System

Abstract

An object of a system according to an embodiment is to efficiently respond to an unnecessary sales call made to a fixed telephone.SOLUTION: A system includes a power reception unit, a determination unit, a response unit, and a transfer unit. The power reception unit has a power reception telephone response function. The determination unit determines whether or not a response is necessary based on the content of the call received by the power receiving unit. The response unit performs an automatic response when the determination unit determines that the response is necessary. The transfer unit summarizes the contents responded by the response unit and transfers the information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately provide a means for efficiently responding to unwanted sales calls received on landlines, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently deal with unwanted sales calls made to landlines. [Means for solving the problem]

[0006] The system according to the embodiment includes a power receiving unit, a determination unit, a response unit, and a transfer unit. The power receiving unit has a function for responding to incoming calls. The determination unit determines whether a response is required based on the content of the call received by the power receiving unit. The response unit automatically responds when the determination unit determines that a response is required. The transfer unit summarizes the content of the response provided by the response unit and transfers the information. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently deal with unwanted sales calls coming to landlines. [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 operator system according to an embodiment of the present invention uses a generation AI to answer only those calls that are truly necessary among calls to a landline phone. In the telephone operator system, the generation AI uses natural language processing to answer calls, implements a response necessity determination algorithm to determine whether a response is necessary based on the content of the call, and, if necessary, automatically answers the call, summarizes the call, and forwards the necessary information. For example, in a telephone operator system, the generation AI uses natural language processing to answer calls. The generation AI understands the content of the call and provides an appropriate response. Next, the generation AI uses a response necessity determination algorithm to determine whether a response is necessary based on the content of the call. For example, if the call is a sales call, the generation AI determines that "this call does not require a response" and automatically ends the response. Furthermore, the generation AI automatically responds using natural language processing. For example, if the call is from an important contact, the generation AI responds by saying, "Mr. / Ms. X is not available right now. Would you like to leave a message?" Finally, the generation AI summarizes the content of the call and forwards the necessary information. For example, if a call comes from an important contact, the generation AI will summarize the call, saying something like, "This is a call from Mr. / Ms. X, and he / she wants to confirm tomorrow's schedule," and forward the necessary information. This allows the telephone operator system to automatically filter out sales calls coming to landlines and handle only those calls that are truly necessary. This allows the telephone operator system to reduce the burden of answering telephone calls within the home and achieve efficient communication. For example, by having the generation AI understand the content of the call and respond appropriately, important calls can be handled without being overlooked. Furthermore, by automatically filtering sales calls, time is no longer wasted on unnecessary calls. Furthermore, by having the generation AI summarize the content of the call and forward the necessary information, important information can be conveyed quickly and accurately.

[0029] A telephone operator system according to an embodiment includes a call receiving unit, a determination unit, a response unit, and a transfer unit. The call receiving unit uses a generation AI to answer a call. The call receiving unit understands the content of the call and provides an appropriate response using, for example, natural language processing technology. For example, when a call comes in, the call receiving unit can respond with, for example, "Hello, is this Mr. / Ms. X?" The call receiving unit can also analyze the content of the call and determine whether it is a call from an important contact. The determination unit uses a generation AI to determine whether a call needs to be responded to based on the content of the call received by the call receiving unit. The determination unit, for example, implements a response need determination algorithm to determine whether the call is a sales call. For example, if the call is a sales call, the determination unit can determine that "this call does not require a response" and automatically end the response. The determination unit can also determine that a call from an important contact requires a response. The response unit uses a generation AI to automatically respond when the determination unit determines that a response is required. The response unit automatically responds using, for example, natural language processing technology. For example, the answering unit can respond with, "Mr. / Ms. X is not in the office right now. Would you like to leave a message?" The answering unit can also provide an appropriate response based on the content of the call. The forwarding unit uses a generation AI to summarize the content of the response from the answering unit and forward the necessary information. The forwarding unit can summarize the content of the call and forward important information. For example, the forwarding unit can summarize the content of the call and forward important information. For example, the forwarding unit can summarize the content of the call and forward important information. For example, the forwarding unit can summarize the content of the call and forward important information. As a result, the call reception operator system according to the embodiment can automatically filter sales calls coming into a landline phone and handle only those calls that are truly necessary. For example, the call receiving unit can understand the content of the call and respond appropriately, allowing important calls to be handled without missing them. Furthermore, the judgment unit can automatically filter sales calls, eliminating the need for time wasted on unnecessary calls. Furthermore, the forwarding unit can summarize the content of the call and forward the necessary information, thereby quickly and accurately conveying important information.

[0030] The power receiving unit can answer calls using natural language processing. The power receiving unit, for example, uses natural language processing technology to understand the content of the call and provide an appropriate response. For example, when a call comes in, the power receiving unit can respond with, "Hello, is this Mr. / Ms. X?" The power receiving unit can also analyze the content of the call and determine whether it is a call from an important contact. For example, the power receiving unit can analyze the content of the call and, if it is a call from an important contact, respond with, "Mr. / Ms. X is not in the office right now. Would you like to leave a message?" This allows the generation AI to use natural language processing to understand the content of the call and provide an appropriate response. Some or all of the above-mentioned processing in the power receiving unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the power receiving unit can input the content of the call into the generation AI, which can then generate an appropriate response.

[0031] The determination unit implements a response necessity determination algorithm and can determine whether a response is necessary based on the content of the call. The determination unit, for example, implements a response necessity determination algorithm and determines whether the content of the call is a sales call. For example, if the call is a sales call, the determination unit can determine that "this call does not require a response" and automatically end the response. The determination unit can also determine that a response is necessary if the call is from an important contact. For example, if the call is from an important contact, the determination unit can determine that "this is a call from Mr. / Ms. X. Your response is necessary." This allows the generation AI to use the response necessity determination algorithm to appropriately determine whether a response is necessary based on the content of the call. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the determination unit can input the content of the call into the generation AI, and the generation AI can determine whether a response is necessary.

[0032] The response unit can automatically respond using natural language processing. The response unit automatically responds using, for example, natural language processing technology. For example, the response unit can respond, "Mr. / Ms. X is not in the office right now. Would you like to leave a message?" The response unit can also provide an appropriate response based on the content of the call. For example, if the call is from an important contact, the response unit can respond, "Mr. / Ms. X is not in the office right now. Would you like to leave a message?" This allows the generation AI to use natural language processing to provide an appropriate automatic response based on the content of the call. Some or all of the above-mentioned processing in the response unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the response unit can input the content of the call into the generation AI, which can then generate an appropriate response.

[0033] The forwarding unit can summarize the contents of the phone call and forward the information. For example, the forwarding unit summarizes the contents of the phone call and forwards important information. For example, the forwarding unit can generate a summary such as, "This is a call from Mr. / Ms. X, and he wants to confirm tomorrow's schedule," and forward the necessary information. The forwarding unit can also generate an appropriate summary based on the contents of the phone call. For example, if the call is from an important contact, the forwarding unit can generate a summary such as, "This is a call from Mr. / Ms. X, and he wants to confirm tomorrow's schedule," and forward the necessary information. In this way, important information can be efficiently conveyed by the generation AI summarizing the contents of the phone call and forwarding the necessary information. Some or all of the above-described processing in the forwarding unit may be performed using, or without, the generation AI. For example, the forwarding unit can input the contents of the phone call into the generation AI, which then generates a summary and forwards the necessary information.

[0034] When receiving a call, the receiving unit can analyze the caller information and change the response method. For example, if the caller is a relative, the generation AI in the receiving unit can respond politely. Also, if the caller is a sales call, the generation AI in the receiving unit can respond briefly and end the call early. Also, if the caller is an emergency contact, the generation AI can respond quickly and immediately convey important information. This allows important calls to be handled appropriately by changing the response method based on the caller information. Some or all of the above-mentioned processing in the receiving unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the receiving unit can input caller information into the generation AI, which can then generate an appropriate response method.

[0035] When receiving a call, the power receiving unit can optimize the response to a call from a specific caller by referring to past call history. For example, in the power receiving unit, the generation AI can provide a friendly response to a call from a caller with which the power receiving unit has frequently been in contact in the past. In addition, in the power receiving unit, the generation AI can provide a careful response to a call from a caller with which the power receiving unit has had trouble in the past. In addition, in the power receiving unit, the generation AI can quickly respond to a call from a caller with which the power receiving unit has received important contact in the past. In this way, by referring to the past call history, an optimal response can be provided to a call from a specific caller. Some or all of the above-mentioned processing in the power receiving unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the power receiving unit can input past call history into the generation AI, which can then generate an optimal response.

[0036] When receiving a call, the power receiving unit can adjust the response according to the language or dialect of the caller. For example, if the caller speaks English, the power receiving unit's generation AI can respond in English. Also, if the caller speaks Kansai dialect, the power receiving unit's generation AI can respond in Kansai dialect. Also, if the caller speaks French, the power receiving unit's generation AI can respond in French. This allows for more natural communication by customizing the response according to the language or dialect of the caller. Some or all of the above-mentioned processing in the power receiving unit may be performed using, or without, the generation AI. For example, the power receiving unit can input the language or dialect of the caller into the generation AI, which can then generate an appropriate response.

[0037] When receiving power, the power receiving unit can adjust the response content based on the geographical location information of the caller. For example, if the caller is far away, the power receiving unit's generation AI can respond according to the distance. Furthermore, if the caller is nearby, the power receiving unit's generation AI can also provide a friendly response. Furthermore, if the caller is overseas, the power receiving unit's generation AI can also provide an international response. This allows for a more appropriate response by adjusting the response content based on the geographical location information of the caller. Some or all of the above-described processing in the power receiving unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the power receiving unit can input the geographical location information of the caller into the generation AI, which can then generate an appropriate response.

[0038] When receiving a call, the receiving unit can analyze the social media activity of the caller and reflect related information in the response. For example, the receiving unit can have the generation AI provide topics based on the content the caller has recently posted on social media. The receiving unit can also have the generation AI customize the response based on the location where the caller checked in on social media. The receiving unit can also have the generation AI adjust the response taking into account the caller's social media friendships. This enables a more personalized response by customizing the response based on the caller's social media activity. Some or all of the above-mentioned processing in the receiving unit can be performed using, or without, the generation AI. For example, the receiving unit can input the caller's social media activity into the generation AI, which can then generate an appropriate response.

[0039] When receiving power, the power receiving unit can customize the response method by reflecting the user's past feedback. For example, the power receiving unit prioritizes response methods for which the user has given favorable feedback in the past. The power receiving unit can also avoid response methods for which the user has expressed dissatisfaction in the past. The power receiving unit can also have a generation AI select the optimal response method based on the user's past feedback. This allows the response method to be customized based on the user's past feedback, making it possible to provide a more satisfying response. Some or all of the above-mentioned processing in the power receiving unit may be performed using, or without, the generation AI. For example, the power receiving unit can input the user's past feedback into the generation AI, which then generates an appropriate response method.

[0040] When making a judgment, the judgment unit can analyze the content of the call in real time and determine whether a response is necessary. For example, if the content of the call is urgent, the judgment unit allows the generation AI to immediately determine whether a response is necessary. Furthermore, if the content of the call is a general inquiry, the judgment unit can also allow the generation AI to determine whether a normal response is necessary. Furthermore, if the content of the call is sales, the judgment unit can also allow the generation AI to determine whether a response is unnecessary. In this way, by analyzing the content of the call in real time, it is possible to quickly determine whether a response is necessary for important calls. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the content of the call into the generation AI, which can analyze it in real time and determine whether a response is necessary.

[0041] When making a judgment, the judgment unit can adjust the judgment algorithm by referring to past judgment history. In the judgment unit, for example, the generation AI adjusts the judgment algorithm based on the past judgment history. The judgment unit can also extract specific patterns from the past judgment history, and the generation AI can optimize the judgment algorithm. The judgment unit can also analyze the past judgment history, and the generation AI can set optimal judgment criteria. By referring to the past judgment history, the judgment algorithm can be optimized, making it possible to more appropriately determine whether or not a response is necessary. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the past judgment history into the generation AI, and the generation AI can adjust the judgment algorithm.

[0042] When making a judgment, the judgment unit can determine whether a response is necessary based on the attribute information of the caller. For example, if the caller is a relative, the judgment unit causes the generation AI to determine that a response is necessary. The judgment unit can also cause the generation AI to determine that a response is unnecessary if the caller is a sales call. The judgment unit can also cause the generation AI to determine that a response is necessary quickly if the caller is an emergency contact. This enables a more appropriate response by determining whether a response is necessary based on the attribute information of the caller. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the attribute information of the caller into the generation AI, and the generation AI can determine whether a response is necessary.

[0043] When making a judgment, the judgment unit can determine whether or not a response is necessary based on the geographical location information of the caller. For example, if the caller is calling from a distant location, the judgment unit causes the generation AI to determine whether or not a response is necessary. Furthermore, if the caller is calling from a nearby location, the judgment unit can also cause the generation AI to determine whether or not a normal response is necessary. Furthermore, if the caller is calling from overseas, the judgment unit can cause the generation AI to determine whether or not an international response is necessary. This enables a more appropriate response by determining whether or not a response is necessary based on the geographical location information of the caller. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the geographical location information of the caller into the generation AI, and the generation AI can determine whether or not a response is necessary.

[0044] When making a judgment, the judgment unit can refer to the literature of the sender to determine whether or not a response is necessary. For example, if the sender is related to a specific industry, the judgment unit can have the generation AI refer to related literature to determine whether or not a response is necessary. Furthermore, if the sender is related to a specific topic, the judgment unit can have the generation AI refer to related literature to determine whether or not a response is necessary. Furthermore, if the sender is related to a specific issue, the judgment unit can have the generation AI refer to related literature to determine whether or not a response is necessary. This enables a more appropriate response by determining whether or not a response is necessary based on the literature related to the sender. Some or all of the above-mentioned processing in the judgment unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the judgment unit can input the literature related to the sender into the generation AI, and the generation AI can determine whether or not a response is necessary.

[0045] When making a judgment, the judgment unit can determine whether or not to take action by taking into account the market value of the sender. For example, if the sender has a high market value, the judgment unit determines that the generation AI needs to take action. Furthermore, if the sender has a low market value, the judgment unit can also determine that the generation AI does not need to take action. Furthermore, the judgment unit can analyze the market value of the sender and have the generation AI determine the optimal need for action. This enables a more appropriate response by determining whether or not to take action based on the market value of the sender. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the market value of the sender into the generation AI, and the generation AI can determine whether or not to take action.

[0046] When answering a call, the response unit can analyze the content of the call in real time and generate a response. For example, if the content of the call is urgent, the generation AI of the response unit can respond quickly. Furthermore, if the content of the call is a general inquiry, the generation AI of the response unit can generate an appropriate response. Furthermore, if the content of the call is sales, the generation AI of the response unit can generate a concise response. In this way, an appropriate response can be generated by analyzing the content of the call in real time. Some or all of the above-mentioned processing in the response unit may be performed using, or without, the generation AI. For example, the response unit can input the content of the call into the generation AI, which can analyze it in real time and generate an appropriate response.

[0047] When responding, the response unit can adjust the response algorithm by referring to past response history. In the response unit, for example, the generation AI adjusts the response algorithm based on the past response history. The response unit can also extract specific patterns from the past response history, and the generation AI can optimize the response algorithm. The response unit can also analyze the past response history, and the generation AI can set optimal response criteria. By referring to the past response history, the response algorithm can be optimized, enabling a more appropriate response. Some or all of the above-mentioned processing in the response unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the response unit can input the past response history into the generation AI, and the generation AI can adjust the response algorithm.

[0048] When responding, the response unit can adjust the response content based on the caller's attribute information. For example, if the caller is a relative, the generation AI in the response unit can provide a friendly response. Furthermore, if the caller is a sales call, the response unit can also provide a concise response and end the call early. Furthermore, if the caller is an emergency contact, the generation AI can respond quickly and immediately convey important information. This allows for a more appropriate response by customizing the response content based on the caller's attribute information. Some or all of the above-described processing in the response unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the response unit can input the caller's attribute information into the generation AI, which can then generate an appropriate response.

[0049] When responding, the response unit can adjust the content of the response based on the geographical location information of the caller. For example, if the caller is calling from a distant location, the generation AI of the response unit can respond according to the distance. Furthermore, if the caller is calling from a nearby location, the response unit can also provide a friendly response. Furthermore, if the caller is calling from overseas, the generation AI can also provide an international response. This allows for a more appropriate response by adjusting the content of the response based on the geographical location information of the caller. Some or all of the above-described processing in the response unit may be performed using, or without, the generation AI. For example, the response unit can input the geographical location information of the caller into the generation AI, which can then generate an appropriate response.

[0050] When responding, the response unit can generate the response content by referring to literature related to the sender. For example, if the sender is related to a specific industry, the generation AI can generate the response content by referring to related literature. Also, if the sender is related to a specific topic, the response unit can generate the response content by referring to related literature. Also, if the sender is related to a specific problem, the generation AI can generate the response content by referring to related literature. This enables a more appropriate response by generating the response content based on the sender's related literature. Some or all of the above-mentioned processing in the response unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the response unit can input the sender's related literature into the generation AI, which can generate an appropriate response.

[0051] When responding, the response unit can determine the content of the response taking into account the market value of the sender. For example, if the sender has a high market value, the generation AI can provide a polite response. Also, if the sender has a low market value, the response unit can cause the generation AI to provide a concise response. The response unit can also analyze the market value of the sender, and the generation AI can determine the optimal response content. This enables a more appropriate response by determining the response content based on the market value of the sender. Some or all of the above-mentioned processing in the response unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the response unit can input the market value of the sender into the generation AI, and the generation AI can generate an appropriate response.

[0052] The forwarding unit can analyze the content of the call in real time at the time of forwarding and adjust the level of detail of the summary. For example, if the content of the call is urgent, the generation AI of the forwarding unit can provide a concise summary that focuses on the main points. Furthermore, if the content of the call is a general inquiry, the generation AI of the forwarding unit can also provide a summary with normal detail. Furthermore, if the content of the call is sales, the generation AI of the forwarding unit can also provide a concise summary. In this way, by analyzing the content of the call in real time, important information can be quickly summarized and forwarded. Some or all of the above-mentioned processing in the forwarding unit may be performed using, or without, the generation AI. For example, the forwarding unit can input the content of the call into the generation AI, which can analyze it in real time and adjust the level of detail of the summary.

[0053] The transfer unit can adjust the summarization algorithm by referring to past transfer history when transferring. For example, the transfer unit allows the generation AI to adjust the summarization algorithm based on the past transfer history. The transfer unit can also extract specific patterns from the past transfer history, allowing the generation AI to optimize the summarization algorithm. The transfer unit can also analyze the past transfer history, allowing the generation AI to set optimal summarization criteria. This allows the summarization algorithm to be optimized by referring to the past transfer history, enabling more appropriate summarization. Some or all of the above-mentioned processing in the transfer unit may be performed using, or without, the generation AI. For example, the transfer unit can input the past transfer history into the generation AI, allowing the generation AI to adjust the summarization algorithm.

[0054] The forwarding unit can adjust the summary content based on the caller's attribute information when forwarding the call. For example, if the caller is a relative, the generation AI can provide a friendly summary. Furthermore, if the caller is a sales call, the forwarding unit can also provide a concise summary and end the call early. Furthermore, if the caller is an emergency contact, the generation AI can respond quickly and immediately convey important information. This allows for more appropriate summaries by customizing the summary content based on the caller's attribute information. Some or all of the above-described processing in the forwarding unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the forwarding unit can input the caller's attribute information into the generation AI, which can then generate an appropriate summary.

[0055] The forwarding unit can adjust the summary content based on the geographical location information of the caller when forwarding. For example, if the caller is calling from a distant location, the forwarding unit causes the generation AI to provide a summary according to the distance. Furthermore, if the caller is calling from a nearby location, the forwarding unit can cause the generation AI to provide a familiar summary. Furthermore, if the caller is calling from overseas, the forwarding unit can cause the generation AI to provide an international summary. This allows for a more appropriate summary by adjusting the summary content based on the geographical location information of the caller. Some or all of the above-described processing in the forwarding unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the forwarding unit can input the geographical location information of the caller into the generation AI, which can then generate an appropriate summary.

[0056] When transferring, the transfer unit can generate summary content by referring to related literature of the source. For example, if the source is related to a specific industry, the transfer unit can cause the generation AI to generate summary content by referring to related literature. Furthermore, if the source is related to a specific topic, the transfer unit can also cause the generation AI to generate summary content by referring to related literature. Furthermore, if the source is related to a specific problem, the transfer unit can also cause the generation AI to generate summary content by referring to related literature. This enables a more appropriate summary to be generated by generating summary content based on the source's related literature. Some or all of the above-mentioned processing in the transfer unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the transfer unit can input the source's related literature into the generation AI, which can then generate an appropriate summary.

[0057] When forwarding, the forwarding unit can determine the summary content taking into account the market value of the sender. For example, if the sender has a high market value, the forwarding unit can cause the generation AI to provide a detailed summary. Also, if the sender has a low market value, the forwarding unit can cause the generation AI to provide a concise summary. The forwarding unit can also analyze the market value of the sender, and the generation AI can determine the optimal summary content. This enables a more appropriate summary by determining the summary content based on the market value of the sender. Some or all of the above-mentioned processing in the forwarding unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the forwarding unit can input the market value of the sender into the generation AI, and the generation AI can generate an appropriate summary.

[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 telephone operator system may further include a voice recognition unit. The voice recognition unit can transcribe the contents of a telephone call in real time and save it as text data. For example, the voice recognition unit may automatically transcribe the contents of a telephone call and save it for later review. The voice recognition unit may also detect specific keywords and highlight important information. Furthermore, the voice recognition unit supports multiple languages ​​and can automatically translate telephone calls in different languages. This allows the contents of a telephone call to be accurately recorded and reviewed later, preventing important information from being overlooked.

[0060] The call handling operator system may further include a learning unit. The learning unit may analyze past call response data and continuously improve the response algorithm. For example, the learning unit may analyze what responses were most effective based on past call response data. The learning unit may also collect user feedback and adjust the response algorithm. Furthermore, the learning unit may detect new trends and patterns and reflect them in the response algorithm. This allows the call handling operator system to always provide optimal responses based on the latest information.

[0061] The telephone operator system may further include a notification unit. The notification unit can send a real-time notification to the user when an important call comes in. For example, the notification unit can send a push notification to the user's smartphone when an important contact calls. The notification unit can also send a notification to the user's email address when an emergency call comes in. Furthermore, the notification unit can link with the user's calendar and automatically add important calls to the calendar. This allows the user to respond quickly without missing important calls.

[0062] The telephone operator system can further include an analysis unit. The analysis unit can perform detailed analysis of the contents of calls and provide business intelligence. For example, the analysis unit can analyze the contents of calls to identify customer needs and trends. The analysis unit can also evaluate customer satisfaction based on the contents of calls. Furthermore, the analysis unit can analyze the contents of calls and suggest improvements to marketing strategies. This allows the telephone operator system to support business decision-making and improve competitiveness.

[0063] The telephone operator system may further include a security unit. The security unit may encrypt telephone content to protect privacy. For example, the security unit may encrypt telephone content in real time to prevent eavesdropping by third parties. The security unit may also use a strong encryption algorithm when storing telephone content. Furthermore, the security unit may manage user authentication information to prevent unauthorized access. This allows the telephone operator system to protect user privacy and achieve secure communications.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The receiver uses generative AI to answer the call. The receiver uses natural language processing technology to understand the content of the call and respond appropriately. For example, when a call comes in, it can respond with something like, "Hello, is this Mr. / Ms. X?" It can also analyze the content of the call and determine whether it is from an important contact. Step 2: The judgment unit uses the generation AI to determine whether a response is necessary based on the content of the call received by the receiving unit. The judgment unit implements a response necessity judgment algorithm and determines whether the content of the call is a sales call. For example, if it is a sales call, it can determine that "this call does not require a response" and automatically end the response. It can also determine that a response is necessary if the call is from an important contact. Step 3: The response unit uses the generation AI to automatically respond when the judgment unit determines that a response is necessary. The response unit uses natural language processing technology to automatically respond. For example, it can respond with something like, "Mr. / Ms. X is not in the office right now. Would you like to leave a message?" It can also respond appropriately based on the content of the call. Step 4: The forwarding unit uses the generation AI to summarize the response from the answering unit and forward the necessary information. The forwarding unit summarizes the contents of the call and forwards important information. For example, it can summarize the call as "This is a call from Mr. / Ms. X, and he / she wants to confirm tomorrow's schedule," and forward the necessary information.

[0066] (Example 2) A telephone operator system according to an embodiment of the present invention uses a generation AI to answer only those calls that are truly necessary among calls to a landline phone. In the telephone operator system, the generation AI uses natural language processing to answer calls, implements a response necessity determination algorithm to determine whether a response is necessary based on the content of the call, and, if necessary, automatically answers the call, summarizes the call, and forwards the necessary information. For example, in a telephone operator system, the generation AI uses natural language processing to answer calls. The generation AI understands the content of the call and provides an appropriate response. Next, the generation AI uses a response necessity determination algorithm to determine whether a response is necessary based on the content of the call. For example, if the call is a sales call, the generation AI determines that "this call does not require a response" and automatically ends the response. Furthermore, the generation AI automatically responds using natural language processing. For example, if the call is from an important contact, the generation AI responds by saying, "Mr. / Ms. X is not available right now. Would you like to leave a message?" Finally, the generation AI summarizes the content of the call and forwards the necessary information. For example, if a call comes from an important contact, the generation AI will summarize the call, saying something like, "This is a call from Mr. / Ms. X, and he / she wants to confirm tomorrow's schedule," and forward the necessary information. This allows the telephone operator system to automatically filter out sales calls coming to landlines and handle only those calls that are truly necessary. This allows the telephone operator system to reduce the burden of answering telephone calls within the home and achieve efficient communication. For example, by having the generation AI understand the content of the call and respond appropriately, important calls can be handled without being overlooked. Furthermore, by automatically filtering sales calls, time is no longer wasted on unnecessary calls. Furthermore, by having the generation AI summarize the content of the call and forward the necessary information, important information can be conveyed quickly and accurately.

[0067] A telephone operator system according to an embodiment includes a call receiving unit, a determination unit, a response unit, and a transfer unit. The call receiving unit uses a generation AI to answer a call. The call receiving unit understands the content of the call and provides an appropriate response using, for example, natural language processing technology. For example, when a call comes in, the call receiving unit can respond with, for example, "Hello, is this Mr. / Ms. X?" The call receiving unit can also analyze the content of the call and determine whether it is a call from an important contact. The determination unit uses a generation AI to determine whether a call needs to be responded to based on the content of the call received by the call receiving unit. The determination unit, for example, implements a response need determination algorithm to determine whether the call is a sales call. For example, if the call is a sales call, the determination unit can determine that "this call does not require a response" and automatically end the response. The determination unit can also determine that a call from an important contact requires a response. The response unit uses a generation AI to automatically respond when the determination unit determines that a response is required. The response unit automatically responds using, for example, natural language processing technology. For example, the answering unit can respond with, "Mr. / Ms. X is not in the office right now. Would you like to leave a message?" The answering unit can also provide an appropriate response based on the content of the call. The forwarding unit uses a generation AI to summarize the content of the response from the answering unit and forward the necessary information. The forwarding unit can summarize the content of the call and forward important information. For example, the forwarding unit can summarize the content of the call and forward important information. For example, the forwarding unit can summarize the content of the call and forward important information. For example, the forwarding unit can summarize the content of the call and forward important information. As a result, the call reception operator system according to the embodiment can automatically filter sales calls coming into a landline phone and handle only those calls that are truly necessary. For example, the call receiving unit can understand the content of the call and respond appropriately, allowing important calls to be handled without missing them. Furthermore, the judgment unit can automatically filter sales calls, eliminating the need for time wasted on unnecessary calls. Furthermore, the forwarding unit can summarize the content of the call and forward the necessary information, thereby quickly and accurately conveying important information.

[0068] The power receiving unit can answer calls using natural language processing. The power receiving unit, for example, uses natural language processing technology to understand the content of the call and provide an appropriate response. For example, when a call comes in, the power receiving unit can respond with, "Hello, is this Mr. / Ms. X?" The power receiving unit can also analyze the content of the call and determine whether it is a call from an important contact. For example, the power receiving unit can analyze the content of the call and, if it is a call from an important contact, respond with, "Mr. / Ms. X is not in the office right now. Would you like to leave a message?" This allows the generation AI to use natural language processing to understand the content of the call and provide an appropriate response. Some or all of the above-mentioned processing in the power receiving unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the power receiving unit can input the content of the call into the generation AI, which can then generate an appropriate response.

[0069] The determination unit implements a response necessity determination algorithm and can determine whether a response is necessary based on the content of the call. The determination unit, for example, implements a response necessity determination algorithm and determines whether the content of the call is a sales call. For example, if the call is a sales call, the determination unit can determine that "this call does not require a response" and automatically end the response. The determination unit can also determine that a response is necessary if the call is from an important contact. For example, if the call is from an important contact, the determination unit can determine that "this is a call from Mr. / Ms. X. Your response is necessary." This allows the generation AI to use the response necessity determination algorithm to appropriately determine whether a response is necessary based on the content of the call. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the determination unit can input the content of the call into the generation AI, and the generation AI can determine whether a response is necessary.

[0070] The response unit can automatically respond using natural language processing. The response unit automatically responds using, for example, natural language processing technology. For example, the response unit can respond, "Mr. / Ms. X is not in the office right now. Would you like to leave a message?" The response unit can also provide an appropriate response based on the content of the call. For example, if the call is from an important contact, the response unit can respond, "Mr. / Ms. X is not in the office right now. Would you like to leave a message?" This allows the generation AI to use natural language processing to provide an appropriate automatic response based on the content of the call. Some or all of the above-mentioned processing in the response unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the response unit can input the content of the call into the generation AI, which can then generate an appropriate response.

[0071] The forwarding unit can summarize the contents of the phone call and forward the information. For example, the forwarding unit summarizes the contents of the phone call and forwards important information. For example, the forwarding unit can generate a summary such as, "This is a call from Mr. / Ms. X, and he wants to confirm tomorrow's schedule," and forward the necessary information. The forwarding unit can also generate an appropriate summary based on the contents of the phone call. For example, if the call is from an important contact, the forwarding unit can generate a summary such as, "This is a call from Mr. / Ms. X, and he wants to confirm tomorrow's schedule," and forward the necessary information. In this way, important information can be efficiently conveyed by the generation AI summarizing the contents of the phone call and forwarding the necessary information. Some or all of the above-described processing in the forwarding unit may be performed using, or without, the generation AI. For example, the forwarding unit can input the contents of the phone call into the generation AI, which then generates a summary and forwards the necessary information.

[0072] The call receiving unit can estimate the user's emotions and adjust the tone and language of the call response based on the estimated user's emotions. For example, if the user is angry, the call receiving unit's generation AI can respond in a calm tone and use calm language. If the user is sad, the call receiving unit's generation AI can respond in a gentle tone and use comforting language. If the user is happy, the call receiving unit's generation AI can respond in a bright tone and use empathetic language. This enables better communication by responding with an appropriate tone and language according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the call receiving unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the call receiving unit can input the user's emotions into the generation AI, which can then generate an appropriate tone and language.

[0073] When receiving a call, the receiving unit can analyze the caller information and change the response method. For example, if the caller is a relative, the generation AI in the receiving unit can respond politely. Also, if the caller is a sales call, the generation AI in the receiving unit can respond briefly and end the call early. Also, if the caller is an emergency contact, the generation AI can respond quickly and immediately convey important information. This allows important calls to be handled appropriately by changing the response method based on the caller information. Some or all of the above-mentioned processing in the receiving unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the receiving unit can input caller information into the generation AI, which can then generate an appropriate response method.

[0074] When receiving a call, the power receiving unit can optimize the response to a call from a specific caller by referring to past call history. For example, in the power receiving unit, the generation AI can provide a friendly response to a call from a caller with which the power receiving unit has frequently been in contact in the past. In addition, in the power receiving unit, the generation AI can provide a careful response to a call from a caller with which the power receiving unit has had trouble in the past. In addition, in the power receiving unit, the generation AI can quickly respond to a call from a caller with which the power receiving unit has received important contact in the past. In this way, by referring to the past call history, an optimal response can be provided to a call from a specific caller. Some or all of the above-mentioned processing in the power receiving unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the power receiving unit can input past call history into the generation AI, which can then generate an optimal response.

[0075] When receiving a call, the power receiving unit can adjust the response according to the language or dialect of the caller. For example, if the caller speaks English, the power receiving unit's generation AI can respond in English. Also, if the caller speaks Kansai dialect, the power receiving unit's generation AI can respond in Kansai dialect. Also, if the caller speaks French, the power receiving unit's generation AI can respond in French. This allows for more natural communication by customizing the response according to the language or dialect of the caller. Some or all of the above-mentioned processing in the power receiving unit may be performed using, or without, the generation AI. For example, the power receiving unit can input the language or dialect of the caller into the generation AI, which can then generate an appropriate response.

[0076] The power receiving unit can estimate the user's emotions and determine the order of responses based on the estimated user emotions. For example, if the user expresses an urgent emotion, the power receiving unit assigns the generation AI the highest priority to respond. Furthermore, if the user is relaxed, the power receiving unit can assign the generation AI the normal priority to respond. Furthermore, if the user is feeling anxious, the power receiving unit can assign the generation AI the quickest response and use reassuring words. This allows for a prompt response to urgent calls by determining the priority of responses based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the power receiving unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the power receiving unit can input the user's emotions into the generation AI, which can then determine the priority of responses.

[0077] When receiving power, the power receiving unit can adjust the response content based on the geographical location information of the caller. For example, if the caller is far away, the power receiving unit's generation AI can respond according to the distance. Furthermore, if the caller is nearby, the power receiving unit's generation AI can also provide a friendly response. Furthermore, if the caller is overseas, the power receiving unit's generation AI can also provide an international response. This allows for a more appropriate response by adjusting the response content based on the geographical location information of the caller. Some or all of the above-described processing in the power receiving unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the power receiving unit can input the geographical location information of the caller into the generation AI, which can then generate an appropriate response.

[0078] When receiving a call, the receiving unit can analyze the social media activity of the caller and reflect related information in the response. For example, the receiving unit can have the generation AI provide topics based on the content the caller has recently posted on social media. The receiving unit can also have the generation AI customize the response based on the location where the caller checked in on social media. The receiving unit can also have the generation AI adjust the response taking into account the caller's social media friendships. This enables a more personalized response by customizing the response based on the caller's social media activity. Some or all of the above-mentioned processing in the receiving unit can be performed using, or without, the generation AI. For example, the receiving unit can input the caller's social media activity into the generation AI, which can then generate an appropriate response.

[0079] When receiving power, the power receiving unit can customize the response method by reflecting the user's past feedback. For example, the power receiving unit prioritizes response methods for which the user has given favorable feedback in the past. The power receiving unit can also avoid response methods for which the user has expressed dissatisfaction in the past. The power receiving unit can also have a generation AI select the optimal response method based on the user's past feedback. This allows the response method to be customized based on the user's past feedback, making it possible to provide a more satisfying response. Some or all of the above-mentioned processing in the power receiving unit may be performed using, or without, the generation AI. For example, the power receiving unit can input the user's past feedback into the generation AI, which then generates an appropriate response method.

[0080] The determination unit can estimate the user's emotions and adjust the criteria for determining whether or not to take action based on the estimated user emotions. For example, if the user expresses an urgent emotion, the determination unit causes the generation AI to relax the criteria for determining whether or not to take action. Furthermore, if the user is relaxed, the determination unit can also cause the generation AI to apply normal criteria. Furthermore, if the user is feeling anxious, the determination unit can also cause the generation AI to quickly determine whether or not to take action. This enables a more appropriate response by adjusting the criteria for determining whether or not to take action based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the determination unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the determination unit can input the user's emotions into the generation AI, and the generation AI can adjust the criteria for determining whether or not to take action.

[0081] When making a judgment, the judgment unit can analyze the content of the call in real time and determine whether a response is necessary. For example, if the content of the call is urgent, the judgment unit allows the generation AI to immediately determine whether a response is necessary. Furthermore, if the content of the call is a general inquiry, the judgment unit can also allow the generation AI to determine whether a normal response is necessary. Furthermore, if the content of the call is sales, the judgment unit can also allow the generation AI to determine whether a response is unnecessary. In this way, by analyzing the content of the call in real time, it is possible to quickly determine whether a response is necessary for important calls. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the content of the call into the generation AI, which can analyze it in real time and determine whether a response is necessary.

[0082] When making a judgment, the judgment unit can adjust the judgment algorithm by referring to past judgment history. In the judgment unit, for example, the generation AI adjusts the judgment algorithm based on the past judgment history. The judgment unit can also extract specific patterns from the past judgment history, and the generation AI can optimize the judgment algorithm. The judgment unit can also analyze the past judgment history, and the generation AI can set optimal judgment criteria. By referring to the past judgment history, the judgment algorithm can be optimized, making it possible to more appropriately determine whether or not a response is necessary. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the past judgment history into the generation AI, and the generation AI can adjust the judgment algorithm.

[0083] When making a judgment, the judgment unit can determine whether a response is necessary based on the attribute information of the caller. For example, if the caller is a relative, the judgment unit causes the generation AI to determine that a response is necessary. The judgment unit can also cause the generation AI to determine that a response is unnecessary if the caller is a sales call. The judgment unit can also cause the generation AI to determine that a response is necessary quickly if the caller is an emergency contact. This enables a more appropriate response by determining whether a response is necessary based on the attribute information of the caller. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the attribute information of the caller into the generation AI, and the generation AI can determine whether a response is necessary.

[0084] The determination unit can estimate the user's emotions and determine the order of whether or not to respond based on the estimated user emotions. For example, when the user expresses an urgent emotion, the determination unit allows the generation AI to determine whether or not to respond with the highest priority. Furthermore, when the user is relaxed, the determination unit can also allow the generation AI to determine whether or not to respond with normal priority. Furthermore, when the user is feeling anxious, the determination unit can also allow the generation AI to quickly determine whether or not to respond. This allows for a prompt response to highly urgent calls by determining the priority of whether or not to respond based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the determination unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the determination unit can input the user's emotions into the generation AI, and the generation AI can determine the order of whether or not to respond.

[0085] When making a judgment, the judgment unit can determine whether or not a response is necessary based on the geographical location information of the caller. For example, if the caller is calling from a distant location, the judgment unit causes the generation AI to determine whether or not a response is necessary. Furthermore, if the caller is calling from a nearby location, the judgment unit can also cause the generation AI to determine whether or not a normal response is necessary. Furthermore, if the caller is calling from overseas, the judgment unit can cause the generation AI to determine whether or not an international response is necessary. This enables a more appropriate response by determining whether or not a response is necessary based on the geographical location information of the caller. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the geographical location information of the caller into the generation AI, and the generation AI can determine whether or not a response is necessary.

[0086] When making a judgment, the judgment unit can refer to the literature of the sender to determine whether or not a response is necessary. For example, if the sender is related to a specific industry, the judgment unit can have the generation AI refer to related literature to determine whether or not a response is necessary. Furthermore, if the sender is related to a specific topic, the judgment unit can have the generation AI refer to related literature to determine whether or not a response is necessary. Furthermore, if the sender is related to a specific issue, the judgment unit can have the generation AI refer to related literature to determine whether or not a response is necessary. This enables a more appropriate response by determining whether or not a response is necessary based on the literature related to the sender. Some or all of the above-mentioned processing in the judgment unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the judgment unit can input the literature related to the sender into the generation AI, and the generation AI can determine whether or not a response is necessary.

[0087] When making a judgment, the judgment unit can determine whether or not to take action by taking into account the market value of the sender. For example, if the sender has a high market value, the judgment unit determines that the generation AI needs to take action. Furthermore, if the sender has a low market value, the judgment unit can also determine that the generation AI does not need to take action. Furthermore, the judgment unit can analyze the market value of the sender and have the generation AI determine the optimal need for action. This enables a more appropriate response by determining whether or not to take action based on the market value of the sender. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the market value of the sender into the generation AI, and the generation AI can determine whether or not to take action.

[0088] The response unit can estimate the user's emotions and adjust the tone and wording of the automated response based on the estimated user's emotions. For example, if the user is angry, the generation AI can respond in a calm tone and use calm language. If the user is sad, the response unit can respond in a gentle tone and use comforting language. If the user is happy, the response unit can respond in a bright tone and use empathetic language. This enables better communication by responding with an appropriate tone and wording according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the response unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the response unit can input the user's emotions into the generation AI, which can then generate an appropriate tone and wording.

[0089] When answering a call, the response unit can analyze the content of the call in real time and generate a response. For example, if the content of the call is urgent, the generation AI of the response unit can respond quickly. Furthermore, if the content of the call is a general inquiry, the generation AI of the response unit can generate an appropriate response. Furthermore, if the content of the call is sales, the generation AI of the response unit can generate a concise response. In this way, an appropriate response can be generated by analyzing the content of the call in real time. Some or all of the above-mentioned processing in the response unit may be performed using, or without, the generation AI. For example, the response unit can input the content of the call into the generation AI, which can analyze it in real time and generate an appropriate response.

[0090] When responding, the response unit can adjust the response algorithm by referring to past response history. In the response unit, for example, the generation AI adjusts the response algorithm based on the past response history. The response unit can also extract specific patterns from the past response history, and the generation AI can optimize the response algorithm. The response unit can also analyze the past response history, and the generation AI can set optimal response criteria. By referring to the past response history, the response algorithm can be optimized, enabling a more appropriate response. Some or all of the above-mentioned processing in the response unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the response unit can input the past response history into the generation AI, and the generation AI can adjust the response algorithm.

[0091] When responding, the response unit can adjust the response content based on the caller's attribute information. For example, if the caller is a relative, the generation AI in the response unit can provide a friendly response. Furthermore, if the caller is a sales call, the response unit can also provide a concise response and end the call early. Furthermore, if the caller is an emergency contact, the generation AI can respond quickly and immediately convey important information. This allows for a more appropriate response by customizing the response content based on the caller's attribute information. Some or all of the above-described processing in the response unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the response unit can input the caller's attribute information into the generation AI, which can then generate an appropriate response.

[0092] The response unit can estimate the user's emotions and determine the order of responses based on the estimated user emotions. For example, if the user expresses an urgent emotion, the response unit assigns the highest priority to the generation AI. Alternatively, if the user is relaxed, the response unit can assign the generation AI a normal priority. Alternatively, if the user is feeling anxious, the response unit can assign the generation AI a quick response using reassuring words. This allows for a prompt response to urgent calls by determining the priority of responses based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the response unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the response unit can input the user's emotions into the generation AI, which then determines the order of responses.

[0093] When responding, the response unit can adjust the content of the response based on the geographical location information of the caller. For example, if the caller is calling from a distant location, the generation AI of the response unit can respond according to the distance. Furthermore, if the caller is calling from a nearby location, the response unit can also provide a friendly response. Furthermore, if the caller is calling from overseas, the generation AI can also provide an international response. This allows for a more appropriate response by adjusting the content of the response based on the geographical location information of the caller. Some or all of the above-described processing in the response unit may be performed using, or without, the generation AI. For example, the response unit can input the geographical location information of the caller into the generation AI, which can then generate an appropriate response.

[0094] When responding, the response unit can generate the response content by referring to literature related to the sender. For example, if the sender is related to a specific industry, the generation AI can generate the response content by referring to related literature. Also, if the sender is related to a specific topic, the response unit can generate the response content by referring to related literature. Also, if the sender is related to a specific problem, the generation AI can generate the response content by referring to related literature. This enables a more appropriate response by generating the response content based on the sender's related literature. Some or all of the above-mentioned processing in the response unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the response unit can input the sender's related literature into the generation AI, which can generate an appropriate response.

[0095] When responding, the response unit can determine the content of the response taking into account the market value of the sender. For example, if the sender has a high market value, the generation AI can provide a polite response. Also, if the sender has a low market value, the response unit can cause the generation AI to provide a concise response. The response unit can also analyze the market value of the sender, and the generation AI can determine the optimal response content. This enables a more appropriate response by determining the response content based on the market value of the sender. Some or all of the above-mentioned processing in the response unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the response unit can input the market value of the sender into the generation AI, and the generation AI can generate an appropriate response.

[0096] The transfer unit can estimate the user's emotions and adjust the summarization method based on the estimated user emotions. For example, if the user expresses an urgent emotion, the transfer unit can cause the generation AI to provide a concise and to-the-point summary. Furthermore, if the user is relaxed, the transfer unit can cause the generation AI to provide a detailed summary. Furthermore, if the user is feeling anxious, the transfer unit can cause the generation AI to provide a summary using reassuring expressions. This allows for more appropriate summarization by adjusting the summary expression method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the transfer unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the transfer unit can input the user's emotions into the generation AI, and the generation AI can adjust the summarization method.

[0097] The forwarding unit can analyze the content of the call in real time at the time of forwarding and adjust the level of detail of the summary. For example, if the content of the call is urgent, the generation AI of the forwarding unit can provide a concise summary that focuses on the main points. Furthermore, if the content of the call is a general inquiry, the generation AI of the forwarding unit can also provide a summary with normal detail. Furthermore, if the content of the call is sales, the generation AI of the forwarding unit can also provide a concise summary. In this way, by analyzing the content of the call in real time, important information can be quickly summarized and forwarded. Some or all of the above-mentioned processing in the forwarding unit may be performed using, or without, the generation AI. For example, the forwarding unit can input the content of the call into the generation AI, which can analyze it in real time and adjust the level of detail of the summary.

[0098] The transfer unit can adjust the summarization algorithm by referring to past transfer history when transferring. For example, the transfer unit allows the generation AI to adjust the summarization algorithm based on the past transfer history. The transfer unit can also extract specific patterns from the past transfer history, allowing the generation AI to optimize the summarization algorithm. The transfer unit can also analyze the past transfer history, allowing the generation AI to set optimal summarization criteria. This allows the summarization algorithm to be optimized by referring to the past transfer history, enabling more appropriate summarization. Some or all of the above-mentioned processing in the transfer unit may be performed using, or without, the generation AI. For example, the transfer unit can input the past transfer history into the generation AI, allowing the generation AI to adjust the summarization algorithm.

[0099] The forwarding unit can adjust the summary content based on the caller's attribute information when forwarding the call. For example, if the caller is a relative, the generation AI can provide a friendly summary. Furthermore, if the caller is a sales call, the forwarding unit can also provide a concise summary and end the call early. Furthermore, if the caller is an emergency contact, the generation AI can respond quickly and immediately convey important information. This allows for more appropriate summaries by customizing the summary content based on the caller's attribute information. Some or all of the above-described processing in the forwarding unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the forwarding unit can input the caller's attribute information into the generation AI, which can then generate an appropriate summary.

[0100] The transfer unit can estimate the user's emotions and determine the order of summaries based on the estimated user emotions. For example, if the user expresses an urgent emotion, the transfer unit can cause the generation AI to perform summarization with the highest priority. Furthermore, if the user is relaxed, the transfer unit can cause the generation AI to perform summarization with normal priority. Furthermore, if the user is feeling anxious, the transfer unit can cause the generation AI to quickly perform summarization and use reassuring words. This allows for the rapid transfer of urgent information by determining the priority of summaries based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the transfer unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the transfer unit can input the user's emotions into the generation AI, and the generation AI can determine the order of summaries.

[0101] The forwarding unit can adjust the summary content based on the geographical location information of the caller when forwarding. For example, if the caller is calling from a distant location, the forwarding unit causes the generation AI to provide a summary according to the distance. Furthermore, if the caller is calling from a nearby location, the forwarding unit can cause the generation AI to provide a familiar summary. Furthermore, if the caller is calling from overseas, the forwarding unit can cause the generation AI to provide an international summary. This allows for a more appropriate summary by adjusting the summary content based on the geographical location information of the caller. Some or all of the above-described processing in the forwarding unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the forwarding unit can input the geographical location information of the caller into the generation AI, which can then generate an appropriate summary.

[0102] When transferring, the transfer unit can generate summary content by referring to related literature of the source. For example, if the source is related to a specific industry, the transfer unit can cause the generation AI to generate summary content by referring to related literature. Furthermore, if the source is related to a specific topic, the transfer unit can also cause the generation AI to generate summary content by referring to related literature. Furthermore, if the source is related to a specific problem, the transfer unit can also cause the generation AI to generate summary content by referring to related literature. This enables a more appropriate summary to be generated by generating summary content based on the source's related literature. Some or all of the above-mentioned processing in the transfer unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the transfer unit can input the source's related literature into the generation AI, which can then generate an appropriate summary.

[0103] When forwarding, the forwarding unit can determine the summary content taking into account the market value of the sender. For example, if the sender has a high market value, the forwarding unit can cause the generation AI to provide a detailed summary. Also, if the sender has a low market value, the forwarding unit can cause the generation AI to provide a concise summary. The forwarding unit can also analyze the market value of the sender, and the generation AI can determine the optimal summary content. This enables a more appropriate summary by determining the summary content based on the market value of the sender. Some or all of the above-mentioned processing in the forwarding unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the forwarding unit can input the market value of the sender into the generation AI, and the generation AI can generate an appropriate summary. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned power receiving unit, determination unit, response unit, and transfer unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the power receiving unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the response unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the transfer unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned power receiving unit, determination unit, response unit, and transfer unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the power receiving unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the response unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the transfer unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned power receiving unit, determination unit, response unit, and transfer unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the power receiving unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the response unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the transfer unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned power receiving unit, determination unit, response unit, and transfer unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the power receiving unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the response unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the transfer unit is realized by the specific processing unit 290 of the data processing device 12.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The telephone operator system may further include a voice recognition unit. The voice recognition unit can transcribe the contents of a telephone call in real time and save it as text data. For example, the voice recognition unit may automatically transcribe the contents of a telephone call and save it for later review. The voice recognition unit may also detect specific keywords and highlight important information. Furthermore, the voice recognition unit supports multiple languages ​​and can automatically translate telephone calls in different languages. This allows the contents of a telephone call to be accurately recorded and reviewed later, preventing important information from being overlooked.

[0106] The call handling operator system may further include a learning unit. The learning unit may analyze past call response data and continuously improve the response algorithm. For example, the learning unit may analyze what responses were most effective based on past call response data. The learning unit may also collect user feedback and adjust the response algorithm. Furthermore, the learning unit may detect new trends and patterns and reflect them in the response algorithm. This allows the call handling operator system to always provide optimal responses based on the latest information.

[0107] The telephone operator system may further include a notification unit. The notification unit can send a real-time notification to the user when an important call comes in. For example, the notification unit can send a push notification to the user's smartphone when an important contact calls. The notification unit can also send a notification to the user's email address when an emergency call comes in. Furthermore, the notification unit can link with the user's calendar and automatically add important calls to the calendar. This allows the user to respond quickly without missing important calls.

[0108] The telephone operator system can further include an analysis unit. The analysis unit can perform detailed analysis of the contents of calls and provide business intelligence. For example, the analysis unit can analyze the contents of calls to identify customer needs and trends. The analysis unit can also evaluate customer satisfaction based on the contents of calls. Furthermore, the analysis unit can analyze the contents of calls and suggest improvements to marketing strategies. This allows the telephone operator system to support business decision-making and improve competitiveness.

[0109] The telephone operator system may further include a security unit. The security unit may encrypt telephone content to protect privacy. For example, the security unit may encrypt telephone content in real time to prevent eavesdropping by third parties. The security unit may also use a strong encryption algorithm when storing telephone content. Furthermore, the security unit may manage user authentication information to prevent unauthorized access. This allows the telephone operator system to protect user privacy and achieve secure communications.

[0110] The telephone operator system can further include an emotion analysis unit. The emotion analysis unit can infer the user's emotions from the content of the telephone call and respond appropriately. For example, if the emotion analysis unit determines that the user is angry, the generation AI can respond in a calm tone and use calm language. If the user is sad, the emotion analysis unit can also determine that the generation AI can respond in a gentle tone and use comforting language. If the user is happy, the emotion analysis unit can also determine that the generation AI can respond in a bright tone and use empathetic language. This enables better communication by responding appropriately according to the user's emotions.

[0111] The call handling operator system may further include an emotion feedback unit. The emotion feedback unit may evaluate the user's emotion after answering the call and use the evaluation results to improve the system. For example, the emotion feedback unit may evaluate whether the user is satisfied with the call handling. If the user is dissatisfied, the emotion feedback unit may identify the cause and suggest improvements. Furthermore, the emotion feedback unit may adjust the response algorithm based on the user's feedback. This allows the call handling operator system to be continuously improved based on the user's emotion, enabling more satisfying responses.

[0112] The telephone operator system can further include an emotion prediction unit. The emotion prediction unit can predict a user's emotions based on past call data and prepare an appropriate response in advance. For example, the emotion prediction unit can analyze past call data and predict what emotions a specific user will show. The emotion prediction unit can also use the generation AI to prepare an appropriate response in advance based on the predicted emotions. Furthermore, the emotion prediction unit can adjust the tone and wording of the response based on the predicted emotions. This allows the telephone operator system to respond to the user's emotions in advance, enabling smoother communication.

[0113] The telephone operator system may further include an emotion history unit. The emotion history unit may record the user's emotions in past calls and use them in future responses. For example, the emotion history unit may record the user's emotions in past calls and refer to them for the next call. The emotion history unit may also track changes in the user's emotions and analyze long-term emotional trends. Furthermore, the emotion history unit may enable the generation AI to select the optimal response based on past emotion data. This allows the telephone operator system to provide more personalized responses based on the user's emotion history.

[0114] The telephone operator system can further include an emotion monitoring unit. The emotion monitoring unit can monitor the user's emotions in real time during a call and respond appropriately. For example, if the emotion monitoring unit detects anger during a call, the generation AI can instantly change the response tone and respond calmly. The emotion monitoring unit can also detect anxiety during a call, allowing the generation AI to use reassuring words. Furthermore, if the user detects joy during a call, the emotion monitoring unit can also detect empathy in the response. This allows the telephone operator system to respond to the user's emotions in real time during a call, achieving better communication.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The receiver uses generative AI to answer the call. The receiver uses natural language processing technology to understand the content of the call and respond appropriately. For example, when a call comes in, it can respond with something like, "Hello, is this Mr. / Ms. X?" It can also analyze the content of the call and determine whether it is from an important contact. Step 2: The judgment unit uses the generation AI to determine whether a response is necessary based on the content of the call received by the receiving unit. The judgment unit implements a response necessity judgment algorithm and determines whether the content of the call is a sales call. For example, if it is a sales call, it can determine that "this call does not require a response" and automatically end the response. It can also determine that a response is necessary if the call is from an important contact. Step 3: The response unit uses the generation AI to automatically respond when the judgment unit determines that a response is necessary. The response unit uses natural language processing technology to automatically respond. For example, it can respond with something like, "Mr. / Ms. X is not in the office right now. Would you like to leave a message?" It can also respond appropriately based on the content of the call. Step 4: The forwarding unit uses the generation AI to summarize the response from the answering unit and forward the necessary information. The forwarding unit summarizes the contents of the call and forwards important information. For example, it can summarize the call as "This is a call from Mr. / Ms. X, and he / she wants to confirm tomorrow's schedule," and forward the necessary information.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 receiving unit having a function of answering a call; a determination unit that determines whether or not a call needs to be answered based on the content of the call received by the call receiving unit; a response unit that automatically responds when the determination unit determines that a response is necessary; a transfer unit that summarizes the content of the response from the response unit and transfers the information; Equipped with A system characterized by:

2. The power receiving unit is Answering calls using natural language processing 2. The system of claim 1.

3. The determination unit Implement a response decision algorithm to determine whether a call needs to be responded to based on the content of the call.

2. The system of claim 1.

4. The response unit Automatically respond using natural language processing 2. The system of claim 1.

5. The transfer unit Summarize calls and transfer information 2. The system of claim 1.

6. The power receiving unit is Inferring user emotions and adjusting the tone and language of phone responses based on the inferred user emotions 2. The system of claim 1.

7. The power receiving unit is When receiving a call, analyze the caller's information and change the response method 2. The system of claim 1.

8. The power receiving unit is When receiving a call, refer to past call history to tailor your response to calls from specific callers.

2. The system of claim 1.

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