system

Abstract

The system according to the embodiment aims to accept only legitimate calls. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a ringing unit. The reception unit collects information about the caller, recipient, and requirements. The analysis unit analyzes the information collected by the reception unit and determines whether the call is legitimate. The ringing unit rings the phone only if the analysis unit determines that the call is legitimate.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to effectively filter out nuisance and fraudulent calls, and lacking a mechanism for accepting only legitimate calls.

[0005] The system according to the embodiment aims to accept only legitimate calls. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a ringing unit. The reception unit collects information about the caller, recipient, and requirements. The analysis unit analyzes the information collected by the reception unit and determines whether the call is legitimate. The ringing unit rings the phone only if the analysis unit determines that the call is legitimate. [Effects of the Invention]

[0007] The system according to the embodiment can only accept legitimate calls. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A telephone answering system according to an embodiment of the present invention uses AI as the primary point of contact for all calls. When a call is received, the AI ​​first answers the call and collects the caller, recipient, and requirements. Then, it analyzes the collected information to determine whether the call is legitimate. Only if the call is determined to be legitimate does the phone ring. This system protects the livelihoods of rural grandparents and blocks nuisance sales calls to businesses. As a result, a clean society is realized in which only truly necessary calls are made. For example, a reception unit is provided in which AI accepts calls. This reception unit collects the caller, recipient, and requirements. Next, an analysis unit is provided that analyzes the collected information. This analysis unit determines whether the call is legitimate based on the collected information. Finally, a ringing unit is provided that rings the phone only if the call is determined to be legitimate. This eliminates nuisance calls and allows only necessary calls to be made. The analysis unit converts the caller's information into text using voice recognition technology and analyzes the text to determine whether the call is legitimate. The ringing unit determines whether to ring the phone based on the analysis unit's determination. This allows the system to filter out nuisance calls and only make necessary calls. Furthermore, the analysis unit uses an algorithm that takes into account past call history and the reliability of the caller to make more accurate decisions. This allows the system to filter out nuisance calls and only make necessary calls.

[0029] A telephone answering system according to an embodiment includes a reception unit, an analysis unit, and a ringing unit. The reception unit collects information about the caller, the recipient, and the requirements of the call. For example, when a call is received, the reception unit asks for the caller's name, the recipient's name, and the requirements of the call. The reception unit can convert the caller's voice into text using speech recognition technology. For example, the reception unit can convert the caller's voice into text using deep learning-based speech recognition technology. The reception unit can also convert the caller's voice into text using HMM (Hidden Markov Model)-based speech recognition technology. The reception unit can also convert the caller's voice into text in real time and send it to the analysis unit. The analysis unit analyzes the information collected by the reception unit to determine whether the call is legitimate. For example, the analysis unit can analyze the collected information using natural language processing technology to understand the content of the call. The analysis unit can also determine whether the call is legitimate using an algorithm that takes into account past call history and the caller's credibility. For example, the analysis unit can refer to past call history to check whether the caller has made any nuisance calls in the past. The analysis unit can also verify the caller's authentication information to evaluate the caller's trustworthiness. The ringing unit rings the phone only if the analysis unit determines that the call is legitimate. For example, the ringing unit determines whether to ring the phone based on the analysis unit's determination. The ringing unit can also adjust the ring volume and ring pattern of the phone. For example, the ringing unit can adjust the ring volume and change the ring pattern depending on the caller's urgency. This allows the telephone answering system to filter out nuisance calls and only allow necessary calls. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use an AI model that uses collected information as input and determines whether the call is legitimate. This allows the telephone answering system to filter out nuisance calls and only allow necessary calls.

[0030] The analysis unit can convert the caller's information into text using speech recognition technology and analyze the text. The analysis unit can convert the caller's speech into text using, for example, deep learning-based speech recognition technology. For example, the analysis unit inputs the caller's speech into a deep learning model and outputs text data. The analysis unit can also convert the caller's speech into text using HMM (hidden Markov model)-based speech recognition technology. For example, the analysis unit inputs the caller's speech into an HMM model and outputs text data. The analysis unit can also convert the caller's speech into text in real time using speech recognition technology and analyze the text. For example, the analysis unit converts the caller's speech into text in real time and analyzes the text using natural language processing technology. This allows the analysis unit to accurately convert the caller's information into text and analyze it. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the caller's speech data into a generation AI and have the generation AI convert the speech data into text data.

[0031] The analysis unit can use an algorithm based on past call history and the caller's trustworthiness. For example, the analysis unit can refer to past call history to check whether the caller has made any nuisance calls in the past. For example, the analysis unit can search a past call history database to obtain the call history of the caller. The analysis unit can also check the caller's authentication information to evaluate the caller's trustworthiness. For example, the analysis unit can obtain the caller's authentication information from a database and evaluate its trustworthiness. The analysis unit can also determine whether a call is legitimate using an algorithm that takes into account the past call history and the caller's trustworthiness. For example, the analysis unit uses an algorithm that inputs past call history data and the caller's authentication information to determine whether a call is legitimate. This allows the analysis unit to more accurately determine whether a call is legitimate by considering the past call history and the caller's trustworthiness. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past call history data and the caller's authentication information to a generation AI and have the generation AI determine whether the call is legitimate.

[0032] The ringing unit can determine whether to ring the phone based on the judgment of the analysis unit. The ringing unit, for example, determines whether to ring the phone based on the judgment of the analysis unit. For example, the ringing unit receives a signal from the analysis unit and determines whether to ring the phone. The ringing unit can also adjust the ring volume and ring pattern of the phone. For example, the ringing unit can adjust the ring volume of the phone and change the ring pattern depending on the urgency of the caller. In this way, by determining whether to ring the phone based on the judgment of the analysis unit, the ringing unit can eliminate nuisance calls and allow only necessary calls to be made. Some or all of the above-mentioned processing in the ringing unit may be performed using, for example, AI, or may be performed without using AI. For example, the ringing unit can input a signal from the analysis unit to a generation AI and have the generation AI determine whether to ring the phone.

[0033] The reception unit can analyze the tone and speed of the caller's voice to determine the level of urgency and prioritize the response. For example, if the caller sounds nervous, the AI ​​in the reception unit determines the level of urgency as high and responds quickly. For example, the reception unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds nervous. Alternatively, if the caller sounds calm, the AI ​​can determine the level of urgency as low and provide a standard response. For example, the reception unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds calm. Alternatively, if the caller sounds rushed, the AI ​​can determine the level of urgency as medium and quickly confirm the requirements. For example, the reception unit can analyze the tone and speed of the caller's voice to determine whether the caller is rushed. This allows the reception unit to determine the level of urgency and provide an appropriate response by analyzing the tone and speed of the caller's voice. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the caller's voice tone and speed data into the generation AI and have the generation AI determine the level of urgency.

[0034] When receiving a call, the reception unit can customize the response method by referring to the caller's past call history. For example, if the caller has contacted the caller frequently in the past, the reception unit has the AI ​​respond in a friendly tone. For example, the reception unit searches a past call history database to obtain the caller's call history. The reception unit can also have the AI ​​respond with caution if the caller has made nuisance calls in the past. For example, the reception unit analyzes past call history data to determine whether the caller has made nuisance calls. The reception unit can also have the AI ​​provide a standard response when the caller is contacting the caller for the first time. For example, the reception unit provides a standard response if the caller does not have a call history. This allows the reception unit to customize the response method by referring to the caller's past call history, enabling a more appropriate response. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the caller's past call history data into the generation AI and have the generation AI customize the response method.

[0035] The reception unit can adjust the response method by taking into account the caller's geographical location information when receiving the call. For example, if the caller is calling from a distant location, the reception unit has the AI ​​respond politely. For example, the reception unit acquires the caller's geographical location information and determines whether the call is from a distant location. The reception unit can also have the AI ​​respond in a friendly manner if the caller is calling from a nearby location. For example, the reception unit acquires the caller's geographical location information and determines whether the call is from a nearby location. The reception unit can also have the AI ​​respond in multiple languages ​​if the caller is calling from overseas. For example, the reception unit acquires the caller's geographical location information and determines whether the call is from overseas. This allows the reception unit to provide a more appropriate response by taking the caller's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the caller's geographical location information data to the generation AI and have the generation AI adjust the response method.

[0036] The reception unit can analyze the caller's social media activity and collect related information when receiving the call. For example, if the caller reports an emergency on social media, the reception unit determines that the AI ​​has a high level of urgency. For example, the reception unit analyzes the caller's social media activity to determine whether the caller is reporting an emergency. The reception unit can also use the AI ​​to collect information about an event that the caller is announcing on social media. For example, the reception unit can analyze the caller's social media activity to determine whether the caller is announcing an event. The reception unit can also use the AI ​​to collect information related to a specific topic that the caller frequently posts on social media. For example, the reception unit can analyze the caller's social media activity to determine whether the caller frequently posts on a specific topic. This allows the reception unit to analyze the caller's social media activity to collect related information and provide a more appropriate response. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the caller's social media activity data into the generation AI and cause the generation AI to collect related information.

[0037] During analysis, the analysis unit can analyze the tone and speed of the caller's voice to determine the level of urgency and prioritize the analysis. For example, if the caller sounds nervous, the analysis unit determines that the AI ​​is urgency-high and prioritizes the analysis. For example, the analysis unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds nervous. Alternatively, if the caller sounds calm, the analysis unit can determine that the AI ​​is urgency-low and perform a normal analysis. For example, the analysis unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds calm. Alternatively, if the caller sounds rushed, the analysis unit can determine that the AI ​​is urgency-medium and perform a quick analysis. For example, the analysis unit can analyze the tone and speed of the caller's voice to determine whether the caller is rushed. This allows the analysis unit to determine the level of urgency and perform an appropriate analysis by analyzing the tone and speed of the caller's voice. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the caller's voice tone and speed data into the generation AI and have the generation AI determine the level of urgency.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the caller's past call history. For example, if the caller has frequently contacted the caller in the past, the analysis unit performs the analysis using the AI, taking into account that pattern. For example, the analysis unit searches a database of past call history to obtain the caller's call history. Furthermore, if the caller has made nuisance calls in the past, the analysis unit can perform an alert analysis using the AI ​​based on that information. For example, the analysis unit analyzes past call history data to determine whether the caller has made nuisance calls. Furthermore, if the caller is contacting the caller for the first time, the analysis unit can perform a standard analysis using the AI. For example, if the caller does not have a call history, the analysis unit performs a standard analysis. By referring to the caller's past call history, the analysis unit can improve the accuracy of the analysis and perform a more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI. For example, the analysis unit can input the caller's past call history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can adjust the analysis method by taking into account the caller's geographical location information. For example, if the caller is calling from a distant location, the analysis unit performs the analysis using an AI that takes into account the characteristics of the area. For example, the analysis unit acquires the caller's geographical location information and determines whether the call is from a distant location. In addition, if the caller is calling from a nearby location, the analysis unit can also perform the analysis using an AI that takes into account the characteristics of the area. For example, the analysis unit acquires the caller's geographical location information and determines whether the call is from a nearby location. In addition, if the caller is calling from overseas, the analysis unit can also perform the analysis using an AI that takes into account the characteristics of the area. For example, the analysis unit acquires the caller's geographical location information and determines whether the call is from overseas. This enables the analysis unit to perform a more appropriate analysis by taking into account the caller's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit can input the caller's geographical location information data to the generation AI and cause the generation AI to adjust the analysis method.

[0040] During the analysis, the analysis unit can analyze the sender's social media activity and use related information for the analysis. For example, if the sender reports an emergency on social media, the analysis unit uses the information for the analysis by the AI. For example, the analysis unit analyzes the sender's social media activity to determine whether the sender is reporting an emergency. Furthermore, if the sender announces an event on social media, the analysis unit can also use the information for the analysis by the AI. For example, the analysis unit analyzes the sender's social media activity to determine whether the sender is announcing an event. Furthermore, if the sender frequently posts about a specific topic on social media, the analysis unit can also use the information for the analysis by the AI. For example, the analysis unit analyzes the sender's social media activity to determine whether the sender frequently posts about a specific topic. By analyzing the sender's social media activity, the analysis unit can use related information for the analysis, enabling more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the sender's social media activity data into the generation AI and have the generation AI analyze the related information.

[0041] When ringing, the ringing unit can analyze the tone and speed of the caller's voice to determine the level of urgency and prioritize ringing. For example, if the caller sounds nervous, the AI ​​determines the level of urgency as high and rings the call first. For example, the ringing unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds nervous. Also, if the caller sounds calm, the AI ​​can determine the level of urgency as low and ring normally. For example, the ringing unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds calm. Also, if the caller sounds rushed, the AI ​​can determine the level of urgency as medium and ring quickly. For example, the ringing unit can analyze the tone and speed of the caller's voice to determine whether the caller is rushed. As a result, the ringing unit can determine the level of urgency and ring appropriately by analyzing the tone and speed of the caller's voice. Some or all of the above-described processing in the ringing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sound unit can input the caller's voice tone and speed data into the generation AI and have the generation AI determine the level of urgency.

[0042] When ringing, the ringing unit can customize the ringing method by referring to the caller's past call history. For example, if the caller has contacted the caller frequently in the past, the AI ​​can ring in a friendly tone. For example, the ringing unit can search a past call history database to obtain the caller's call history. Furthermore, if the caller has made nuisance calls in the past, the AI ​​can ring with caution. For example, the ringing unit can analyze past call history data to determine whether the caller has made nuisance calls. Furthermore, if the caller is contacting the caller for the first time, the AI ​​can ring in a standard manner. For example, if the caller does not have a call history, the ringing unit can ring in a standard manner. This allows the ringing unit to customize the ringing method by referring to the caller's past call history, enabling more appropriate ringing. Some or all of the above-described processing in the ringing unit may be performed using, or without, AI. For example, the ringing unit can input the caller's past call history data into the generation AI and have the generation AI customize the ringing method.

[0043] When ringing, the ringing unit can adjust the ringing method by taking into account the caller's geographical location information. For example, if the caller is calling from a distant location, the ringing unit uses AI to ring politely. For example, the ringing unit acquires the caller's geographical location information and determines whether the call is from a distant location. In addition, if the caller is calling from a nearby location, the ringing unit can also ring in a friendly manner. For example, the ringing unit acquires the caller's geographical location information and determines whether the call is from a nearby location. In addition, if the caller is calling from overseas, the ringing unit can ring in multiple languages. For example, the ringing unit acquires the caller's geographical location information and determines whether the call is from overseas. This allows the ringing unit to ring more appropriately by taking the caller's geographical location information into account. Some or all of the above-described processing in the ringing unit may be performed using AI, for example, or may be performed without using AI. For example, the ringing unit can input the caller's geographical location information data to the generation AI and cause the generation AI to adjust the ringing method.

[0044] When sounding, the sounding unit can analyze the caller's social media activity and use related information for sounding. For example, if the caller reports an emergency on social media, the AI ​​determines the level of urgency as high and prioritizes sounding. For example, the sounding unit analyzes the caller's social media activity and determines whether the caller is reporting an emergency. In addition, if the caller announces an event on social media, the AI ​​can use that information for sounding. For example, the sounding unit analyzes the caller's social media activity and determines whether the caller is announcing an event. In addition, if the caller frequently posts about a specific topic on social media, the AI ​​can use that information for sounding. For example, the sounding unit analyzes the caller's social media activity and determines whether the caller frequently posts about a specific topic. In this way, the sounding unit can analyze the caller's social media activity and use related information for sounding, thereby enabling more appropriate sounding. Some or all of the above-described processing in the sounding unit may be performed, for example, using AI, or may be performed without using AI. For example, the sound unit can input the caller's social media activity data into the generation AI and have the generation AI use the related information to sound the call.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The reception unit can analyze the tone and speed of the caller's voice to determine the level of urgency and prioritize the response. For example, if the caller sounds nervous, the AI ​​can determine the level of urgency and respond quickly. The reception unit can analyze the tone and speed of the caller's voice to determine whether they sound nervous. If the caller sounds calm, the AI ​​can determine the level of urgency as low and respond normally. The reception unit can analyze the tone and speed of the caller's voice to determine whether they sound calm. If the caller sounds rushed, the AI ​​can determine the level of urgency as medium and quickly confirm the requirements. The reception unit can analyze the tone and speed of the caller's voice to determine whether they are rushed. This allows the reception unit to determine the level of urgency and provide an appropriate response by analyzing the tone and speed of the caller's voice. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the caller's tone and speed of voice data into the generation AI and have the generation AI determine the level of urgency.

[0047] During analysis, the analysis unit can analyze the caller's tone and speed of voice to determine the level of urgency and prioritize the analysis. For example, if the caller sounds nervous, the AI ​​can determine the level of urgency and prioritize the analysis. The caller's tone and speed of voice can be analyzed to determine whether they are nervous. Alternatively, if the caller sounds calm, the AI ​​can determine the level of urgency as low and perform normal analysis. The caller's tone and speed of voice can be analyzed to determine whether they are calm. Alternatively, if the caller sounds rushed, the AI ​​can determine the level of urgency as medium and perform quick analysis. The caller's tone and speed of voice can be analyzed to determine whether they are rushed. This allows the analysis unit to determine the level of urgency and perform appropriate analysis by analyzing the caller's tone and speed of voice. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the caller's tone and speed of voice data to the generation AI and have the generation AI determine the level of urgency.

[0048] When ringing, the ringing unit can analyze the tone and speed of the caller's voice to determine the level of urgency and prioritize ringing. For example, if the caller sounds nervous, the AI ​​can determine the level of urgency and prioritize ringing. The caller's tone and speed can be analyzed to determine whether they are nervous. Alternatively, if the caller sounds calm, the AI ​​can determine the level of urgency as low and ring normally. The caller's tone and speed can be analyzed to determine whether they are calm. Alternatively, if the caller sounds rushed, the AI ​​can determine the level of urgency as medium and ring quickly. The caller's tone and speed can be analyzed to determine whether they are rushed. This allows the ringing unit to determine the level of urgency and ring appropriately by analyzing the caller's tone and speed. Some or all of the above-mentioned processing in the ringing unit may be performed using AI, for example, or without AI. For example, the ringing unit can input the caller's tone and speed data to the generation AI and have the generation AI determine the level of urgency.

[0049] When receiving a call, the reception unit can customize the response method by referring to the caller's past call history. For example, if the caller has contacted the caller frequently in the past, the AI ​​responds in a friendly tone. The AI ​​searches a past call history database to retrieve the caller's call history. Furthermore, if the caller has made nuisance calls in the past, the AI ​​can respond with caution. The AI ​​analyzes past call history data to determine whether the caller has made nuisance calls. Furthermore, if the caller is contacting the caller for the first time, the AI ​​can provide a standard response. If the caller does not have a call history, the AI ​​provides a standard response. This allows the reception unit to customize the response method by referring to the caller's past call history, enabling a more appropriate response. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the caller's past call history data into the generation AI and have the generation AI customize the response method.

[0050] During analysis, the analysis unit can refer to the caller's past call history to improve the accuracy of the analysis. For example, if the caller has contacted the caller frequently in the past, the AI ​​will take that pattern into account in the analysis. The AI ​​will search a past call history database to obtain the caller's call history. If the caller has made nuisance calls in the past, the AI ​​can also use that information to analyze with caution. The AI ​​will analyze past call history data to determine whether the caller has made nuisance calls. If the caller is contacting the caller for the first time, the AI ​​can also perform a standard analysis. If the caller does not have a call history, the AI ​​will perform a standard analysis. By referencing the caller's past call history, the analysis unit can improve the accuracy of the analysis and enable more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the caller's past call history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The reception unit collects the caller, recipient, and requirements. For example, when a call comes in, it asks for the caller's name, the recipient's name, and the requirements for the call. The reception unit can use speech recognition technology to convert the caller's voice into text. Specifically, it can use deep learning-based speech recognition technology or HMM (hidden Markov model)-based speech recognition technology. The reception unit then converts the caller's voice into text in real time and sends it to the analysis unit. Step 2: The analysis unit analyzes the information collected by the reception unit and determines whether the call is legitimate. For example, it uses natural language processing technology to understand the content of the call and uses an algorithm that takes into account past call history and the caller's trustworthiness. Specifically, it refers to past call history to check whether the caller has made any nuisance calls in the past. It can also check the caller's authentication information to evaluate their trustworthiness. The analysis unit's processing can also be performed using an AI model. Step 3: The ringing unit rings the phone only if the analysis unit determines that the call is legitimate. For example, the ringing unit can decide whether to ring the phone based on the analysis unit's judgment, and adjust the ring volume and ring pattern of the phone. It is also possible to change the ring pattern depending on the caller's level of urgency.

[0053] (Example 2) A telephone answering system according to an embodiment of the present invention uses AI as the primary point of contact for all calls. When a call is received, the AI ​​first answers the call and collects the caller, recipient, and requirements. Then, it analyzes the collected information to determine whether the call is legitimate. Only if the call is determined to be legitimate does the phone ring. This system protects the livelihoods of rural grandparents and blocks nuisance sales calls to businesses. As a result, a clean society is realized in which only truly necessary calls are made. For example, a reception unit is provided in which AI accepts calls. This reception unit collects the caller, recipient, and requirements. Next, an analysis unit is provided that analyzes the collected information. This analysis unit determines whether the call is legitimate based on the collected information. Finally, a ringing unit is provided that rings the phone only if the call is determined to be legitimate. This eliminates nuisance calls and allows only necessary calls to be made. The analysis unit converts the caller's information into text using voice recognition technology and analyzes the text to determine whether the call is legitimate. The ringing unit determines whether to ring the phone based on the analysis unit's determination. This allows the system to filter out nuisance calls and only make necessary calls. Furthermore, the analysis unit uses an algorithm that takes into account past call history and the reliability of the caller to make more accurate decisions. This allows the system to filter out nuisance calls and only make necessary calls.

[0054] A telephone answering system according to an embodiment includes a reception unit, an analysis unit, and a ringing unit. The reception unit collects information about the caller, the recipient, and the requirements of the call. For example, when a call is received, the reception unit asks for the caller's name, the recipient's name, and the requirements of the call. The reception unit can convert the caller's voice into text using speech recognition technology. For example, the reception unit can convert the caller's voice into text using deep learning-based speech recognition technology. The reception unit can also convert the caller's voice into text using HMM (Hidden Markov Model)-based speech recognition technology. The reception unit can also convert the caller's voice into text in real time and send it to the analysis unit. The analysis unit analyzes the information collected by the reception unit to determine whether the call is legitimate. For example, the analysis unit can analyze the collected information using natural language processing technology to understand the content of the call. The analysis unit can also determine whether the call is legitimate using an algorithm that takes into account past call history and the caller's credibility. For example, the analysis unit can refer to past call history to check whether the caller has made any nuisance calls in the past. The analysis unit can also verify the caller's authentication information to evaluate the caller's trustworthiness. The ringing unit rings the phone only if the analysis unit determines that the call is legitimate. For example, the ringing unit determines whether to ring the phone based on the analysis unit's determination. The ringing unit can also adjust the ring volume and ring pattern of the phone. For example, the ringing unit can adjust the ring volume and change the ring pattern depending on the caller's urgency. This allows the telephone answering system to filter out nuisance calls and only allow necessary calls. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use an AI model that uses collected information as input and determines whether the call is legitimate. This allows the telephone answering system to filter out nuisance calls and only allow necessary calls.

[0055] The analysis unit can convert the caller's information into text using speech recognition technology and analyze the text. The analysis unit can convert the caller's speech into text using, for example, deep learning-based speech recognition technology. For example, the analysis unit inputs the caller's speech into a deep learning model and outputs text data. The analysis unit can also convert the caller's speech into text using HMM (hidden Markov model)-based speech recognition technology. For example, the analysis unit inputs the caller's speech into an HMM model and outputs text data. The analysis unit can also convert the caller's speech into text in real time using speech recognition technology and analyze the text. For example, the analysis unit converts the caller's speech into text in real time and analyzes the text using natural language processing technology. This allows the analysis unit to accurately convert the caller's information into text and analyze it. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the caller's speech data into a generation AI and have the generation AI convert the speech data into text data.

[0056] The analysis unit can use an algorithm based on past call history and the caller's trustworthiness. For example, the analysis unit can refer to past call history to check whether the caller has made any nuisance calls in the past. For example, the analysis unit can search a past call history database to obtain the call history of the caller. The analysis unit can also check the caller's authentication information to evaluate the caller's trustworthiness. For example, the analysis unit can obtain the caller's authentication information from a database and evaluate its trustworthiness. The analysis unit can also determine whether a call is legitimate using an algorithm that takes into account the past call history and the caller's trustworthiness. For example, the analysis unit uses an algorithm that inputs past call history data and the caller's authentication information to determine whether a call is legitimate. This allows the analysis unit to more accurately determine whether a call is legitimate by considering the past call history and the caller's trustworthiness. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past call history data and the caller's authentication information to a generation AI and have the generation AI determine whether the call is legitimate.

[0057] The ringing unit can determine whether to ring the phone based on the judgment of the analysis unit. The ringing unit, for example, determines whether to ring the phone based on the judgment of the analysis unit. For example, the ringing unit receives a signal from the analysis unit and determines whether to ring the phone. The ringing unit can also adjust the ring volume and ring pattern of the phone. For example, the ringing unit can adjust the ring volume of the phone and change the ring pattern depending on the urgency of the caller. In this way, by determining whether to ring the phone based on the judgment of the analysis unit, the ringing unit can eliminate nuisance calls and allow only necessary calls to be made. Some or all of the above-mentioned processing in the ringing unit may be performed using, for example, AI, or may be performed without using AI. For example, the ringing unit can input a signal from the analysis unit to a generation AI and have the generation AI determine whether to ring the phone.

[0058] The reception unit can estimate the user's emotions and adjust the way the call is answered based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit has the AI ​​respond in a calm voice and ask simple questions. For example, the reception unit analyzes the user's voice to determine whether the user is feeling stressed. Alternatively, if the user is relaxed, the reception unit can have the AI ​​respond in a friendly tone and collect detailed information. For example, the reception unit analyzes the user's voice to determine whether the user is relaxed. Alternatively, if the user is in a hurry, the AI ​​can quickly confirm the user's requirements and collect the minimum necessary information. For example, the reception unit analyzes the user's voice to determine whether the user is in a hurry. This allows the reception unit to adjust the way the call is answered based on the user's emotions, thereby enabling a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user voice data to the generation AI and cause the generation AI to estimate emotions.

[0059] The reception unit can analyze the tone and speed of the caller's voice to determine the level of urgency and prioritize the response. For example, if the caller sounds nervous, the AI ​​in the reception unit determines the level of urgency as high and responds quickly. For example, the reception unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds nervous. Alternatively, if the caller sounds calm, the AI ​​can determine the level of urgency as low and provide a standard response. For example, the reception unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds calm. Alternatively, if the caller sounds rushed, the AI ​​can determine the level of urgency as medium and quickly confirm the requirements. For example, the reception unit can analyze the tone and speed of the caller's voice to determine whether the caller is rushed. This allows the reception unit to determine the level of urgency and provide an appropriate response by analyzing the tone and speed of the caller's voice. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the caller's voice tone and speed data into the generation AI and have the generation AI determine the level of urgency.

[0060] When receiving a call, the reception unit can customize the response method by referring to the caller's past call history. For example, if the caller has contacted the caller frequently in the past, the reception unit has the AI ​​respond in a friendly tone. For example, the reception unit searches a past call history database to obtain the caller's call history. The reception unit can also have the AI ​​respond with caution if the caller has made nuisance calls in the past. For example, the reception unit analyzes past call history data to determine whether the caller has made nuisance calls. The reception unit can also have the AI ​​provide a standard response when the caller is contacting the caller for the first time. For example, the reception unit provides a standard response if the caller does not have a call history. This allows the reception unit to customize the response method by referring to the caller's past call history, enabling a more appropriate response. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the caller's past call history data into the generation AI and have the generation AI customize the response method.

[0061] The reception unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is nervous, the reception unit allows the AI ​​to collect requirements as a top priority. For example, the reception unit can analyze the user's voice to determine whether the user is nervous. Furthermore, if the user is relaxed, the reception unit can also allow the AI ​​to collect caller information as a top priority. For example, the reception unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can also allow the AI ​​to collect recipient information as a top priority. For example, the reception unit can analyze the user's voice to determine whether the user is in a hurry. This allows the reception unit to determine the priority of information to be collected based on the user's emotions, thereby enabling more appropriate information collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's voice data into the generation AI and have the generation AI estimate emotions.

[0062] The reception unit can adjust the response method by taking into account the caller's geographical location information when receiving the call. For example, if the caller is calling from a distant location, the reception unit has the AI ​​respond politely. For example, the reception unit acquires the caller's geographical location information and determines whether the call is from a distant location. The reception unit can also have the AI ​​respond in a friendly manner if the caller is calling from a nearby location. For example, the reception unit acquires the caller's geographical location information and determines whether the call is from a nearby location. The reception unit can also have the AI ​​respond in multiple languages ​​if the caller is calling from overseas. For example, the reception unit acquires the caller's geographical location information and determines whether the call is from overseas. This allows the reception unit to provide a more appropriate response by taking the caller's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the caller's geographical location information data to the generation AI and have the generation AI adjust the response method.

[0063] The reception unit can analyze the caller's social media activity and collect related information when receiving the call. For example, if the caller reports an emergency on social media, the reception unit determines that the AI ​​has a high level of urgency. For example, the reception unit analyzes the caller's social media activity to determine whether the caller is reporting an emergency. The reception unit can also use the AI ​​to collect information about an event that the caller is announcing on social media. For example, the reception unit can analyze the caller's social media activity to determine whether the caller is announcing an event. The reception unit can also use the AI ​​to collect information related to a specific topic that the caller frequently posts on social media. For example, the reception unit can analyze the caller's social media activity to determine whether the caller frequently posts on a specific topic. This allows the reception unit to analyze the caller's social media activity to collect related information and provide a more appropriate response. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the caller's social media activity data into the generation AI and cause the generation AI to collect related information.

[0064] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is relaxed, the analysis unit uses AI to perform a detailed analysis. For example, the analysis unit analyzes the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can also perform a quick analysis. For example, the analysis unit analyzes the user's voice to determine whether the user is in a hurry. Furthermore, if the user is nervous, the analysis unit can perform a more focused analysis. For example, the analysis unit analyzes the user's voice to determine whether the user is nervous. This allows the analysis unit to adjust the analysis criteria according to the user's emotions, thereby enabling more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI estimate emotions.

[0065] During analysis, the analysis unit can analyze the tone and speed of the caller's voice to determine the level of urgency and prioritize the analysis. For example, if the caller sounds nervous, the analysis unit determines that the AI ​​is urgency-high and prioritizes the analysis. For example, the analysis unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds nervous. Alternatively, if the caller sounds calm, the analysis unit can determine that the AI ​​is urgency-low and perform a normal analysis. For example, the analysis unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds calm. Alternatively, if the caller sounds rushed, the analysis unit can determine that the AI ​​is urgency-medium and perform a quick analysis. For example, the analysis unit can analyze the tone and speed of the caller's voice to determine whether the caller is rushed. This allows the analysis unit to determine the level of urgency and perform an appropriate analysis by analyzing the tone and speed of the caller's voice. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the caller's voice tone and speed data into the generation AI and have the generation AI determine the level of urgency.

[0066] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the caller's past call history. For example, if the caller has frequently contacted the caller in the past, the analysis unit performs the analysis using the AI, taking into account that pattern. For example, the analysis unit searches a database of past call history to obtain the caller's call history. Furthermore, if the caller has made nuisance calls in the past, the analysis unit can perform an alert analysis using the AI ​​based on that information. For example, the analysis unit analyzes past call history data to determine whether the caller has made nuisance calls. Furthermore, if the caller is contacting the caller for the first time, the analysis unit can perform a standard analysis using the AI. For example, if the caller does not have a call history, the analysis unit performs a standard analysis. By referring to the caller's past call history, the analysis unit can improve the accuracy of the analysis and perform a more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI. For example, the analysis unit can input the caller's past call history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0067] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit uses AI to provide a simple, highly visible display method. For example, the analysis unit analyzes the user's voice to determine whether the user is nervous. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit analyzes the user's voice to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the key points. For example, the analysis unit analyzes the user's voice to determine whether the user is in a hurry. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions, thereby enabling more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI estimate emotions.

[0068] During analysis, the analysis unit can adjust the analysis method by taking into account the caller's geographical location information. For example, if the caller is calling from a distant location, the analysis unit performs the analysis using an AI that takes into account the characteristics of the area. For example, the analysis unit acquires the caller's geographical location information and determines whether the call is from a distant location. In addition, if the caller is calling from a nearby location, the analysis unit can also perform the analysis using an AI that takes into account the characteristics of the area. For example, the analysis unit acquires the caller's geographical location information and determines whether the call is from a nearby location. In addition, if the caller is calling from overseas, the analysis unit can also perform the analysis using an AI that takes into account the characteristics of the area. For example, the analysis unit acquires the caller's geographical location information and determines whether the call is from overseas. This enables the analysis unit to perform a more appropriate analysis by taking into account the caller's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit can input the caller's geographical location information data to the generation AI and cause the generation AI to adjust the analysis method.

[0069] During the analysis, the analysis unit can analyze the sender's social media activity and use related information for the analysis. For example, if the sender reports an emergency on social media, the analysis unit uses the information for the analysis by the AI. For example, the analysis unit analyzes the sender's social media activity to determine whether the sender is reporting an emergency. Furthermore, if the sender announces an event on social media, the analysis unit can also use the information for the analysis by the AI. For example, the analysis unit analyzes the sender's social media activity to determine whether the sender is announcing an event. Furthermore, if the sender frequently posts about a specific topic on social media, the analysis unit can also use the information for the analysis by the AI. For example, the analysis unit analyzes the sender's social media activity to determine whether the sender frequently posts about a specific topic. By analyzing the sender's social media activity, the analysis unit can use related information for the analysis, enabling more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the sender's social media activity data into the generation AI and have the generation AI analyze the related information.

[0070] The ringing unit can estimate the user's emotions and adjust the phone ringing method based on the estimated user's emotions. For example, if the user is relaxed, the AI ​​of the ringing unit can ring with a gentle sound. For example, the ringing unit can analyze the user's voice and determine whether the user is relaxed. Also, if the user is in a hurry, the AI ​​can ring quickly. For example, the ringing unit can analyze the user's voice and determine whether the user is in a hurry. Also, if the user is nervous, the AI ​​can ring with a more subdued sound. For example, the ringing unit can analyze the user's voice and determine whether the user is nervous. This allows the ringing unit to adjust the phone ringing method according to the user's emotions, thereby enabling more appropriate ringing. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sounding unit may be performed using, for example, AI, or may be performed without using AI. For example, the sounding unit may input user voice data to the generation AI and cause the generation AI to estimate emotions.

[0071] When ringing, the ringing unit can analyze the tone and speed of the caller's voice to determine the level of urgency and prioritize ringing. For example, if the caller sounds nervous, the AI ​​determines the level of urgency as high and rings the call first. For example, the ringing unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds nervous. Also, if the caller sounds calm, the AI ​​can determine the level of urgency as low and ring normally. For example, the ringing unit can analyze the tone and speed of the caller's voice to determine whether the caller sounds calm. Also, if the caller sounds rushed, the AI ​​can determine the level of urgency as medium and ring quickly. For example, the ringing unit can analyze the tone and speed of the caller's voice to determine whether the caller is rushed. As a result, the ringing unit can determine the level of urgency and ring appropriately by analyzing the tone and speed of the caller's voice. Some or all of the above-described processing in the ringing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sound unit can input the caller's voice tone and speed data into the generation AI and have the generation AI determine the level of urgency.

[0072] When ringing, the ringing unit can customize the ringing method by referring to the caller's past call history. For example, if the caller has contacted the caller frequently in the past, the AI ​​can ring in a friendly tone. For example, the ringing unit can search a past call history database to obtain the caller's call history. Furthermore, if the caller has made nuisance calls in the past, the AI ​​can ring with caution. For example, the ringing unit can analyze past call history data to determine whether the caller has made nuisance calls. Furthermore, if the caller is contacting the caller for the first time, the AI ​​can ring in a standard manner. For example, if the caller does not have a call history, the ringing unit can ring in a standard manner. This allows the ringing unit to customize the ringing method by referring to the caller's past call history, enabling more appropriate ringing. Some or all of the above-described processing in the ringing unit may be performed using, or without, AI. For example, the ringing unit can input the caller's past call history data into the generation AI and have the generation AI customize the ringing method.

[0073] The sounding unit can estimate the user's emotions and adjust the timing of the sound based on the estimated user's emotions. For example, if the user is relaxed, the AI ​​of the sounding unit can make the sound at a gentle timing. For example, the sounding unit can analyze the user's voice and determine whether the user is relaxed. Also, if the user is in a hurry, the AI ​​can make the sound at a quicker timing. For example, the sounding unit can analyze the user's voice and determine whether the user is in a hurry. Also, if the user is nervous, the AI ​​can make the sound at a more moderate timing. For example, the sounding unit can analyze the user's voice and determine whether the user is nervous. In this way, the sounding unit can adjust the timing of the sound according to the user's emotions, thereby making it possible to make the sound at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sounding unit may be performed using, for example, AI, or may be performed without using AI. For example, the sounding unit may input user voice data to the generation AI and cause the generation AI to estimate emotions.

[0074] When ringing, the ringing unit can adjust the ringing method by taking into account the caller's geographical location information. For example, if the caller is calling from a distant location, the ringing unit uses AI to ring politely. For example, the ringing unit acquires the caller's geographical location information and determines whether the call is from a distant location. In addition, if the caller is calling from a nearby location, the ringing unit can also ring in a friendly manner. For example, the ringing unit acquires the caller's geographical location information and determines whether the call is from a nearby location. In addition, if the caller is calling from overseas, the ringing unit can ring in multiple languages. For example, the ringing unit acquires the caller's geographical location information and determines whether the call is from overseas. This allows the ringing unit to ring more appropriately by taking the caller's geographical location information into account. Some or all of the above-described processing in the ringing unit may be performed using AI, for example, or may be performed without using AI. For example, the ringing unit can input the caller's geographical location information data to the generation AI and cause the generation AI to adjust the ringing method.

[0075] When sounding, the sounding unit can analyze the caller's social media activity and use related information for sounding. For example, if the caller reports an emergency on social media, the AI ​​determines the level of urgency as high and prioritizes sounding. For example, the sounding unit analyzes the caller's social media activity and determines whether the caller is reporting an emergency. In addition, if the caller announces an event on social media, the AI ​​can use that information for sounding. For example, the sounding unit analyzes the caller's social media activity and determines whether the caller is announcing an event. In addition, if the caller frequently posts about a specific topic on social media, the AI ​​can use that information for sounding. For example, the sounding unit analyzes the caller's social media activity and determines whether the caller frequently posts about a specific topic. In this way, the sounding unit can analyze the caller's social media activity and use related information for sounding, thereby enabling more appropriate sounding. Some or all of the above-described processing in the sounding unit may be performed, for example, using AI, or may be performed without using AI. For example, the sound unit can input the caller's social media activity data into the generation AI and have the generation AI use the related information to sound the call. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and ringing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the microphone 38B and control unit 46A of the smart device 14 and converts the caller's voice into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes collected information to determine whether the call is legitimate. The ringing unit is realized, for example, by the control unit 46A of the smart device 14 and determines whether to ring the phone based on the determination of the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and ringing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and control unit 46A of the smart glasses 214 and converts the caller's voice into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes collected information to determine whether the call is legitimate. The ringing unit is realized, for example, by the control unit 46A of the smart glasses 214 and determines whether to ring the phone based on the judgment of the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and ringing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and control unit 46A of the headset type terminal 314 and converts the caller's voice into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes collected information to determine whether the call is legitimate. The ringing unit is realized, for example, by the control unit 46A of the headset type terminal 314 and determines whether to ring the phone based on the determination of the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and ringing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and control unit 46A of the robot 414, and converts the caller's voice into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to determine whether the call is legitimate. The ringing unit is realized, for example, by the control unit 46A of the robot 414, and decides whether to ring the phone based on the judgment of the analysis unit.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The reception unit can analyze the tone and speed of the caller's voice to determine the level of urgency and prioritize the response. For example, if the caller sounds nervous, the AI ​​can determine the level of urgency and respond quickly. The reception unit can analyze the tone and speed of the caller's voice to determine whether they sound nervous. If the caller sounds calm, the AI ​​can determine the level of urgency as low and respond normally. The reception unit can analyze the tone and speed of the caller's voice to determine whether they sound calm. If the caller sounds rushed, the AI ​​can determine the level of urgency as medium and quickly confirm the requirements. The reception unit can analyze the tone and speed of the caller's voice to determine whether they are rushed. This allows the reception unit to determine the level of urgency and provide an appropriate response by analyzing the tone and speed of the caller's voice. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the caller's tone and speed of voice data into the generation AI and have the generation AI determine the level of urgency.

[0078] During analysis, the analysis unit can analyze the caller's tone and speed of voice to determine the level of urgency and prioritize the analysis. For example, if the caller sounds nervous, the AI ​​can determine the level of urgency and prioritize the analysis. The caller's tone and speed of voice can be analyzed to determine whether they are nervous. Alternatively, if the caller sounds calm, the AI ​​can determine the level of urgency as low and perform normal analysis. The caller's tone and speed of voice can be analyzed to determine whether they are calm. Alternatively, if the caller sounds rushed, the AI ​​can determine the level of urgency as medium and perform quick analysis. The caller's tone and speed of voice can be analyzed to determine whether they are rushed. This allows the analysis unit to determine the level of urgency and perform appropriate analysis by analyzing the caller's tone and speed of voice. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the caller's tone and speed of voice data to the generation AI and have the generation AI determine the level of urgency.

[0079] When ringing, the ringing unit can analyze the tone and speed of the caller's voice to determine the level of urgency and prioritize ringing. For example, if the caller sounds nervous, the AI ​​can determine the level of urgency and prioritize ringing. The caller's tone and speed can be analyzed to determine whether they are nervous. Alternatively, if the caller sounds calm, the AI ​​can determine the level of urgency as low and ring normally. The caller's tone and speed can be analyzed to determine whether they are calm. Alternatively, if the caller sounds rushed, the AI ​​can determine the level of urgency as medium and ring quickly. The caller's tone and speed can be analyzed to determine whether they are rushed. This allows the ringing unit to determine the level of urgency and ring appropriately by analyzing the caller's tone and speed. Some or all of the above-mentioned processing in the ringing unit may be performed using AI, for example, or without AI. For example, the ringing unit can input the caller's tone and speed data to the generation AI and have the generation AI determine the level of urgency.

[0080] When receiving a call, the reception unit can customize the response method by referring to the caller's past call history. For example, if the caller has contacted the caller frequently in the past, the AI ​​responds in a friendly tone. The AI ​​searches a past call history database to retrieve the caller's call history. Furthermore, if the caller has made nuisance calls in the past, the AI ​​can respond with caution. The AI ​​analyzes past call history data to determine whether the caller has made nuisance calls. Furthermore, if the caller is contacting the caller for the first time, the AI ​​can provide a standard response. If the caller does not have a call history, the AI ​​provides a standard response. This allows the reception unit to customize the response method by referring to the caller's past call history, enabling a more appropriate response. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the caller's past call history data into the generation AI and have the generation AI customize the response method.

[0081] During analysis, the analysis unit can refer to the caller's past call history to improve the accuracy of the analysis. For example, if the caller has contacted the caller frequently in the past, the AI ​​will take that pattern into account in the analysis. The AI ​​will search a past call history database to obtain the caller's call history. If the caller has made nuisance calls in the past, the AI ​​can also use that information to analyze with caution. The AI ​​will analyze past call history data to determine whether the caller has made nuisance calls. If the caller is contacting the caller for the first time, the AI ​​can also perform a standard analysis. If the caller does not have a call history, the AI ​​will perform a standard analysis. By referencing the caller's past call history, the analysis unit can improve the accuracy of the analysis and enable more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the caller's past call history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0082] The reception unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is nervous, the AI ​​prioritizes collecting requirements. The AI ​​analyzes the user's voice and determines whether the user is nervous. If the user is relaxed, the AI ​​can prioritize collecting caller information. The AI ​​analyzes the user's voice and determines whether the user is relaxed. If the user is in a hurry, the AI ​​can prioritize collecting recipient information. The AI ​​analyzes the user's voice and determines whether the user is in a hurry. This allows the reception unit to prioritize the information to be collected based on the user's emotions, enabling more appropriate information collection. 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 reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is relaxed, the AI ​​can perform a detailed analysis. The AI ​​can analyze the user's voice and determine whether the user is relaxed. If the user is in a hurry, the AI ​​can perform a quick analysis. The AI ​​can analyze the user's voice and determine whether the user is in a hurry. If the user is nervous, the AI ​​can perform a key analysis. The AI ​​can analyze the user's voice and determine whether the user is nervous. This allows the analysis unit to adjust the analysis criteria according to the user's emotions, enabling more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using an AI, for example, or without an AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0084] The ringing unit can estimate the user's emotions and adjust the ringing method of the phone based on the estimated user's emotions. For example, if the user is relaxed, the AI ​​can ring with a gentle sound. The AI ​​can analyze the user's voice and determine whether the user is relaxed. If the user is in a hurry, the AI ​​can ring quickly. The AI ​​can analyze the user's voice and determine whether the user is in a hurry. If the user is nervous, the AI ​​can ring with a more subdued sound. The AI ​​can analyze the user's voice and determine whether the user is nervous. This allows the ringing unit to adjust the ringing method of the phone according to the user's emotions, enabling more appropriate ringing. 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-mentioned processing in the ringing unit can be performed using an AI, for example, or without an AI. For example, the ringing unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0085] The sounding unit can estimate the user's emotions and adjust the timing of the sound based on the estimated user's emotions. For example, if the user is relaxed, the AI ​​can sound the alarm at a gentler timing. The AI ​​can analyze the user's voice and determine whether the user is relaxed. If the user is in a hurry, the AI ​​can sound the alarm quickly. The AI ​​can analyze the user's voice and determine whether the user is in a hurry. If the user is nervous, the AI ​​can sound the alarm at a more moderate timing. The AI ​​can analyze the user's voice and determine whether the user is nervous. This allows the sounding unit to adjust the timing of the sound according to the user's emotions, thereby enabling the sounding unit to sound at a more appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the sounding unit can be performed using, for example, an AI, or without an AI. For example, the sounding unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the AI ​​can provide a simple, highly visible display method. The AI ​​can analyze the user's voice and determine whether the user is nervous. If the user is relaxed, the AI ​​can provide a display method that includes detailed information. The AI ​​can analyze the user's voice and determine whether the user is relaxed. If the user is in a hurry, the AI ​​can provide a display method that focuses on the main points. The AI ​​can analyze the user's voice and determine whether the user is in a hurry. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions, enabling more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using an AI, for example, or without an AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The reception unit collects the caller, recipient, and requirements. For example, when a call comes in, it asks for the caller's name, the recipient's name, and the requirements for the call. The reception unit can use speech recognition technology to convert the caller's voice into text. Specifically, it can use deep learning-based speech recognition technology or HMM (hidden Markov model)-based speech recognition technology. The reception unit then converts the caller's voice into text in real time and sends it to the analysis unit. Step 2: The analysis unit analyzes the information collected by the reception unit and determines whether the call is legitimate. For example, it uses natural language processing technology to understand the content of the call and uses an algorithm that takes into account past call history and the caller's trustworthiness. Specifically, it refers to past call history to check whether the caller has made any nuisance calls in the past. It can also check the caller's authentication information to evaluate their trustworthiness. The analysis unit's processing can also be performed using an AI model. Step 3: The ringing unit rings the phone only if the analysis unit determines that the call is legitimate. For example, the ringing unit can decide whether to ring the phone based on the analysis unit's judgment, and adjust the ring volume and ring pattern of the phone. It is also possible to change the ring pattern depending on the caller's level of urgency.

[0089] 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.

[0090] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0091] 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.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] 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.

[0160] [Explanation of symbols]

[0161] 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 receptionist who collects callers, recipients, and requirements; an analysis unit that analyzes the information collected by the reception unit and determines whether the call is legitimate; a ringing unit that rings the phone only when the analysis unit determines that the call is legitimate. A system characterized by:

2. The analysis unit Using voice recognition technology, the caller's information is converted into text and that text is analyzed.

2. The system of claim 1.

3. The analysis unit Uses an algorithm based on past call history and the caller's credibility 2. The system of claim 1.

4. The sound unit Deciding whether to ring the phone based on the judgment of the analysis unit 2. The system of claim 1.

5. The reception unit Inferring user emotions and adjusting how a call is answered based on the estimated user emotions 2. The system of claim 1.

6. The reception unit Analyze the caller's tone and speed of speech to determine urgency and prioritize responses 2. The system of claim 1.

7. The reception unit When accepting a call, view the caller's past call history and customize your response 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

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