unit. For example, the call forwarding unit can forward important calls to users and share unimportant calls on social media. Step 4: The response department will appropriately address the nuisance call based on the information relayed by the forwarding department. For example, the response department may brush off the nuisance call and share it on social media. In some cases, they may also report it to the police.

[0070] (Example of form 2) The telephone answering system according to an embodiment of the present invention is a system that uses AI to handle and forward calls and inquiries from around the world. This telephone answering system connects all incoming calls to registered landlines, mobile phones, and intercoms to an AI center. The AI ​​answers the call on behalf of the user and, if necessary, forwards it to a smartphone. For example, the AI ​​can pretend to be a family member when answering the call. Next, the AI ​​analyzes the content of the call and decides whether to forward it or share it on social media. For example, if it is an important call, it will be forwarded to the user; otherwise, it will be shared on social media. Also, if the user cannot answer immediately, the AI ​​will respond in a polite manner. Furthermore, if the AI ​​determines that it is a spam call, it will take appropriate action. For example, it will brush off the spam call and share it on social media. In some cases, it can also report it to the police. As a result, the user is freed from troublesome problems related to telephones. Because the AI ​​analyzes the content of the call and takes appropriate action, the user can entrust their calls to the system with peace of mind. Also, because spam calls are handled appropriately, the user's safety is ensured. In this way, the telephone answering system solves troublesome problems related to telephones for the user and allows them to entrust their calls to the system with peace of mind.

[0071] The telephone reception system according to this embodiment comprises a reception unit, an analysis unit, a call forwarding unit, and a response unit. The reception unit receives incoming calls from telephones and intercoms. The reception unit can receive incoming calls from, for example, landlines, mobile phones, and video intercoms. The analysis unit analyzes the content of the call based on the incoming call received by the reception unit. The analysis unit analyzes the content of the call using, for example, speech recognition technology or natural language processing technology. The call forwarding unit forwards the call based on the content analyzed by the analysis unit. The call forwarding unit can, for example, forward important calls to users and share unimportant calls on social media. The response unit responds appropriately to nuisance calls based on the content forwarded by the call forwarding unit. The response unit can, for example, brush off nuisance calls appropriately and share them on social media. In some cases, it can also report to the police. As a result, the telephone reception system can efficiently receive, analyze, forward, and appropriately respond to nuisance calls from telephones and intercoms.

[0072] The reception unit receives incoming calls from telephones and intercoms. For example, it can receive calls from landlines, mobile phones, and video intercoms. Specifically, the reception unit supports various communication methods and is equipped with hardware and software to receive signals from landline telephones, mobile phone networks, and video intercoms via the internet. For landlines, the reception unit is connected to the telephone line and automatically answers upon detecting an incoming call. For mobile phones, the reception unit is equipped with a SIM card and receives calls via the mobile phone network. For video intercoms, the reception unit receives video and audio signals via an internet connection and can respond in real time. This allows the reception unit to centrally manage and efficiently handle calls from various devices. Furthermore, the reception unit can be configured to use different response methods depending on the type and origin of the call. For example, it can answer important calls immediately and play an automated response message for spam calls or calls from unknown sources. The reception unit also has a function to record call history for later review. This allows the reception unit to efficiently manage telephones and intercoms without the user missing important calls.

[0073] The analysis unit analyzes the content of incoming calls based on those received by the reception unit. The analysis unit uses technologies such as speech recognition and natural language processing to analyze the call content. Specifically, it uses speech recognition to convert incoming audio into text data, and then analyzes that text data using natural language processing. The speech recognition technology uses a highly accurate speech recognition engine to convert the speaker's voice into text in real time. Natural language processing extracts important keywords and phrases from the text data and performs analysis to understand the call content. For example, the analysis unit identifies the caller's intentions and requirements from the call content and extracts important information. The analysis unit can also refer to past call content and caller history data to identify callers and evaluate the importance of calls. This allows the analysis unit to quickly and accurately analyze the content of received calls and provide the information necessary for the next processing step. Furthermore, the analysis unit can utilize AI technology to automatically classify the call content and suggest appropriate responses. For example, it can classify calls into business-related calls, personal calls, spam calls, etc., and provide information for appropriate responses to each. This allows the analysis unit to efficiently analyze the content of phone calls, improving the overall response accuracy and efficiency of the system.

[0074] The call forwarding unit forwards calls based on the analysis performed by the analysis unit. For example, the call forwarding unit can forward important calls to users and share unimportant calls on social media. Specifically, based on the information provided by the analysis unit, the call forwarding unit selects the appropriate forwarding method according to the importance and content of the call. In the case of an important call, the call forwarding unit notifies the user directly so that they can respond immediately. For example, it can send a notification to the user's smartphone or computer to let them know that an important call has come in. The call forwarding unit can also select the optimal forwarding method considering the user's schedule and current situation. For example, if the user is in a meeting, it can send a notification after the meeting has ended. On the other hand, in the case of unimportant or spam calls, the call forwarding unit can share them on social media. For example, by posting the content of a spam call on social media and sharing the information with other users, measures can be taken to combat spam calls. The call forwarding unit also has a function to record the content of calls so that they can be reviewed later. This allows the call forwarding unit to forward calls appropriately according to their content, enabling users to respond efficiently without missing important calls. Furthermore, the intermediary unit can collect user feedback and continuously improve the accuracy and effectiveness of its intermediary methods. This allows the intermediary unit to provide users with the best possible intermediary service and improve the overall reliability and convenience of the system.

[0075] The response department will appropriately address nuisance calls based on the information relayed by the call forwarding department. For example, the response department may brush off nuisance calls appropriately and share the information on social media. In some cases, it may also report the call to the police. Specifically, the response department will analyze the content of the nuisance call and select an appropriate response method. For example, it may play an automated message to the caller urging them to hang up. It can also record the content of the nuisance call and share it on social media to share information with other users and take measures against nuisance calls. Furthermore, the response department can identify the source of the nuisance call and report it to the police if necessary. For example, if nuisance calls occur repeatedly or contain threatening content, the response department can report it to the police and request appropriate action. In this way, the response department can respond to nuisance calls quickly and appropriately, ensuring the safety and security of users. Furthermore, the response department can accumulate data on nuisance calls and use it for future countermeasures. For example, it can analyze patterns of nuisance calls and trends of callers to take preventative measures. This allows the response unit to strengthen its ability to handle nuisance calls and improve the overall reliability and security of the system.

[0076] The telephone reception system according to this embodiment includes a response unit that responds by pretending to be a family member. The response unit can respond by pretending to be a family member. For example, the response unit can respond by imitating the voice and manner of speaking of the user's family members. For example, the response unit can record the voice of the user's family members and use that voice to respond. The response unit can also learn the manner of speaking of the user's family members and respond by imitating that manner. For example, the response unit can use a generative AI to learn the manner of speaking of the user's family members. The generative AI learns the manner of speaking of the user's family members and responds by imitating that manner. As a result, the response unit can respond more naturally by pretending to be a family member.

[0077] The telephone reception system according to this embodiment includes a sharing unit that performs SNS sharing. The sharing unit can perform SNS sharing. For example, the sharing unit can share the content of a phone call on SNS. For example, the sharing unit can convert the content of a phone call into text and share that text on SNS. The sharing unit can also share the content of a phone call as an audio file on SNS. For example, the sharing unit can save the content of a phone call as an audio file and share that audio file on SNS. For example, the sharing unit can use a generation AI to convert the content of a phone call into text. The generation AI converts the content of a phone call into text and shares that text on SNS. As a result, the sharing unit makes it easy to share information by performing SNS sharing.

[0078] The telephone reception system according to this embodiment includes a reporting unit that makes reports to the police. The reporting unit can make reports to the police. For example, the reporting unit can report nuisance calls to the police. For example, the reporting unit can record the content of nuisance calls and send the recording to the police. The reporting unit can also convert the content of nuisance calls into text and send that text to the police. For example, the reporting unit can use a generation AI to convert the content of nuisance calls into text. The generation AI converts the content of nuisance calls into text and sends that text to the police. As a result, the reporting unit strengthens its response to nuisance calls by making reports to the police.

[0079] The analysis unit can analyze the content of a phone call and decide whether to transfer the call or share it on social media. For example, the analysis unit can analyze the content of a call and transfer it to the user if it is important, or share it on social media otherwise. For example, the analysis unit can use generative AI to analyze the content of a phone call. The generative AI analyzes the content of a phone call and transfers it to the user if it is important, or shares it on social media otherwise. This allows the analysis unit to analyze the content of a call and determine the appropriate response, enabling efficient call transfers.

[0080] The response unit can appropriately handle spam calls and share the information on social media. For example, the response unit can appropriately handle spam calls and share the content on social media. For example, the response unit can record the content of spam calls and share the recording on social media. The response unit can also convert the content of spam calls into text and share that text on social media. For example, the response unit can use generative AI to convert the content of spam calls into text. The generative AI converts the content of spam calls into text and shares that text on social media. In this way, the response unit can appropriately handle spam calls and reduce their impact by sharing the information on social media.

[0081] The reception system can estimate the user's emotions and adjust how incoming calls are handled based on those emotions. For example, if the user is stressed, the reception system might provide a simple interface and minimize the steps required to handle the call. If the user is relaxed, for example, the reception system might offer detailed reception options and suggest a customizable reception method. If the user is in a hurry, for example, the reception system might prioritize voice input to ensure the call is handled quickly. This allows the reception system to provide more appropriate service by adjusting how calls are handled according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The reception desk can select the optimal reception method when receiving an incoming call by referring to the user's past interaction history. For example, the reception desk may prioritize incoming calls from people the user has frequently interacted with in the past. For example, the reception desk may prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk may predict and suggest who the user should interact with during a specific time period based on the user's past interaction history. In this way, the reception desk can select the optimal reception method by referring to past interaction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the user's past interaction history data into a generating AI and have the generating AI select the optimal reception method.

[0083] The reception unit can filter incoming calls based on the user's current situation and areas of interest. For example, if the user is in a meeting, the reception unit will only accept important calls and notify the user of other calls later. For example, the reception unit will prioritize incoming calls related to the user's specific areas of interest. For example, if the user is driving, the reception unit will prioritize voice input and allow hands-free call acceptance. This allows the reception unit to accept calls more appropriately by filtering based on the user's situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0084] The reception desk can estimate the user's emotions and determine the priority of incoming calls based on those emotions. For example, if the user is stressed, the reception desk will prioritize only important calls. If the user is relaxed, the reception desk will accept all calls equally. If the user is in a hurry, the reception desk will prioritize urgent calls. In this way, the reception desk can prioritize important calls by determining the priority of calls according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The reception unit can prioritize incoming calls by considering the user's geographical location when receiving incoming calls. For example, if the user is in a specific location, the reception unit will prioritize incoming calls related to that location. For example, if the user is traveling, the reception unit will prioritize incoming calls related to the travel destination. For example, if the user is at home, the reception unit will prioritize incoming calls from family and friends. In this way, the reception unit can prioritize incoming calls by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select the most relevant incoming calls.

[0086] The reception unit can analyze the user's social media activity when receiving an incoming call and accept relevant calls. For example, the reception unit may prioritize incoming calls from people the user frequently interacts with on social media. For example, the reception unit may prioritize incoming calls related to topics the user has shown interest in on social media. For example, the reception unit may prioritize incoming calls from accounts the user follows on social media. In this way, the reception unit can prioritize accepting relevant calls by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit may input the user's social media activity data into a generating AI and have the generating AI select relevant calls.

[0087] The analysis unit can estimate the user's emotions and adjust the method of analyzing the phone call content based on the estimated emotions. For example, if the user is stressed, the analysis unit will perform a concise analysis. For example, if the user is relaxed, the analysis unit will perform a detailed analysis. For example, if the user is in a hurry, the analysis unit will analyze only the important points. In this way, the analysis unit can perform a more appropriate analysis by adjusting the content analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing the content of a phone call. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. For example, the analysis unit can extract specific patterns from past analysis data and optimize the analysis algorithm. For example, the analysis unit can analyze past analysis data to improve analysis accuracy. In this way, the analysis unit can optimize its analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0089] The analysis unit can apply different analysis methods depending on the category of the call when analyzing the content of a call. For example, the analysis unit applies a business analysis method for business calls. For example, the analysis unit applies a private analysis method for private calls. For example, the analysis unit applies a rapid analysis method for emergency calls. This allows the analysis unit to perform more appropriate analysis by applying an analysis method according to the category of the call. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input call category data into a generating AI and have the generating AI execute the application of an appropriate analysis method.

[0090] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit will display concise results. If the user is relaxed, for example, the analysis unit will display detailed results. If the user is in a hurry, for example, the analysis unit will display only the important points. In this way, the analysis unit can provide more appropriate information by adjusting how the analysis results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The analysis unit can determine the priority of analysis based on the submission date of the phone call when analyzing the content of the call. For example, the analysis unit will perform the analysis of an urgent call with the highest priority. For example, the analysis unit will postpone the analysis of an older phone call. For example, the analysis unit will prioritize the analysis of a newer phone call. In this way, the analysis unit can prioritize the analysis of important phone calls by determining the priority of analysis based on the submission date of the phone call. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input phone call submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the calls when analyzing the content of the calls. For example, the analysis unit will prioritize the analysis of important calls. For example, the analysis unit will postpone the analysis of less relevant calls. For example, the analysis unit will prioritize the analysis of highly relevant calls. In this way, the analysis unit can prioritize the analysis of important calls by adjusting the order of analysis based on the relevance of the calls. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the calls into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0093] The intermediary unit can estimate the user's emotions and adjust the intermediary method based on the estimated emotions. For example, if the user is stressed, the intermediary unit provides a concise intermediary method. For example, if the user is relaxed, the intermediary unit provides a detailed intermediary method. For example, if the user is in a hurry, the intermediary unit provides a quick intermediary method. This allows the intermediary unit to provide more appropriate intermediary services by adjusting the intermediary method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The call forwarding unit can adjust the level of detail in the call forwarding based on the importance of the call. For example, the call forwarding unit will provide detailed forwarding for important calls. For example, the call forwarding unit will provide concise forwarding for less important calls. For example, the call forwarding unit will provide rapid forwarding for urgent calls. In this way, the call forwarding unit can appropriately forward important calls by adjusting the level of detail in the call forwarding based on the importance of the call. Some or all of the above processing in the call forwarding unit may be performed using AI, for example, or without AI. For example, the call forwarding unit can input call importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the call forwarding.

[0095] The call forwarding unit can apply different call forwarding algorithms depending on the category of the call. For example, the call forwarding unit applies a business call forwarding algorithm for business calls. For example, the call forwarding unit applies a private call forwarding algorithm for private calls. For example, the call forwarding unit applies a rapid call forwarding algorithm for emergency calls. This allows the call forwarding unit to provide more appropriate forwarding by applying a call forwarding algorithm according to the category of the call. Some or all of the above processing in the call forwarding unit may be performed using AI, for example, or without AI. For example, the call forwarding unit can input call category data into a generating AI and have the generating AI apply an appropriate call forwarding algorithm.

[0096] The call forwarding unit can estimate the user's emotions and determine the priority of calls based on the estimated emotions. For example, if the user is stressed, the call forwarding unit will prioritize only important calls. If the user is relaxed, the call forwarding unit will distribute all calls equally. If the user is in a hurry, the call forwarding unit will prioritize urgent calls. In this way, the call forwarding unit can prioritize important calls by determining the priority of calls according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The call forwarding unit can adjust the order of calls based on when the calls were submitted. For example, the call forwarding unit will prioritize urgent calls. For example, the call forwarding unit will postpone calls that were submitted a long time ago. For example, the call forwarding unit will prioritize calls that were submitted a long time ago. In this way, the call forwarding unit can prioritize important calls by adjusting the order of calls based on when they were submitted. Some or all of the above processing in the call forwarding unit may be performed using AI, for example, or not using AI. For example, the call forwarding unit can input call submission data into a generating AI and have the generating AI perform the adjustment of the order of calls.

[0098] The call forwarding unit can adjust the order of calls based on their relevance. For example, it can prioritize important calls, postpone less relevant calls, and prioritize highly relevant calls. This allows the call forwarding unit to prioritize important calls by adjusting the order of calls based on their relevance. Some or all of the above processing in the call forwarding unit may be performed using AI, for example, or without AI. For example, the call forwarding unit can input call relevance data into a generating AI and have the generating AI perform the adjustment of the call forwarding order.

[0099] The response unit can estimate the user's emotions and adjust its response to spam calls based on those emotions. For example, if the user is stressed, the response unit provides a concise response. For example, if the user is relaxed, the response unit provides a detailed response. For example, if the user is in a hurry, the response unit provides a quick response. This allows the response unit to provide a more appropriate response by adjusting its response to spam calls according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The response unit can optimize its response algorithm by referring to past response data when dealing with spam calls. For example, the response unit can select the optimal response algorithm based on past response data. For example, the response unit can extract specific patterns from past response data and optimize the response algorithm. For example, the response unit can analyze past response data to improve response accuracy. In this way, the response unit can optimize its response algorithm by referring to past response data. Some or all of the above processes in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input past response data into a generating AI and have the generating AI perform the optimization of the response algorithm.

[0101] The response unit can apply different response methods depending on the category of the call when dealing with unwanted calls. For example, in the case of a sales call, the response unit applies a sales response method. For example, in the case of a fraudulent call, the response unit applies a fraud response method. For example, in the case of a spam call, the response unit applies a spam response method. This allows the response unit to provide a more appropriate response by applying a response method according to the category of the call. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input call category data into a generating AI and have the generating AI execute the application of an appropriate response method.

[0102] The response unit can estimate the user's emotions and adjust how the response results are displayed based on the estimated emotions. For example, if the user is stressed, the response unit will display a concise response. For example, if the user is relaxed, the response unit will display a detailed response. For example, if the user is in a hurry, the response unit will display only the important points. In this way, the response unit can provide more appropriate displays by adjusting how the response results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The response unit can determine the priority of responses to spam calls based on when the call was submitted. For example, the response unit will prioritize urgent calls. For example, the response unit will postpone responses to older calls. For example, the response unit will prioritize responses to newer calls. In this way, the response unit can prioritize important calls by determining the priority of responses based on when the call was submitted. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input call submission data into a generating AI and have the generating AI perform the determination of response priorities.

[0104] The response unit can adjust the order of responses based on the relevance of the calls when dealing with spam calls. For example, the response unit will prioritize important calls. For example, it will postpone responding to less relevant calls. For example, it will prioritize responding to highly relevant calls. In this way, the response unit can prioritize important calls by adjusting the order of responses based on the relevance of the calls. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input call relevance data into a generating AI and have the generating AI perform the adjustment of the response order.

[0105] The response unit can estimate the user's emotions and adjust its response method, pretending to be a family member, based on the estimated emotions. For example, if the user is stressed, the response unit will provide a concise response. For example, if the user is relaxed, the response unit will provide a detailed response. For example, if the user is in a hurry, the response unit will provide a quick response. This allows the response unit to provide a more appropriate response by adjusting its response method, pretending to be a family member, according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The response unit can optimize its response algorithm by referring to past response data during a response. For example, the response unit can select the optimal response algorithm based on past response data. For example, the response unit can extract specific patterns from past response data and optimize the response algorithm. For example, the response unit can analyze past response data to improve response accuracy. In this way, the response unit can optimize its response algorithm by referring to past response data. Some or all of the above processes in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input past response data into a generating AI and have the generating AI perform the optimization of the response algorithm.

[0107] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user is stressed, the response unit will prioritize only important responses. If the user is relaxed, the response unit will handle all responses equally. If the user is in a hurry, the response unit will prioritize urgent responses. In this way, the response unit can prioritize important responses by determining the priority of responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The call handling unit can adjust the order of calls based on when the calls were submitted. For example, the call handling unit will prioritize urgent calls. For example, the call handling unit will postpone calls that are older. For example, the call handling unit will prioritize calls that are newer. In this way, the call handling unit can prioritize important calls by adjusting the order of calls based on when they were submitted. Some or all of the above processing in the call handling unit may be performed using AI, for example, or not using AI. For example, the call handling unit can input call submission data into a generating AI and have the generating AI perform the adjustment of the order of calls.

[0109] The sharing function can estimate the user's emotions and adjust the method of SNS sharing based on the estimated emotions. For example, if the user is stressed, the sharing function provides a concise method of SNS sharing. For example, if the user is relaxed, the sharing function provides a detailed method of SNS sharing. For example, if the user is in a hurry, the sharing function provides a quick method of SNS sharing. In this way, the sharing function enables more appropriate sharing by adjusting the method of SNS sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The sharing unit can optimize the sharing algorithm by referring to past sharing data when sharing on social media. For example, the sharing unit can select the optimal sharing algorithm based on past sharing data. For example, the sharing unit can extract specific patterns from past sharing data and optimize the sharing algorithm. For example, the sharing unit can analyze past sharing data and improve sharing accuracy. In this way, the sharing unit can optimize the sharing algorithm by referring to past sharing data. Some or all of the above processes in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input past sharing data into a generating AI and have the generating AI perform the optimization of the sharing algorithm.

[0111] The sharing function can estimate the user's emotions and determine the priority of sharing based on those emotions. For example, if the user is stressed, the sharing function will prioritize only important sharing. If the user is relaxed, the sharing function will distribute all information equally. If the user is in a hurry, the sharing function will prioritize urgent sharing. In this way, the sharing function can prioritize important sharing by determining the priority of sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The sharing function can adjust the sharing order based on the submission date of phone calls when sharing on social media. For example, the sharing function will prioritize sharing urgent phone calls. For example, it will postpone sharing older phone calls. For example, it will prioritize sharing newer phone calls. In this way, the sharing function can prioritize sharing important phone calls by adjusting the sharing order based on the submission date of the phone calls. Some or all of the above processing in the sharing function may be performed using AI, for example, or not using AI. For example, the sharing function can input phone submission date data into a generating AI and have the generating AI perform the adjustment of the sharing order.

[0113] The reporting system can estimate the user's emotions and adjust the method of reporting to the police based on the estimated emotions. For example, if the user is stressed, the reporting system will provide a concise method of reporting. For example, if the user is relaxed, the reporting system will provide a detailed method of reporting. For example, if the user is in a hurry, the reporting system will provide a quick method of reporting. This allows the reporting system to make more appropriate reports by adjusting the method of reporting to the police according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The reporting unit can optimize its reporting algorithm by referring to past reporting data when a report is made. For example, the reporting unit can select the optimal reporting algorithm based on past reporting data. For example, the reporting unit can extract specific patterns from past reporting data and optimize the reporting algorithm. For example, the reporting unit can analyze past reporting data to improve reporting accuracy. In this way, the reporting unit can optimize its reporting algorithm by referring to past reporting data. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past reporting data into a generating AI and have the generating AI perform the optimization of the reporting algorithm.

[0115] The reporting system can estimate the user's emotions and prioritize reports based on those emotions. For example, if the user is stressed, the reporting system will prioritize only important reports. If the user is relaxed, the reporting system will distribute all reports equally. If the user is in a hurry, the reporting system will prioritize urgent reports. In this way, the reporting system can prioritize important reports by determining the priority of reports according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The reporting unit can adjust the order of reports based on when the phone call was submitted. For example, the reporting unit will prioritize reporting urgent calls. For example, the reporting unit will postpone reporting older calls. For example, the reporting unit will prioritize reporting newer calls. In this way, the reporting unit can prioritize reporting important calls by adjusting the order of reports based on when the phone call was submitted. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input phone submission date data into a generating AI and have the generating AI perform the adjustment of the reporting order.

[0117] The reporting unit can adjust the order of reports based on the relevance of the calls when a report is made. For example, the reporting unit will prioritize reporting important calls. For example, the reporting unit will postpone reporting less relevant calls. For example, the reporting unit will prioritize reporting highly relevant calls. In this way, the reporting unit can prioritize reporting important calls by adjusting the order of reports based on the relevance of the calls. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input call relevance data into a generating AI and have the generating AI perform the adjustment of the order of reports.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The reception desk can refer to the user's schedule and adjust how incoming calls are received based on that schedule. For example, if the user is in a meeting, only important calls will be received, and other calls will be notified later. If the user is on vacation, all incoming calls will be automatically shared on social media. If the user is exercising, voice input will be prioritized, allowing for hands-free call reception. This allows the reception desk to provide more appropriate reception by adjusting how calls are received according to the user's schedule.

[0120] The analysis unit can learn from the user's past phone calls and apply the most suitable analysis method to similar calls. For example, it performs a detailed analysis on calls previously deemed important, a concise analysis on calls previously deemed spam, and an analysis suitable for social media sharing on content previously shared on social media. This allows the analysis unit to perform more efficient analysis by referring to past phone calls.

[0121] The call forwarding unit can detect the user's current activity status and adjust the call forwarding method based on that status. For example, if the user is driving, voice input will be prioritized and the call forwarding will be done hands-free. If the user is in a meeting, only important calls will be forwarded, and other calls will be notified later. If the user is relaxed, all calls will be forwarded equally. In this way, the call forwarding unit can provide more appropriate calls forwarding by adjusting the call forwarding method according to the user's activity status.

[0122] The response unit can refer to the user's past response history and select the optimal response method. For example, it can prioritize applying response methods that were effective in the past, and avoid response methods that caused problems in the past. It can also extract specific patterns from past response history and optimize the response algorithm. As a result, the response unit can provide more effective responses by referring to past response history.

[0123] The sharing function can analyze a user's social media activity and suggest relevant sharing methods. For example, it can provide sharing methods tailored to the social media platforms the user frequently uses, prioritize sharing content related to topics the user is interested in, and prioritize sharing information from accounts the user follows. In this way, the sharing function can suggest more appropriate sharing methods by analyzing the user's social media activity.

[0124] The reception desk can estimate the user's emotions and adjust how calls are answered based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the steps required to answer the call. If the user is relaxed, it can offer detailed answering options and suggest a customizable answering method. If the user is in a hurry, it can prioritize voice input to allow for quick answering. This allows the reception desk to provide more appropriate answers by adjusting how calls are answered according to the user's emotions.

[0125] The analysis unit can estimate the user's emotions and adjust the phone call content analysis method based on the estimated emotions. For example, if the user is stressed, it performs a concise content analysis. If the user is relaxed, it performs a detailed content analysis. If the user is in a hurry, it analyzes only the important points. In this way, the analysis unit can perform a more appropriate analysis by adjusting the content analysis method according to the user's emotions.

[0126] The intermediary unit can estimate the user's emotions and adjust the intermediary method based on the estimated emotions. For example, if the user is stressed, it provides a concise intermediary method. If the user is relaxed, it provides a detailed intermediary method. If the user is in a hurry, it provides a quick intermediary method. In this way, the intermediary unit can provide more appropriate intermediary services by adjusting the intermediary method according to the user's emotions.

[0127] The response unit can estimate the user's emotions and adjust its response to nuisance calls based on those emotions. For example, if the user is stressed, it provides a concise response. If the user is relaxed, it provides a detailed response. If the user is in a hurry, it provides a quick response. This allows the response unit to provide a more appropriate response by adjusting its response to nuisance calls according to the user's emotions.

[0128] The sharing function can estimate the user's emotions and adjust the SNS sharing method based on those emotions. For example, if the user is stressed, it provides a concise SNS sharing method. If the user is relaxed, it provides a detailed SNS sharing method. If the user is in a hurry, it provides a quick SNS sharing method. In this way, the sharing function can enable more appropriate sharing by adjusting the SNS sharing method according to the user's emotions.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The reception desk receives incoming calls via telephone and intercom. The reception desk can receive calls from, for example, landlines, mobile phones, and video intercoms. Step 2: The analysis unit analyzes the content of the call based on the incoming call received by the reception unit. The analysis unit analyzes the content of the call using, for example, speech recognition technology or natural language processing technology. Step 3: The call forwarding unit forwards calls based on the analysis performed by the analysis unit. For example, the call forwarding unit can forward important calls to users and share unimportant calls on social media. Step 4: The response department will appropriately address the nuisance call based on the information relayed by the forwarding department. For example, the response department may brush off the nuisance call and share it on social media. In some cases, they may also report it to the police.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0133] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, call forwarding unit, response unit, customer service unit, sharing unit, and notification unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives incoming calls from landlines, mobile phones, video intercoms, etc. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the content of the call using speech recognition technology and natural language processing technology. The call forwarding unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and forwards important calls to the user and shares unimportant calls on social media. The response unit is implemented by, for example, the control unit 46A of the smart device 14 and handles spam calls appropriately and shares them on social media. The customer service unit is implemented by, for example, the control unit 46A of the smart device 14 and responds by mimicking the voices and speaking styles of the user's family members. The sharing function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which converts the content of a phone call into text and shares that text on social media. The reporting function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which records the content of a nuisance call and sends the recording to the police. The correspondence between each function and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, call forwarding unit, response unit, customer service unit, sharing unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives incoming calls from landlines, mobile phones, video intercoms, etc. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the call using speech recognition technology and natural language processing technology. The call forwarding unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and forwards important calls to the user and shares unimportant calls on social media. The response unit is implemented, for example, by the control unit 46A of the smart glasses 214 and handles spam calls appropriately and shares them on social media. The customer service unit is implemented, for example, by the control unit 46A of the smart glasses 214 and responds by mimicking the voices and speaking styles of the user's family members. The sharing function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which converts the content of a phone call into text and shares that text on social media. The reporting function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which records the content of a nuisance call and sends the recording to the police. The correspondence between each function and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, call forwarding unit, response unit, customer service unit, sharing unit, and notification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives incoming calls from landlines, mobile phones, video intercoms, etc. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the content of the call using speech recognition technology and natural language processing technology. The call forwarding unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and forwards important calls to the user and shares unimportant calls on SNS. The response unit is implemented by, for example, the control unit 46A of the headset terminal 314 and handles spam calls appropriately and shares them on SNS. The customer service unit is implemented by, for example, the control unit 46A of the headset terminal 314 and responds by mimicking the voices and speaking styles of the user's family members. The sharing function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which converts the content of a phone call into text and shares that text on social media. The reporting function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which records the content of a nuisance call and sends the recording to the police. The correspondence between each function and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0175] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0176] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0177] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0178] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0179] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0181] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0183] Each of the multiple elements described above, including the reception unit, analysis unit, call forwarding unit, response unit, customer service unit, sharing unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives incoming calls from landlines, mobile phones, video intercoms, etc. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the content of the call using speech recognition technology and natural language processing technology. The call forwarding unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and forwards important calls to the user and shares unimportant calls on social media. The response unit is implemented by, for example, the control unit 46A of the robot 414 and handles spam calls appropriately and shares them on social media. The customer service unit is implemented by, for example, the control unit 46A of the robot 414 and responds by mimicking the voices and speaking styles of the user's family members. The sharing function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which converts the content of a phone call into text and shares that text on social media. The reporting function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which records the content of a nuisance call and sends the recording to the police. The correspondence between each function and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0184] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0186] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0187] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0188] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0192] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0193] 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.

[0194] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0195] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0196] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0197] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0199] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0200] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0201] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0202] (Note 1) The reception area handles incoming calls via telephone and intercom, An analysis unit analyzes the content of a phone call based on an incoming call received by the reception unit, A forwarding unit that forwards information based on the content analyzed by the aforementioned analysis unit, A response unit that appropriately handles nuisance calls based on the content forwarded by the aforementioned forwarding unit, Equipped with A system characterized by the following features. (Note 2) Customer service staff will respond by pretending to be family members. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a sharing section for SNS sharing. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a reporting department that makes calls to the police. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The system analyzes the content of the call to determine whether to transfer the call or simply share it on social media. The system described in Appendix 1, characterized by the features described herein. (Note 6) The corresponding part is, I brush off nuisance calls and share them on social media. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how incoming calls are handled based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving an incoming call, the system selects the most appropriate method of handling the call by referring to the user's past interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving an incoming call, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of incoming calls to be received based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving incoming calls, the system prioritizes receiving calls that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving an incoming call, the system analyzes the user's social media activity and accepts relevant calls. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the phone call content analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing the content of a phone call, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing the content of a phone call, different analysis methods are applied depending on the category of the call. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing the content of phone calls, the priority of the analysis is determined based on when the calls were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing the content of phone calls, the order of analysis is adjusted based on the relevance of the calls. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned intermediary unit, It estimates the user's emotions and adjusts the method of communication based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned intermediary unit, When transferring a call, adjust the level of detail based on the importance of the call. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned intermediary unit, When transferring a call, a different transfer algorithm is applied depending on the category of the call. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned intermediary unit, It estimates the user's emotions and determines the priority of call forwarding based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned intermediary unit, When forwarding a call, the order of forwarding will be adjusted based on when the phone call was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned intermediary unit, When transferring calls, the order of transfers is adjusted based on the relevance of the calls. The system described in Appendix 1, characterized by the features described herein. (Note 25) The corresponding part is, The system estimates the user's emotions and adjusts how it handles spam calls based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The corresponding part is, When dealing with spam calls, the response algorithm is optimized by referring to past response data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The corresponding part is, When dealing with spam calls, apply different response methods depending on the category of the call. The system described in Appendix 1, characterized by the features described herein. (Note 28) The corresponding part is, The system estimates the user's emotions and adjusts how the response is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The corresponding part is, When dealing with nuisance calls, prioritize responses based on when the call was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The corresponding part is, When dealing with nuisance calls, adjust the order of responses based on the relevance of the calls. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned customer service unit, It estimates the user's emotions and adjusts its responses based on those emotions, pretending to be a family member. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned customer service unit, During customer interaction, the response algorithm is optimized by referring to past interaction data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned customer service unit, It estimates the user's emotions and determines the priority of responses based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned customer service unit, When responding to inquiries, the order of responses will be adjusted based on when the phone call was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned shared portion is, It estimates user sentiment and adjusts how social media sharing is done based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned shared portion is, When sharing on social media, the sharing algorithm is optimized by referring to past sharing data. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned shared portion is, It estimates the user's emotions and determines sharing priorities based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned shared portion is, When sharing on social media, the order of sharing will be adjusted based on when the phone call was submitted. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned reporting unit, The system estimates the user's emotions and adjusts how the call is made to the police based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned reporting unit, When a report is submitted, the reporting algorithm is optimized by referring to past reporting data. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned reporting unit, The system estimates the user's emotions and prioritizes reports based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned reporting unit, When reporting, the order of reports will be adjusted based on when the phone call was submitted. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned reporting unit, When a call is made, the order of calls will be adjusted based on the relevance of the call. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area handles incoming calls via telephone and intercom, An analysis unit analyzes the content of a phone call based on an incoming call received by the reception unit, A forwarding unit that forwards information based on the content analyzed by the aforementioned analysis unit, A response unit that appropriately handles nuisance calls based on the content forwarded by the aforementioned forwarding unit, Equipped with A system characterized by the following features.

2. Customer service staff will respond by pretending to be family members. The system according to feature 1.

3. It is equipped with a sharing section for SNS sharing. The system according to feature 1.

4. It has a reporting department that makes calls to the police. The system according to feature 1.

5. The aforementioned analysis unit, The system analyzes the content of the call to determine whether to transfer the call or simply share it on social media. The system according to feature 1.

6. The corresponding part is, I brush off nuisance calls and share them on social media. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how incoming calls are handled based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving an incoming call, the system selects the most appropriate method of handling the call by referring to the user's past interaction history. The system according to feature 1.

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