System

Abstract

An object of a system according to an embodiment is to efficiently handle an unnecessary phone call to a phone at home.SOLUTION: A system includes a reception unit, an analysis unit, a notification unit, and an association unit. The reception unit receives a telephone call. The analysis unit analyzes the content of the call received by the reception unit. The notification unit notifies the user based on the content analyzed by the analysis unit. The dealing unit automatically deals with the call determined to be unnecessary by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, responding to unwanted sales calls on home phones is cumbersome, and there is a demand for efficient responses.

[0005] The system according to the embodiment aims to efficiently handle unwanted calls coming to a home phone. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a notification unit, and a response unit. The reception unit receives a call. The analysis unit analyzes the content of the call received by the reception unit. The notification unit notifies the user based on the content analyzed by the analysis unit. The response unit automatically responds to calls determined to be unnecessary by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently handle unwanted calls to a home phone. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A telephone reception operator system according to an embodiment of the present invention uses a generation AI to accept and analyze calls to a home phone, determine whether the call is necessary, and notify the user. In the telephone reception operator system, the generation AI analyzes the content of the call and distinguishes between necessary and unnecessary calls. For example, a call from a relative or other person is determined to be necessary, while a sales call is determined to be unnecessary. If the call is determined to be necessary, the generation AI notifies the user, and if the call is determined to be unnecessary, the generation AI automatically handles the call. For example, the generation AI automatically sends a rejection message to sales calls. This mechanism allows users to receive only necessary calls without being bothered by unnecessary sales calls. This allows the telephone reception operator system to receive only necessary calls without being bothered by unnecessary sales calls. For example, users can receive important messages from relatives or other people without missing them, and they will not be bothered by sales calls.

[0029] A telephone reception operator system according to an embodiment includes a reception unit, an analysis unit, a notification unit, and a response unit. The reception unit receives telephone calls. For example, the reception unit can receive calls to a home telephone. The reception unit can also acquire caller information. The analysis unit analyzes the content of the calls received by the reception unit. For example, the analysis unit converts the content of the calls into text using voice recognition technology and analyzes the content. The analysis unit can analyze the content of the calls using a generation AI and determine whether the calls are necessary. The notification unit notifies the user based on the content analyzed by the analysis unit. For example, the notification unit can send a notification to the user's smartphone. The notification unit can send an appropriate notification to the user using a generation AI. The response unit automatically handles calls determined to be unnecessary by the analysis unit. For example, the response unit can automatically send a rejection message to sales calls. The response unit can use a generation AI to provide an appropriate response to unnecessary calls. As a result, the telephone reception operator system according to an embodiment allows users to receive only necessary calls without being bothered by unnecessary sales calls.

[0030] The reception unit can analyze the call source and determine that a call from a relative or the like is an important call. The reception unit can, for example, analyze a telephone number and identify the call source. For example, the reception unit can compare the telephone number with a database and determine that a call from a relative or the like is an important call. The reception unit can also analyze geographical information of the call source and determine that a call from a relative or the like is an important call. For example, the reception unit can determine that a call from a relative or the like is an important call based on the geographical information of the call source. This allows calls from relatives or the like to be received with priority.

[0031] The analysis unit can convert the contents of the phone call into text and analyze the contents. The analysis unit can convert the contents of the phone call into text, for example, using voice recognition technology. For example, the analysis unit can convert the contents of the phone call into text in real time using voice recognition technology. The analysis unit can also analyze the converted text and determine whether the call is necessary. For example, the analysis unit can analyze the contents of the phone call using text analysis technology and determine whether the call is necessary. This allows the contents of the phone call to be analyzed accurately.

[0032] The notification unit can send notifications to the user's smartphone. The notification unit can send notifications to the user's smartphone using, for example, push notifications. For example, the notification unit can use generation AI to send appropriate notifications to the user's smartphone. The notification unit can also send notifications to the user's smartphone using SMS. For example, the notification unit can use SMS to notify the user that an important call has come in. The notification unit can also send notifications to the user's smartphone using app notifications. For example, the notification unit can use a dedicated app to send notifications to the user. This allows the user to check important calls on their smartphone.

[0033] The response unit can automatically send a rejection message to sales calls. The response unit, for example, automatically sends a rejection message to sales calls. For example, the response unit uses a generation AI to send an appropriate rejection message to sales calls. The response unit can also use a message template to send a rejection message to sales calls. For example, the response unit uses a message template prepared in advance to send a rejection message to sales calls. The response unit can also adjust the timing of sending the rejection message to sales calls. For example, the response unit sends a rejection message immediately after the call comes in. This prevents users from being bothered by sales calls.

[0034] The reception unit can analyze the caller's past call history and change the reception method depending on the importance. The reception unit, for example, analyzes call history and determines the importance of the caller. For example, the reception unit prioritizes callers who have made important calls in the past. The reception unit can also analyze the frequency of callers and determine the importance. For example, the reception unit prioritizes callers who contact frequently. The reception unit can also analyze the content of calls from new numbers and determine the importance. For example, the reception unit analyzes calls from new numbers and determines whether the call is important. This enables appropriate responses based on the caller's past call history. For example, if the caller has made important calls in the past, the generation AI prioritizes receiving the call and notifies the user. If the caller has made sales calls in the past, the generation AI automatically sends a message declining the call. If the caller is a new number, the generation AI analyzes the content of the call, determines the importance, and then notifies the user.

[0035] When accepting a call, the reception unit can adjust the reception method taking into account the geographical information of the caller. The reception unit acquires the geographical information of the caller using, for example, GPS data. For example, the reception unit adjusts the reception method of the call based on the geographical information of the caller. The reception unit can also acquire the geographical information of the caller using an IP address. For example, the reception unit analyzes the IP address to identify the geographical information of the caller. The reception unit can also adjust the reception method of the call based on the geographical information of the caller. For example, the reception unit prioritizes reception of calls from the area where the user lives. If the caller is from overseas, the reception unit automatically sends a rejection message. If the caller is from a specific area, the reception method is adjusted based on the characteristics of that area. This makes it possible to respond appropriately based on the geographical information of the caller.

[0036] When accepting a call, the reception unit can analyze the caller's voice tone and language and select an appropriate response. The reception unit, for example, uses a voice analysis algorithm to analyze the caller's voice tone. For example, if the caller's voice tone indicates an emergency, the reception unit prioritizes accepting the call. The reception unit can also analyze the caller's language using a language model. For example, if the caller's voice tone indicates an emergency, the reception unit automatically sends a decline message. The reception unit can also select an appropriate response based on the caller's voice tone and language. For example, if the caller's voice tone indicates a sales characteristic, the reception unit automatically sends a decline message. This enables an appropriate response based on the caller's voice tone and language. For example, if the caller's voice tone indicates an emergency, the generation AI prioritizes accepting the call and notifies the user. If the caller's language is not the user's native language, the generation AI automatically sends a decline message. If the caller's voice tone indicates a sales characteristic, the generation AI automatically sends a decline message.

[0037] When accepting a call, the reception unit can analyze the caller's social media activity and prioritize accepting calls that are highly relevant. The reception unit, for example, analyzes the content of social media posts to determine the relevance of the caller. For example, the reception unit prioritizes accepting calls if the caller is included in the user's friend list. The reception unit can also prioritize accepting calls if the caller is someone the user frequently interacts with on social media. For example, the reception unit prioritizes accepting calls if the caller has common interests with the user on social media. This enables appropriate responses based on the caller's social media activity. For example, if the caller is included in the user's friend list, the generation AI prioritizes accepting the call and notifies the user. If the caller is someone the user frequently interacts with on social media, the generation AI prioritizes accepting the call and notifies the user. If the caller has common interests with the user on social media, the generation AI prioritizes accepting the call and notifies the user.

[0038] When accepting a call, the reception unit can refer to the user's calendar information and select the optimal timing to accept the call. The reception unit refers to calendar information such as Google (registered trademark) Calendar or Outlook (registered trademark) Calendar. For example, if the user is in a meeting, the reception unit automatically accepts the call and notifies the user later. Furthermore, if the user is on vacation, the reception unit can prioritize only important calls and notify the user later for other calls. For example, the reception unit selects the optimal timing to accept the call from the user's calendar information and accepts the call. In this way, an appropriate acceptance timing is selected based on the user's calendar information. For example, if the user is in a meeting, the generation AI automatically accepts the call and notifies the user later. If the user is on vacation, the generation AI prioritizes only important calls and notifies the user later for other calls. The reception unit selects the optimal timing to accept the call from the user's calendar information and accepts the call.

[0039] When accepting a call, the reception unit can customize the acceptance method by reflecting the user's past feedback. The reception unit customizes the acceptance method by, for example, referring to the user's past ratings and comments. For example, if the user has previously disliked sales calls, the reception unit can automatically send a message declining the call. The reception unit can also prioritize accepting calls and notify the user if the user has previously missed an important call. For example, the reception unit customizes the optimal acceptance method based on the user's past feedback. This allows the appropriate acceptance method to be customized based on the user's past feedback. For example, if the user has previously disliked sales calls, the generation AI automatically sends a message declining the call. If the user has previously missed an important call, the generation AI prioritizes accepting calls and notifies the user. The optimal acceptance method is customized based on the user's past feedback.

[0040] When analyzing the contents of a call, the analysis unit can improve the accuracy of the analysis by referring to data on similar calls from the past. For example, the analysis unit improves the accuracy of the analysis by referring to data on important calls received in the past. For example, the analysis unit improves the accuracy of the analysis by referring to data on sales calls received in the past. The analysis unit can also improve the accuracy of the analysis based on data on similar calls from the past. For example, the analysis unit stores data on similar calls from the past in a database and refers to it during analysis. This improves the accuracy of the analysis based on data on similar calls from the past. For example, the analysis unit improves the accuracy of the analysis by referring to data on important calls received in the past. The analysis unit improves the accuracy of the analysis by referring to data on sales calls received in the past. The analysis accuracy is improved based on data on similar calls from the past.

[0041] When analyzing the contents of a phone call, the analysis unit can change the analysis method taking into account the caller's attribute information. The analysis unit changes the analysis method taking into account attribute information such as the caller's age, gender, and occupation. For example, if the caller is a relative, the analysis unit performs a detailed analysis to determine whether the call is important. In addition, if the caller is a sales company, the analysis unit can perform a simplified analysis and determine that the call is unnecessary. For example, the analysis unit changes the analysis method based on the caller's attribute information and performs the optimal analysis. This allows an appropriate analysis method to be selected based on the caller's attribute information. For example, if the caller is a relative, the generation AI performs a detailed analysis to determine whether the call is important. If the caller is a sales company, the generation AI performs a simplified analysis and determines that the call is unnecessary. The analysis method is changed based on the caller's attribute information and performs the optimal analysis.

[0042] When analyzing the contents of a telephone call, the analysis unit can use voice recognition technology to more accurately understand the caller's intention. For example, the analysis unit uses voice recognition technology to convert the contents of the telephone call into text and analyze the intention. For example, the analysis unit uses voice recognition technology to convert the caller's voice into text and analyze the intention. The analysis unit can also analyze the tone of the voice to understand the caller's intention. For example, the analysis unit analyzes the tone of the caller's voice to understand the intention. The analysis unit can also use voice recognition technology to more accurately understand the caller's intention. For example, the analysis unit uses a deep learning model to analyze the caller's intention. As a result, the use of voice recognition technology can more accurately understand the caller's intention. For example, the caller's voice is converted into text and the intention is analyzed. The caller's tone of the voice is analyzed to understand the intention. The use of voice recognition technology can more accurately understand the caller's intention.

[0043] When analyzing the contents of a phone call, the analysis unit can correct the analysis result by taking into account the geographical information of the caller. The analysis unit, for example, uses GPS data to acquire the geographical information of the caller. For example, the analysis unit corrects the analysis result based on the geographical information of the caller. The analysis unit can also acquire the geographical information of the caller using an IP address. For example, the analysis unit analyzes the IP address to identify the geographical information of the caller. The analysis unit can also correct the analysis result based on the geographical information of the caller. For example, the analysis unit corrects the analysis result if the caller is from the area where the user lives. If the caller is from overseas, the analysis unit corrects the analysis result. The analysis result is corrected based on the geographical information of the caller. As a result, an appropriate analysis result is corrected based on the geographical information of the caller. For example, if the caller is from the area where the user lives, the generation AI corrects the analysis result. If the caller is from overseas, the generation AI corrects the analysis result. The analysis result is corrected based on the geographical information of the caller.

[0044] When analyzing the contents of a telephone call, the analysis unit can improve the accuracy of the analysis by referring to related literature and databases. The analysis unit, for example, refers to related literature and improves the accuracy of the analysis. For example, the analysis unit refers to academic papers and patent databases and improves the accuracy of the analysis. The analysis unit can also improve the accuracy of the analysis by referring to related databases. For example, the analysis unit improves the accuracy of the analysis based on related databases. This improves the accuracy of the analysis based on related literature and databases. For example, the analysis unit improves the accuracy of the analysis by referring to related literature. The analysis unit improves the accuracy of the analysis by referring to related databases. The analysis unit improves the accuracy of the analysis based on related information.

[0045] When analyzing the content of a call, the analysis unit can customize the analysis results by taking into account the industry information of the caller. The analysis unit, for example, customizes the analysis results by taking into account the industry information of the caller. For example, if the caller is in the medical industry, the analysis unit customizes the analysis results based on medical-related information. Furthermore, if the caller is in the financial industry, the analysis unit can also customize the analysis results based on financial-related information. For example, the analysis unit customizes the analysis results based on the industry information of the caller. This allows an appropriate analysis result to be customized based on the industry information of the caller. For example, if the caller is in the medical industry, the generation AI customizes the analysis results based on medical-related information. If the caller is in the financial industry, the generation AI customizes the analysis results based on financial-related information. The analysis results are customized based on the industry information of the caller.

[0046] At the time of notification, the notification unit can select the optimal notification method by referring to the user's past notification history. The notification unit, for example, refers to the user's past notification history and selects the optimal notification method. For example, the notification unit selects the optimal notification method based on notification methods that the user has preferred in the past. The notification unit can also customize the optimal notification method based on the user's past notification history. For example, the notification unit refers to the user's past notification history and selects the optimal notification method. In this way, an appropriate notification method is selected based on the user's past notification history. For example, the generation AI selects the optimal notification method based on notification methods that the user has preferred in the past. The user's past notification history is referred to and the optimal notification method is selected. The optimal notification method is customized based on the user's past notification history.

[0047] The notification unit can analyze the user's current activity status at the time of notification and select the optimal notification timing. The notification unit analyzes, for example, the user's location information and activity log to understand the current activity status. For example, the notification unit delays notifications when the user is in a meeting. The notification unit can also send only important notifications when the user is on vacation. For example, the notification unit analyzes the user's current activity status and selects the optimal notification timing. In this way, an appropriate notification timing is selected based on the user's current activity status. For example, when the user is in a meeting, the generation AI delays notifications. When the user is on vacation, the generation AI sends only important notifications. The user's current activity status is analyzed and the optimal notification timing is selected.

[0048] When sending a notification, the notification unit can select the optimal notification means by taking into account the user's device information. The notification unit selects the notification means by taking into account device information such as the user's device type, OS version, and connection status. For example, if the user is using a smartphone, the notification unit sends a push notification. The notification unit can also send an email notification if the user is using a PC. For example, the notification unit selects the optimal notification means based on the user's device information. This allows the appropriate notification means to be selected based on the user's device information. For example, if the user is using a smartphone, the generation AI sends a push notification. If the user is using a PC, the generation AI sends an email notification. The optimal notification means is selected based on the user's device information.

[0049] The notification unit can select the optimal notification method by taking into consideration the user's geographical information when sending a notification. The notification unit acquires the user's geographical information, for example, using GPS data. For example, the notification unit sends a voice notification when the user is at home. The notification unit can also send a vibration notification when the user is out. For example, the notification unit selects the optimal notification method based on the user's geographical information. In this way, an appropriate notification method is selected based on the user's geographical information. For example, when the user is at home, the generation AI sends a voice notification. When the user is out, the generation AI sends a vibration notification. The optimal notification method is selected based on the user's geographical information.

[0050] The notification unit can analyze the user's social media activity at the time of notification and prioritize related notifications. The notification unit can, for example, analyze the content of posts on social media and prioritize related notifications. For example, the notification unit prioritizes sending notifications about places the user has checked in to on social media. The notification unit can also analyze the content of posts on social media by prioritizing sending related notifications. For example, the notification unit can refer to the activities of the user's friends on social media and prioritize sending related notifications. This allows appropriate notifications to be sent based on the user's social media activity. For example, the notification unit can prioritize sending notifications about places the user has checked in to on social media. The notification unit can analyze the content of posts on social media by prioritizing sending related notifications. The notification unit can prioritize sending related notifications based on the activities of the user's friends on social media.

[0051] The notification unit can customize the notification method by reflecting the user's past feedback when notifying. The notification unit, for example, refers to the user's past ratings and comment content to customize the notification method. For example, the notification unit customizes the notification method based on the user's previously preferred notification method. The notification unit can also customize the optimal notification method based on the user's past feedback. For example, the notification unit refers to the user's past feedback to customize the optimal notification method. In this way, an appropriate notification method is customized based on the user's past feedback. For example, the generation AI customizes the notification method based on the user's previously preferred notification method. The optimal notification method is customized by referring to the user's past feedback. The notification method is customized based on the user's past feedback.

[0052] When responding, the response unit can select the optimal response method by referring to past response history. The response unit, for example, refers to the user's past response history and selects the optimal response method. For example, the response unit selects the optimal response method based on response methods that the user has preferred in the past. The response unit can also customize the optimal response method based on the user's past response history. For example, the response unit refers to the user's past response history and selects the optimal response method. In this way, an appropriate response method is selected based on the past response history. For example, the generation AI selects the optimal response method based on response methods that the user has preferred in the past. The optimal response method is selected by referring to the user's past response history. The optimal response method is customized based on the user's past response history.

[0053] When responding, the response unit can change the response method taking into account the caller's attribute information. The response unit changes the response method taking into account attribute information such as the caller's age, gender, and occupation. For example, if the caller is a relative, the response unit responds in detail and notifies the user. The response unit can also automatically send a message declining the call if the caller is a sales company. For example, the response unit changes the response method based on the caller's attribute information and takes the optimal response. In this way, an appropriate response method is selected based on the caller's attribute information. For example, if the caller is a relative, the generation AI responds in detail and notifies the user. If the caller is a sales company, the generation AI automatically sends a message declining the call. The response method is changed and the optimal response is taken based on the caller's attribute information.

[0054] When responding, the response unit can analyze the caller's voice tone and language and select an appropriate response. The response unit, for example, uses a voice analysis algorithm to analyze the caller's voice tone. For example, if the caller's voice tone indicates an emergency, the response unit prioritizes responding and notifies the user. The response unit can also analyze the caller's language using a language model. For example, if the caller's voice tone is not the user's native language, the response unit automatically sends a decline message. The response unit can also select an appropriate response based on the caller's voice tone and language. For example, if the caller's voice tone has sales characteristics, the response unit automatically sends a decline message. In this way, an appropriate response is selected based on the caller's voice tone and language. For example, if the caller's voice tone indicates an emergency, the generation AI prioritizes responding and notifies the user. If the caller's language is not the user's native language, the generation AI automatically sends a decline message. If the caller's voice tone has sales characteristics, the generation AI automatically sends a decline message.

[0055] When responding, the response unit can select the optimal response method by taking into account the geographical information of the caller. The response unit, for example, uses GPS data to acquire the geographical information of the caller. For example, the response unit selects the response method based on the geographical information of the caller. The response unit can also acquire the geographical information of the caller using an IP address. For example, the response unit analyzes the IP address to identify the geographical information of the caller. The response unit can also select the response method based on the geographical information of the caller. For example, if the caller is a call from the user's area, the response unit prioritizes responding and notifies the user. If the caller is a call from overseas, the response unit automatically sends a rejection message. The optimal response method is selected based on the geographical information of the caller. In this way, an appropriate response method is selected based on the geographical information of the caller. For example, if the caller is a call from the user's area, the generation AI prioritizes responding and notifies the user. If the caller is a call from overseas, the generation AI automatically sends a rejection message. The optimal response method is selected based on the geographical information of the caller.

[0056] When responding, the response unit can analyze the social media activity of the sender and prioritize relevant responses. The response unit, for example, analyzes the content of social media posts and prioritizes relevant responses. For example, if the sender is included in the user's friend list, the response unit prioritizes responding and notifying the user. The response unit can also prioritize responding if the sender is someone the user frequently interacts with on social media. For example, the response unit prioritizes responding if the sender has common interests with the user on social media. This allows an appropriate response to be taken based on the sender's social media activity. For example, if the sender is included in the user's friend list, the generation AI prioritizes responding and notifying the user. If the sender is someone the user frequently interacts with on social media, the generation AI prioritizes responding and notifying the user. If the sender has common interests with the user on social media, the generation AI prioritizes responding and notifying the user.

[0057] The response unit can customize the response method by reflecting the user's past feedback when responding. The response unit, for example, refers to the user's past ratings and comment content to customize the response method. For example, the response unit customizes the response method based on the user's preferred response methods in the past. The response unit can also customize the optimal response method based on the user's past feedback. For example, the response unit refers to the user's past feedback to customize the optimal response method. In this way, an appropriate response method is customized based on the user's past feedback. For example, the generation AI customizes the response method based on the user's preferred response methods in the past. The optimal response method is customized by referring to the user's past feedback. The response method is customized based on the user's past feedback.

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

[0059] The reception unit can refer to the user's calendar information and select the optimal timing to accept calls. For example, the reception unit refers to calendar information such as Google Calendar or Outlook Calendar. If the user is in a meeting, the call will be automatically accepted and the call will be notified later. Also, if the user is on vacation, it is possible to prioritize only important calls and notify other calls later. In this way, an appropriate acceptance timing is selected based on the user's calendar information. For example, if the user is in a meeting, the generation AI will automatically accept the call and notify the call later. If the user is on vacation, the generation AI will prioritize only important calls and notify other calls later. The optimal acceptance timing is selected from the user's calendar information and the call will be accepted.

[0060] When analyzing the content of a call, the analysis unit can improve the accuracy of the analysis by referring to data on similar calls from the past. For example, the analysis unit can improve the accuracy of the analysis by referring to data on important calls received in the past. The analysis unit can also improve the accuracy of the analysis based on data on similar calls from the past. For example, the analysis unit stores data on similar calls from the past in a database and refers to it during analysis. This improves the accuracy of the analysis based on data on similar calls from the past. For example, the analysis unit can improve the accuracy of the analysis by referring to data on important calls received in the past. The analysis unit can improve the accuracy of the analysis by referring to data on sales calls received in the past. The analysis unit can improve the accuracy of the analysis based on data on similar calls from the past.

[0061] When analyzing the contents of a phone call, the analysis unit can change the analysis method by taking into account the caller's attribute information. For example, the analysis unit changes the analysis method by taking into account attribute information such as the caller's age, gender, and occupation. If the caller is a relative, the analysis unit performs a detailed analysis to determine whether the call is important. Also, if the caller is a sales company, the analysis unit can perform a simplified analysis and determine that the call is unnecessary. For example, the analysis method can be changed and the optimal analysis can be performed based on the caller's attribute information. This allows the appropriate analysis method to be selected based on the caller's attribute information. For example, if the caller is a relative, the generation AI performs a detailed analysis to determine whether the call is important. If the caller is a sales company, the generation AI performs a simplified analysis and determines that the call is unnecessary. The analysis method can be changed and the optimal analysis can be performed based on the caller's attribute information.

[0062] When accepting a call, the reception unit can analyze the caller's social media activity and prioritize highly relevant calls. For example, the reception unit analyzes the content of social media posts and determines the relevance of the caller. If the caller is included in the user's friend list, the reception unit prioritizes accepting the call. Also, if the caller is someone the user frequently interacts with on social media, the reception unit can prioritize accepting the call. For example, if the caller has common interests with the user on social media, the reception unit prioritizes accepting the call. This enables an appropriate response based on the caller's social media activity. For example, if the caller is included in the user's friend list, the generation AI prioritizes accepting the call and notifies the user. If the caller is someone the user frequently interacts with on social media, the generation AI prioritizes accepting the call and notifies the user. If the caller has common interests with the user on social media, the generation AI prioritizes accepting the call and notifies the user.

[0063] When notifying, the notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification unit refers to the user's past notification history and selects the optimal notification method. The optimal notification method is selected based on the notification methods the user has preferred in the past. The notification unit can also customize the optimal notification method based on the user's past notification history. For example, the notification unit refers to the user's past notification history and selects the optimal notification method. This allows an appropriate notification method to be selected based on the user's past notification history. For example, the generation AI selects the optimal notification method based on the notification methods the user has preferred in the past. The notification unit refers to the user's past notification history and selects the optimal notification method. The optimal notification method is customized based on the user's past notification history.

[0064] When responding, the response unit can analyze the caller's voice tone and language and select an appropriate response. For example, the response unit uses a voice analysis algorithm to analyze the caller's voice tone. If the caller's voice tone indicates an emergency, the response unit prioritizes responding and notifies the user. The response unit can also analyze the caller's language using a language model. For example, if the caller's language is not the user's native language, the response unit automatically sends a decline message. The response unit can also select an appropriate response based on the caller's voice tone and language. For example, if the caller's voice tone has sales characteristics, the response unit automatically sends a decline message. This allows the appropriate response to be selected based on the caller's voice tone and language. For example, if the caller's voice tone indicates an emergency, the generation AI prioritizes responding and notifies the user. If the caller's language is not the user's native language, the generation AI automatically sends a decline message. If the caller's voice tone has sales characteristics, the generation AI automatically sends a decline message.

[0065] When analyzing the contents of a telephone call, the analysis unit can improve the accuracy of the analysis by referring to related literature and databases. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature. By referring to academic papers and patent databases, the analysis accuracy can be improved. The analysis unit can also improve the accuracy of the analysis by referring to related databases. For example, the analysis accuracy can be improved based on related databases. This improves the accuracy of the analysis based on related literature and databases. For example, the analysis accuracy can be improved by referring to related literature. By referring to related databases, the analysis accuracy can be improved. By referring to related information, the analysis accuracy can be improved.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives a call. For example, the reception unit can receive a call that has come in to a home phone. The reception unit can also obtain information about the caller. Step 2: The analysis unit analyzes the content of the call received by the reception unit. For example, the analysis unit converts the content of the call into text using voice recognition technology and analyzes that content. The analysis unit can use generation AI to analyze the content of the call and determine whether it is an essential call. Step 3: The notification unit notifies the user based on the content analyzed by the analysis unit. For example, the notification unit can send a notification to the user's smartphone. The notification unit can use the generation AI to send an appropriate notification to the user. Step 4: The response department automatically responds to calls determined to be unnecessary by the analysis department. For example, the response department can automatically send a rejection message to sales calls. The response department can use generative AI to respond appropriately to unnecessary calls.

[0068] (Example 2) A telephone reception operator system according to an embodiment of the present invention uses a generation AI to accept and analyze calls to a home phone, determine whether the call is necessary, and notify the user. In the telephone reception operator system, the generation AI analyzes the content of the call and distinguishes between necessary and unnecessary calls. For example, a call from a relative or other person is determined to be necessary, while a sales call is determined to be unnecessary. If the call is determined to be necessary, the generation AI notifies the user, and if the call is determined to be unnecessary, the generation AI automatically handles the call. For example, the generation AI automatically sends a rejection message to sales calls. This mechanism allows users to receive only necessary calls without being bothered by unnecessary sales calls. This allows the telephone reception operator system to receive only necessary calls without being bothered by unnecessary sales calls. For example, users can receive important messages from relatives or other people without missing them, and they will not be bothered by sales calls.

[0069] A telephone reception operator system according to an embodiment includes a reception unit, an analysis unit, a notification unit, and a response unit. The reception unit receives telephone calls. For example, the reception unit can receive calls to a home telephone. The reception unit can also acquire caller information. The analysis unit analyzes the content of the calls received by the reception unit. For example, the analysis unit converts the content of the calls into text using voice recognition technology and analyzes the content. The analysis unit can analyze the content of the calls using a generation AI and determine whether the calls are necessary. The notification unit notifies the user based on the content analyzed by the analysis unit. For example, the notification unit can send a notification to the user's smartphone. The notification unit can send an appropriate notification to the user using a generation AI. The response unit automatically handles calls determined to be unnecessary by the analysis unit. For example, the response unit can automatically send a rejection message to sales calls. The response unit can use a generation AI to provide an appropriate response to unnecessary calls. As a result, the telephone reception operator system according to an embodiment allows users to receive only necessary calls without being bothered by unnecessary sales calls.

[0070] The reception unit can analyze the call source and determine that a call from a relative or the like is an important call. The reception unit can, for example, analyze a telephone number and identify the call source. For example, the reception unit can compare the telephone number with a database and determine that a call from a relative or the like is an important call. The reception unit can also analyze geographical information of the call source and determine that a call from a relative or the like is an important call. For example, the reception unit can determine that a call from a relative or the like is an important call based on the geographical information of the call source. This allows calls from relatives or the like to be received with priority.

[0071] The analysis unit can convert the contents of the phone call into text and analyze the contents. The analysis unit can convert the contents of the phone call into text, for example, using voice recognition technology. For example, the analysis unit can convert the contents of the phone call into text in real time using voice recognition technology. The analysis unit can also analyze the converted text and determine whether the call is necessary. For example, the analysis unit can analyze the contents of the phone call using text analysis technology and determine whether the call is necessary. This allows the contents of the phone call to be analyzed accurately.

[0072] The notification unit can send notifications to the user's smartphone. The notification unit can send notifications to the user's smartphone using, for example, push notifications. For example, the notification unit can use generation AI to send appropriate notifications to the user's smartphone. The notification unit can also send notifications to the user's smartphone using SMS. For example, the notification unit can use SMS to notify the user that an important call has come in. The notification unit can also send notifications to the user's smartphone using app notifications. For example, the notification unit can use a dedicated app to send notifications to the user. This allows the user to check important calls on their smartphone.

[0073] The response unit can automatically send a rejection message to sales calls. The response unit, for example, automatically sends a rejection message to sales calls. For example, the response unit uses a generation AI to send an appropriate rejection message to sales calls. The response unit can also use a message template to send a rejection message to sales calls. For example, the response unit uses a message template prepared in advance to send a rejection message to sales calls. The response unit can also adjust the timing of sending the rejection message to sales calls. For example, the response unit sends a rejection message immediately after the call comes in. This prevents users from being bothered by sales calls.

[0074] The reception unit can estimate the user's emotions and adjust the method of accepting calls based on the estimated user emotions. The reception unit can estimate the user's emotions using, for example, voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to estimate emotions. The reception unit can also estimate the user's emotions using facial expression recognition technology. For example, the reception unit can analyze the user's facial expressions captured with a camera to estimate emotions. The reception unit can also estimate the user's emotions using text analysis technology. For example, the reception unit can analyze text entered by the user to estimate emotions. This enables appropriate call acceptance based on the user's emotions. For example, if the user is feeling stressed, the generation AI automatically accepts the call and analyzes the content before notifying the user. If the user is relaxed, the generation AI accepts the call and notifies the user directly to determine whether it is an essential call. If the user is busy, the generation AI accepts the call and notifies the user only of important calls, while automatically answering other calls.

[0075] The reception unit can analyze the caller's past call history and change the reception method depending on the importance. The reception unit, for example, analyzes call history and determines the importance of the caller. For example, the reception unit prioritizes callers who have made important calls in the past. The reception unit can also analyze the frequency of callers and determine the importance. For example, the reception unit prioritizes callers who contact frequently. The reception unit can also analyze the content of calls from new numbers and determine the importance. For example, the reception unit analyzes calls from new numbers and determines whether the call is important. This enables appropriate responses based on the caller's past call history. For example, if the caller has made important calls in the past, the generation AI prioritizes receiving the call and notifies the user. If the caller has made sales calls in the past, the generation AI automatically sends a message declining the call. If the caller is a new number, the generation AI analyzes the content of the call, determines the importance, and then notifies the user.

[0076] When accepting a call, the reception unit can adjust the reception method taking into account the geographical information of the caller. The reception unit acquires the geographical information of the caller using, for example, GPS data. For example, the reception unit adjusts the reception method of the call based on the geographical information of the caller. The reception unit can also acquire the geographical information of the caller using an IP address. For example, the reception unit analyzes the IP address to identify the geographical information of the caller. The reception unit can also adjust the reception method of the call based on the geographical information of the caller. For example, the reception unit prioritizes reception of calls from the area where the user lives. If the caller is from overseas, the reception unit automatically sends a rejection message. If the caller is from a specific area, the reception method is adjusted based on the characteristics of that area. This makes it possible to respond appropriately based on the geographical information of the caller.

[0077] When accepting a call, the reception unit can analyze the caller's voice tone and language and select an appropriate response. The reception unit, for example, uses a voice analysis algorithm to analyze the caller's voice tone. For example, if the caller's voice tone indicates an emergency, the reception unit prioritizes accepting the call. The reception unit can also analyze the caller's language using a language model. For example, if the caller's voice tone indicates an emergency, the reception unit automatically sends a decline message. The reception unit can also select an appropriate response based on the caller's voice tone and language. For example, if the caller's voice tone indicates a sales characteristic, the reception unit automatically sends a decline message. This enables an appropriate response based on the caller's voice tone and language. For example, if the caller's voice tone indicates an emergency, the generation AI prioritizes accepting the call and notifies the user. If the caller's language is not the user's native language, the generation AI automatically sends a decline message. If the caller's voice tone indicates a sales characteristic, the generation AI automatically sends a decline message.

[0078] The reception unit can estimate the user's emotions and determine the priority of receiving calls based on the estimated user's emotions. The reception unit can estimate the user's emotions using, for example, voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotion. The reception unit can also estimate the user's emotions using facial expression recognition technology. For example, the reception unit can analyze the user's facial expressions captured with a camera to estimate the emotion. The reception unit can also estimate the user's emotions using text analysis technology. For example, the reception unit can analyze text entered by the user to estimate the emotion. This allows for the appropriate priority of receiving calls to be determined according to the user's emotions. For example, if the user is feeling stressed, the generation AI can prioritize only important calls and automatically answer other calls. If the user is relaxed, the generation AI can accept all calls and notify the user. If the user is busy, the generation AI can prioritize only urgent calls and notify the user later.

[0079] When accepting a call, the reception unit can analyze the caller's social media activity and prioritize accepting calls that are highly relevant. The reception unit, for example, analyzes the content of social media posts to determine the relevance of the caller. For example, the reception unit prioritizes accepting calls if the caller is included in the user's friend list. The reception unit can also prioritize accepting calls if the caller is someone the user frequently interacts with on social media. For example, the reception unit prioritizes accepting calls if the caller has common interests with the user on social media. This enables appropriate responses based on the caller's social media activity. For example, if the caller is included in the user's friend list, the generation AI prioritizes accepting the call and notifies the user. If the caller is someone the user frequently interacts with on social media, the generation AI prioritizes accepting the call and notifies the user. If the caller has common interests with the user on social media, the generation AI prioritizes accepting the call and notifies the user.

[0080] When accepting a call, the reception unit can refer to the user's calendar information and select the optimal timing to accept the call. The reception unit refers to calendar information such as Google Calendar or Outlook Calendar. For example, if the user is in a meeting, the reception unit automatically accepts the call and notifies the user later. Furthermore, if the user is on vacation, the reception unit can prioritize only important calls and notify the user later for other calls. For example, the reception unit selects the optimal timing to accept the call from the user's calendar information and accepts the call. In this way, an appropriate timing to accept the call is selected based on the user's calendar information. For example, if the user is in a meeting, the generation AI automatically accepts the call and notifies the user later. If the user is on vacation, the generation AI prioritizes only important calls and notifies the user later for other calls. The reception unit selects the optimal timing to accept the call from the user's calendar information and accepts the call.

[0081] When accepting a call, the reception unit can customize the acceptance method by reflecting the user's past feedback. The reception unit customizes the acceptance method by, for example, referring to the user's past ratings and comments. For example, if the user has previously disliked sales calls, the reception unit can automatically send a message declining the call. The reception unit can also prioritize accepting calls and notify the user if the user has previously missed an important call. For example, the reception unit customizes the optimal acceptance method based on the user's past feedback. This allows the appropriate acceptance method to be customized based on the user's past feedback. For example, if the user has previously disliked sales calls, the generation AI automatically sends a message declining the call. If the user has previously missed an important call, the generation AI prioritizes accepting calls and notifies the user. The optimal acceptance method is customized based on the user's past feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. The analysis unit, for example, uses voice analysis technology to estimate the user's emotions. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit can also estimate the user's emotions using facial expression recognition technology. For example, the analysis unit analyzes the user's facial expressions captured with a camera to estimate the emotions. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit analyzes text entered by the user to estimate the emotions. This allows the analysis accuracy to be adjusted appropriately according to the user's emotions. For example, if the user is feeling stressed, the generation AI increases the analysis accuracy and notifies only important calls. If the user is relaxed, the generation AI maintains the analysis accuracy as normal and analyzes all calls. If the user is busy, the generation AI increases the analysis accuracy and notifies only urgent calls.

[0083] When analyzing the contents of a call, the analysis unit can improve the accuracy of the analysis by referring to data on similar calls from the past. For example, the analysis unit improves the accuracy of the analysis by referring to data on important calls received in the past. For example, the analysis unit improves the accuracy of the analysis by referring to data on sales calls received in the past. The analysis unit can also improve the accuracy of the analysis based on data on similar calls from the past. For example, the analysis unit stores data on similar calls from the past in a database and refers to it during analysis. This improves the accuracy of the analysis based on data on similar calls from the past. For example, the analysis unit improves the accuracy of the analysis by referring to data on important calls received in the past. The analysis unit improves the accuracy of the analysis by referring to data on sales calls received in the past. The analysis accuracy is improved based on data on similar calls from the past.

[0084] When analyzing the contents of a phone call, the analysis unit can change the analysis method taking into account the caller's attribute information. The analysis unit changes the analysis method taking into account attribute information such as the caller's age, gender, and occupation. For example, if the caller is a relative, the analysis unit performs a detailed analysis to determine whether the call is important. In addition, if the caller is a sales company, the analysis unit can perform a simplified analysis and determine that the call is unnecessary. For example, the analysis unit changes the analysis method based on the caller's attribute information and performs the optimal analysis. This allows an appropriate analysis method to be selected based on the caller's attribute information. For example, if the caller is a relative, the generation AI performs a detailed analysis to determine whether the call is important. If the caller is a sales company, the generation AI performs a simplified analysis and determines that the call is unnecessary. The analysis method is changed based on the caller's attribute information and performs the optimal analysis.

[0085] When analyzing the contents of a telephone call, the analysis unit can use voice recognition technology to more accurately understand the caller's intention. For example, the analysis unit uses voice recognition technology to convert the contents of the telephone call into text and analyze the intention. For example, the analysis unit uses voice recognition technology to convert the caller's voice into text and analyze the intention. The analysis unit can also analyze the tone of the voice to understand the caller's intention. For example, the analysis unit analyzes the tone of the caller's voice to understand the intention. The analysis unit can also use voice recognition technology to more accurately understand the caller's intention. For example, the analysis unit uses a deep learning model to analyze the caller's intention. As a result, the use of voice recognition technology can more accurately understand the caller's intention. For example, the caller's voice is converted into text and the intention is analyzed. The caller's tone of the voice is analyzed to understand the intention. The use of voice recognition technology can more accurately understand the caller's intention.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, uses voice analysis technology to estimate the user's emotions. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate emotions. The analysis unit can also estimate the user's emotions using facial expression recognition technology. For example, the analysis unit analyzes the user's facial expressions captured with a camera to estimate emotions. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit analyzes text entered by the user to estimate emotions. This makes it possible to display appropriate analysis results according to the user's emotions. For example, if the user is feeling stressed, the generation AI displays concise analysis results. If the user is relaxed, the generation AI displays detailed analysis results. If the user is busy, the generation AI displays analysis results that focus on the main points.

[0087] When analyzing the contents of a phone call, the analysis unit can correct the analysis result by taking into account the geographical information of the caller. The analysis unit, for example, uses GPS data to acquire the geographical information of the caller. For example, the analysis unit corrects the analysis result based on the geographical information of the caller. The analysis unit can also acquire the geographical information of the caller using an IP address. For example, the analysis unit analyzes the IP address to identify the geographical information of the caller. The analysis unit can also correct the analysis result based on the geographical information of the caller. For example, the analysis unit corrects the analysis result if the caller is from the area where the user lives. If the caller is from overseas, the analysis unit corrects the analysis result. The analysis result is corrected based on the geographical information of the caller. As a result, an appropriate analysis result is corrected based on the geographical information of the caller. For example, if the caller is from the area where the user lives, the generation AI corrects the analysis result. If the caller is from overseas, the generation AI corrects the analysis result. The analysis result is corrected based on the geographical information of the caller.

[0088] When analyzing the contents of a telephone call, the analysis unit can improve the accuracy of the analysis by referring to related literature and databases. The analysis unit, for example, refers to related literature and improves the accuracy of the analysis. For example, the analysis unit refers to academic papers and patent databases and improves the accuracy of the analysis. The analysis unit can also improve the accuracy of the analysis by referring to related databases. For example, the analysis unit improves the accuracy of the analysis based on related databases. This improves the accuracy of the analysis based on related literature and databases. For example, the analysis unit improves the accuracy of the analysis by referring to related literature. The analysis unit improves the accuracy of the analysis by referring to related databases. The analysis unit improves the accuracy of the analysis based on related information.

[0089] When analyzing the content of a call, the analysis unit can customize the analysis results by taking into account the industry information of the caller. The analysis unit, for example, customizes the analysis results by taking into account the industry information of the caller. For example, if the caller is in the medical industry, the analysis unit customizes the analysis results based on medical-related information. Furthermore, if the caller is in the financial industry, the analysis unit can also customize the analysis results based on financial-related information. For example, the analysis unit customizes the analysis results based on the industry information of the caller. This allows an appropriate analysis result to be customized based on the industry information of the caller. For example, if the caller is in the medical industry, the generation AI customizes the analysis results based on medical-related information. If the caller is in the financial industry, the generation AI customizes the analysis results based on financial-related information. The analysis results are customized based on the industry information of the caller.

[0090] The notification unit can estimate the user's emotions and adjust the way the notification is presented based on the estimated user's emotions. The notification unit can estimate the user's emotions using, for example, voice analysis technology. For example, the notification unit can analyze the tone and speed of the user's voice to estimate the emotions. The notification unit can also estimate the user's emotions using facial expression recognition technology. For example, the notification unit can analyze the user's facial expressions captured with a camera to estimate the emotions. The notification unit can also estimate the user's emotions using text analysis technology. For example, the notification unit can analyze text entered by the user to estimate the emotions. This makes it possible to present notifications appropriately according to the user's emotions. For example, if the user is feeling stressed, the generation AI can send a concise notification. If the user is relaxed, the generation AI can send a detailed notification. If the user is busy, the generation AI can send a notification that focuses on the main points.

[0091] At the time of notification, the notification unit can select the optimal notification method by referring to the user's past notification history. The notification unit, for example, refers to the user's past notification history and selects the optimal notification method. For example, the notification unit selects the optimal notification method based on notification methods that the user has preferred in the past. The notification unit can also customize the optimal notification method based on the user's past notification history. For example, the notification unit refers to the user's past notification history and selects the optimal notification method. In this way, an appropriate notification method is selected based on the user's past notification history. For example, the generation AI selects the optimal notification method based on notification methods that the user has preferred in the past. The user's past notification history is referred to and the optimal notification method is selected. The optimal notification method is customized based on the user's past notification history.

[0092] The notification unit can analyze the user's current activity status at the time of notification and select the optimal notification timing. The notification unit analyzes, for example, the user's location information and activity log to understand the current activity status. For example, the notification unit delays notifications when the user is in a meeting. The notification unit can also send only important notifications when the user is on vacation. For example, the notification unit analyzes the user's current activity status and selects the optimal notification timing. In this way, an appropriate notification timing is selected based on the user's current activity status. For example, when the user is in a meeting, the generation AI delays notifications. When the user is on vacation, the generation AI sends only important notifications. The user's current activity status is analyzed and the optimal notification timing is selected.

[0093] When sending a notification, the notification unit can select the optimal notification means by taking into account the user's device information. The notification unit selects the notification means by taking into account device information such as the user's device type, OS version, and connection status. For example, if the user is using a smartphone, the notification unit sends a push notification. The notification unit can also send an email notification if the user is using a PC. For example, the notification unit selects the optimal notification means based on the user's device information. This allows the appropriate notification means to be selected based on the user's device information. For example, if the user is using a smartphone, the generation AI sends a push notification. If the user is using a PC, the generation AI sends an email notification. The optimal notification means is selected based on the user's device information.

[0094] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions. The notification unit can estimate the user's emotions using, for example, voice analysis technology. For example, the notification unit can analyze the tone and speed of the user's voice to estimate the emotions. The notification unit can also estimate the user's emotions using facial expression recognition technology. For example, the notification unit can analyze the user's facial expressions captured with a camera to estimate the emotions. The notification unit can also estimate the user's emotions using text analysis technology. For example, the notification unit can analyze text entered by the user to estimate the emotions. This allows the appropriate priority of notifications to be determined according to the user's emotions. For example, if the user is feeling stressed, the generation AI will prioritize sending only important notifications. If the user is relaxed, the generation AI will send all notifications. If the user is busy, the generation AI will prioritize sending only urgent notifications.

[0095] The notification unit can select the optimal notification method by taking into consideration the user's geographical information when sending a notification. The notification unit acquires the user's geographical information, for example, using GPS data. For example, the notification unit sends a voice notification when the user is at home. The notification unit can also send a vibration notification when the user is out. For example, the notification unit selects the optimal notification method based on the user's geographical information. In this way, an appropriate notification method is selected based on the user's geographical information. For example, when the user is at home, the generation AI sends a voice notification. When the user is out, the generation AI sends a vibration notification. The optimal notification method is selected based on the user's geographical information.

[0096] The notification unit can analyze the user's social media activity at the time of notification and prioritize related notifications. The notification unit can, for example, analyze the content of posts on social media and prioritize related notifications. For example, the notification unit prioritizes sending notifications about places the user has checked in to on social media. The notification unit can also analyze the content of posts on social media by prioritizing sending related notifications. For example, the notification unit can refer to the activities of the user's friends on social media and prioritize sending related notifications. This allows appropriate notifications to be sent based on the user's social media activity. For example, the notification unit can prioritize sending notifications about places the user has checked in to on social media. The notification unit can analyze the content of posts on social media by prioritizing sending related notifications. The notification unit can prioritize sending related notifications based on the activities of the user's friends on social media.

[0097] The notification unit can customize the notification method by reflecting the user's past feedback when notifying. The notification unit, for example, refers to the user's past ratings and comment content to customize the notification method. For example, the notification unit customizes the notification method based on the user's previously preferred notification method. The notification unit can also customize the optimal notification method based on the user's past feedback. For example, the notification unit refers to the user's past feedback to customize the optimal notification method. In this way, an appropriate notification method is customized based on the user's past feedback. For example, the generation AI customizes the notification method based on the user's previously preferred notification method. The optimal notification method is customized by referring to the user's past feedback. The notification method is customized based on the user's past feedback.

[0098] The response unit can estimate the user's emotions and adjust the response method based on the estimated user's emotions. The response unit, for example, uses voice analysis technology to estimate the user's emotions. For example, the response unit analyzes the tone and speed of the user's voice to estimate the emotions. The response unit can also estimate the user's emotions using facial expression recognition technology. For example, the response unit analyzes the user's facial expressions captured with a camera to estimate the emotions. The response unit can also estimate the user's emotions using text analysis technology. For example, the response unit analyzes text entered by the user to estimate the emotions. This allows an appropriate response method to be selected according to the user's emotions. For example, if the user is feeling stressed, the generation AI automatically sends a message declining the call. If the user is relaxed, the generation AI accepts the call and notifies the user. If the user is busy, the generation AI accepts only important calls and automatically declines other calls.

[0099] When responding, the response unit can select the optimal response method by referring to past response history. The response unit, for example, refers to the user's past response history and selects the optimal response method. For example, the response unit selects the optimal response method based on response methods that the user has preferred in the past. The response unit can also customize the optimal response method based on the user's past response history. For example, the response unit refers to the user's past response history and selects the optimal response method. In this way, an appropriate response method is selected based on the past response history. For example, the generation AI selects the optimal response method based on response methods that the user has preferred in the past. The optimal response method is selected by referring to the user's past response history. The optimal response method is customized based on the user's past response history.

[0100] When responding, the response unit can change the response method taking into account the caller's attribute information. The response unit changes the response method taking into account attribute information such as the caller's age, gender, and occupation. For example, if the caller is a relative, the response unit responds in detail and notifies the user. The response unit can also automatically send a message declining the call if the caller is a sales company. For example, the response unit changes the response method based on the caller's attribute information and takes the optimal response. In this way, an appropriate response method is selected based on the caller's attribute information. For example, if the caller is a relative, the generation AI responds in detail and notifies the user. If the caller is a sales company, the generation AI automatically sends a message declining the call. The response method is changed and the optimal response is taken based on the caller's attribute information.

[0101] When responding, the response unit can analyze the caller's voice tone and language and select an appropriate response. The response unit, for example, uses a voice analysis algorithm to analyze the caller's voice tone. For example, if the caller's voice tone indicates an emergency, the response unit prioritizes responding and notifies the user. The response unit can also analyze the caller's language using a language model. For example, if the caller's voice tone is not the user's native language, the response unit automatically sends a decline message. The response unit can also select an appropriate response based on the caller's voice tone and language. For example, if the caller's voice tone has sales characteristics, the response unit automatically sends a decline message. In this way, an appropriate response is selected based on the caller's voice tone and language. For example, if the caller's voice tone indicates an emergency, the generation AI prioritizes responding and notifies the user. If the caller's language is not the user's native language, the generation AI automatically sends a decline message. If the caller's voice tone has sales characteristics, the generation AI automatically sends a decline message.

[0102] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated user's emotions. The response unit, for example, uses voice analysis technology to estimate the user's emotions. For example, the response unit analyzes the tone and speed of the user's voice to estimate the emotions. The response unit can also estimate the user's emotions using facial expression recognition technology. For example, the response unit analyzes the user's facial expressions captured with a camera to estimate the emotions. The response unit can also estimate the user's emotions using text analysis technology. For example, the response unit analyzes text entered by the user to estimate the emotions. This allows the appropriate priority of responses to be determined according to the user's emotions. For example, if the user is feeling stressed, the generation AI prioritizes only important responses. If the user is relaxed, the generation AI handles all responses. If the user is busy, the generation AI prioritizes only urgent responses.

[0103] When responding, the response unit can select the optimal response method by taking into account the geographical information of the caller. The response unit, for example, uses GPS data to acquire the geographical information of the caller. For example, the response unit selects the response method based on the geographical information of the caller. The response unit can also acquire the geographical information of the caller using an IP address. For example, the response unit analyzes the IP address to identify the geographical information of the caller. The response unit can also select the response method based on the geographical information of the caller. For example, if the caller is a call from the user's area, the response unit prioritizes responding and notifies the user. If the caller is a call from overseas, the response unit automatically sends a rejection message. The optimal response method is selected based on the geographical information of the caller. In this way, an appropriate response method is selected based on the geographical information of the caller. For example, if the caller is a call from the user's area, the generation AI prioritizes responding and notifies the user. If the caller is a call from overseas, the generation AI automatically sends a rejection message. The optimal response method is selected based on the geographical information of the caller.

[0104] When responding, the response unit can analyze the social media activity of the sender and prioritize relevant responses. The response unit, for example, analyzes the content of social media posts and prioritizes relevant responses. For example, if the sender is included in the user's friend list, the response unit prioritizes responding and notifying the user. The response unit can also prioritize responding if the sender is someone the user frequently interacts with on social media. For example, the response unit prioritizes responding if the sender has common interests with the user on social media. This allows an appropriate response to be taken based on the sender's social media activity. For example, if the sender is included in the user's friend list, the generation AI prioritizes responding and notifying the user. If the sender is someone the user frequently interacts with on social media, the generation AI prioritizes responding and notifying the user. If the sender has common interests with the user on social media, the generation AI prioritizes responding and notifying the user.

[0105] The response unit can customize the response method by reflecting the user's past feedback when responding. The response unit, for example, refers to the user's past ratings and comment content to customize the response method. For example, the response unit customizes the response method based on the user's preferred response methods in the past. The response unit can also customize the optimal response method based on the user's past feedback. For example, the response unit refers to the user's past feedback to customize the optimal response method. In this way, an appropriate response method is customized based on the user's past feedback. For example, the generation AI customizes the response method based on the user's preferred response methods in the past. The optimal response method is customized by referring to the user's past feedback. The response method is customized based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, notification unit, and response unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a call using the communication I / F 44 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, converts the content of the call into text using voice recognition technology, and analyzes it using a generation AI. The notification unit is realized by the control unit 46A of the smart device 14, and sends a notification to the user's smartphone. The response unit is realized by the specific processing unit 290 of the data processing device 12, and automatically sends a message to decline unwanted calls. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, notification unit, and response unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a call using the communication I / F 44 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, converts the content of the call into text using voice recognition technology, and analyzes it using a generation AI. The notification unit is realized by the control unit 46A of the smart glasses 214, and sends a notification to the user's smartphone. The response unit is realized by the specific processing unit 290 of the data processing device 12, and automatically sends a message to decline unwanted calls. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, notification unit, and response unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a call using the communication I / F 44 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, converts the content of the call into text using voice recognition technology, and analyzes it using a generation AI. The notification unit is realized by the control unit 46A of the headset type terminal 314, and sends a notification to the user's smartphone. The response unit is realized by the specific processing unit 290 of the data processing device 12, and automatically sends a message to decline unwanted calls. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, notification unit, and response unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a call using the communication I / F 44 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, converts the content of the call into text using voice recognition technology, and analyzes it using a generative AI. The notification unit is realized by the control unit 46A of the robot 414, and sends a notification to the user's smartphone. The response unit is realized by the specific processing unit 290 of the data processing device 12, and automatically sends a message to decline unwanted calls.

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

[0107] The reception unit can refer to the user's calendar information and select the optimal timing to accept calls. For example, the reception unit refers to calendar information such as Google Calendar or Outlook Calendar. If the user is in a meeting, the call will be automatically accepted and the call will be notified later. Also, if the user is on vacation, it is possible to prioritize only important calls and notify other calls later. In this way, an appropriate acceptance timing is selected based on the user's calendar information. For example, if the user is in a meeting, the generation AI will automatically accept the call and notify the call later. If the user is on vacation, the generation AI will prioritize only important calls and notify other calls later. The optimal acceptance timing is selected from the user's calendar information and the call will be accepted.

[0108] When analyzing the content of a call, the analysis unit can improve the accuracy of the analysis by referring to data on similar calls from the past. For example, the analysis unit can improve the accuracy of the analysis by referring to data on important calls received in the past. The analysis unit can also improve the accuracy of the analysis based on data on similar calls from the past. For example, the analysis unit stores data on similar calls from the past in a database and refers to it during analysis. This improves the accuracy of the analysis based on data on similar calls from the past. For example, the analysis unit can improve the accuracy of the analysis by referring to data on important calls received in the past. The analysis unit can improve the accuracy of the analysis by referring to data on sales calls received in the past. The analysis unit can improve the accuracy of the analysis based on data on similar calls from the past.

[0109] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on the estimated user emotions. For example, the notification unit can estimate the user's emotions using voice analysis technology. It can estimate emotions by analyzing the tone and speed of the user's voice. The notification unit can also estimate the user's emotions using facial expression recognition technology. For example, it can estimate emotions by analyzing the user's facial expressions captured with a camera. It can also estimate the user's emotions using text analysis technology. For example, it can estimate emotions by analyzing text entered by the user. This makes it possible to present notifications appropriately according to the user's emotions. For example, if the user is feeling stressed, the generation AI will send a concise notification. If the user is relaxed, the generation AI will send a detailed notification. If the user is busy, the generation AI will send a notification that hits the main points.

[0110] The response unit can estimate the user's emotions and adjust the response method based on the estimated user emotions. For example, the response unit estimates the user's emotions using voice analysis technology. It estimates emotions by analyzing the tone and speed of the user's voice. The response unit can also estimate the user's emotions using facial expression recognition technology. For example, it can estimate emotions by analyzing the user's facial expressions captured with a camera. It can also estimate the user's emotions using text analysis technology. For example, it can estimate emotions by analyzing text entered by the user. This allows an appropriate response method to be selected according to the user's emotions. For example, if the user is feeling stressed, the generation AI automatically sends a message declining the call. If the user is relaxed, the generation AI accepts the call and notifies the user. If the user is busy, the generation AI will accept only important calls and automatically decline other calls.

[0111] When analyzing the contents of a phone call, the analysis unit can change the analysis method by taking into account the caller's attribute information. For example, the analysis unit changes the analysis method by taking into account attribute information such as the caller's age, gender, and occupation. If the caller is a relative, the analysis unit performs a detailed analysis to determine whether the call is important. Also, if the caller is a sales company, the analysis unit can perform a simplified analysis and determine that the call is unnecessary. For example, the analysis method can be changed and the optimal analysis can be performed based on the caller's attribute information. This allows the appropriate analysis method to be selected based on the caller's attribute information. For example, if the caller is a relative, the generation AI performs a detailed analysis to determine whether the call is important. If the caller is a sales company, the generation AI performs a simplified analysis and determines that the call is unnecessary. The analysis method can be changed and the optimal analysis can be performed based on the caller's attribute information.

[0112] When accepting a call, the reception unit can analyze the caller's social media activity and prioritize highly relevant calls. For example, the reception unit analyzes the content of social media posts and determines the relevance of the caller. If the caller is included in the user's friend list, the reception unit prioritizes accepting the call. Also, if the caller is someone the user frequently interacts with on social media, the reception unit can prioritize accepting the call. For example, if the caller has common interests with the user on social media, the reception unit prioritizes accepting the call. This enables an appropriate response based on the caller's social media activity. For example, if the caller is included in the user's friend list, the generation AI prioritizes accepting the call and notifies the user. If the caller is someone the user frequently interacts with on social media, the generation AI prioritizes accepting the call and notifies the user. If the caller has common interests with the user on social media, the generation AI prioritizes accepting the call and notifies the user.

[0113] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, the analysis unit uses voice analysis technology to estimate the user's emotions. It analyzes the tone and speed of the user's voice to estimate emotions. The analysis unit can also estimate the user's emotions using facial expression recognition technology. For example, it can analyze the user's facial expressions captured with a camera to estimate emotions. It can also estimate the user's emotions using text analysis technology. For example, it can analyze the text entered by the user to estimate emotions. This allows the analysis accuracy to be adjusted appropriately according to the user's emotions. For example, if the user is feeling stressed, the generation AI will increase the analysis accuracy and notify only important calls. If the user is relaxed, the generation AI will keep the analysis accuracy normal and analyze all calls. If the user is busy, the generation AI will increase the analysis accuracy and notify only urgent calls.

[0114] When notifying, the notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification unit refers to the user's past notification history and selects the optimal notification method. The optimal notification method is selected based on the notification methods the user has preferred in the past. The notification unit can also customize the optimal notification method based on the user's past notification history. For example, the notification unit refers to the user's past notification history and selects the optimal notification method. This allows an appropriate notification method to be selected based on the user's past notification history. For example, the generation AI selects the optimal notification method based on the notification methods the user has preferred in the past. The notification unit refers to the user's past notification history and selects the optimal notification method. The optimal notification method is customized based on the user's past notification history.

[0115] When responding, the response unit can analyze the caller's voice tone and language and select an appropriate response. For example, the response unit uses a voice analysis algorithm to analyze the caller's voice tone. If the caller's voice tone indicates an emergency, the response unit prioritizes responding and notifies the user. The response unit can also analyze the caller's language using a language model. For example, if the caller's language is not the user's native language, the response unit automatically sends a decline message. The response unit can also select an appropriate response based on the caller's voice tone and language. For example, if the caller's voice tone has sales characteristics, the response unit automatically sends a decline message. This allows the appropriate response to be selected based on the caller's voice tone and language. For example, if the caller's voice tone indicates an emergency, the generation AI prioritizes responding and notifies the user. If the caller's language is not the user's native language, the generation AI automatically sends a decline message. If the caller's voice tone has sales characteristics, the generation AI automatically sends a decline message.

[0116] When analyzing the contents of a telephone call, the analysis unit can improve the accuracy of the analysis by referring to related literature and databases. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature. By referring to academic papers and patent databases, the analysis accuracy can be improved. The analysis unit can also improve the accuracy of the analysis by referring to related databases. For example, the analysis accuracy can be improved based on related databases. This improves the accuracy of the analysis based on related literature and databases. For example, the analysis accuracy can be improved by referring to related literature. By referring to related databases, the analysis accuracy can be improved. By referring to related information, the analysis accuracy can be improved.

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

[0118] Step 1: The reception unit receives a call. For example, the reception unit can receive a call that has come in to a home phone. The reception unit can also obtain information about the caller. Step 2: The analysis unit analyzes the content of the call received by the reception unit. For example, the analysis unit converts the content of the call into text using voice recognition technology and analyzes that content. The analysis unit can use generation AI to analyze the content of the call and determine whether it is an essential call. Step 3: The notification unit notifies the user based on the content analyzed by the analysis unit. For example, the notification unit can send a notification to the user's smartphone. The notification unit can use the generation AI to send an appropriate notification to the user. Step 4: The response department automatically responds to calls determined to be unnecessary by the analysis department. For example, the response department can automatically send a rejection message to sales calls. The response department can use generative AI to respond appropriately to unnecessary calls.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

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

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for receiving telephone calls; an analysis unit that analyzes the contents of the call received by the reception unit; a notification unit that notifies a user based on the content analyzed by the analysis unit; a response unit that automatically responds to calls determined to be unnecessary by the analysis unit; Equipped with A system characterized by:

2. The reception unit Analyze the source of the call and determine that calls from relatives, etc. are necessary calls. The system of claim 1 .

3. The analysis unit Convert phone calls into text and analyze the content The system of claim 1 .

4. The notification unit Send a notification to the user's smartphone The system of claim 1 .

5. The corresponding part is Send an automatic rejection message to sales calls The system of claim 1 .

6. The reception unit Estimate the user's emotions and adjust the way you accept calls based on the estimated user emotions The system of claim 1 .

7. The reception unit Analyze the caller's past history and change the reception method depending on the importance of the call The system of claim 1 .

8. The reception unit When accepting a call, adjust the way you accept it based on the geographic location of the caller The system of claim 1 .

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