System

Abstract

To provide a system capable of providing an effective measurement against a troublesome telephone call and a scam telephone call.SOLUTION: A system according to an embodiment, comprises a collection part, a learning part, a responce part, and a display part. The collection part collects information on a troublesome telephone call. The learning part leans a pattern of the troublesome telephone call on the basis of the information collected by the collection part. The responce part automatically responses to the troublesome telephone call on the basis of the pattern learned by the learning part. The display part displays a risk ranking on an incoming call screen on the basis of the pattern learned by the learning part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not provide sufficient effective countermeasures against nuisance and fraudulent calls, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an effective measure against nuisance calls and fraudulent calls. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, a response unit, and a display unit. The collection unit collects information on nuisance calls. The learning unit learns patterns of nuisance calls based on the information collected by the collection unit. The response unit automatically responds to nuisance calls based on the patterns learned by the learning unit. The display unit displays a risk ranking on the incoming call screen based on the patterns learned by the learning unit. Effect of the Invention

[0007] The system according to the embodiment can provide an effective measure against nuisance calls and fraudulent calls. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The spam and fraud call countermeasure system according to the embodiment of the present invention is a system that collects information on nuisance calls, learns from them, automatically responds to them, and displays a risk ranking. This system ensures the safety and security of users by collecting information on nuisance calls, learning from them, automatically responding to them, and displaying a risk ranking. For example, there is an automatic response function for nuisance calls. When a user receives a nuisance call, the system automatically responds and responds using a fixed phrase. For example, a phrase such as "This call has been determined to be a nuisance call. Please refrain from contacting us in the future" is used. This function eliminates the need for users to respond directly to nuisance calls. Next, there is the operation of a database for eliminating nuisance calls. Information on nuisance calls is collected and stored in a database. The system refers to this database and learns the patterns of nuisance calls. This makes it possible to quickly respond to new nuisance calls. In addition, there is a function to display a risk ranking on the incoming call screen. When a call that may be a nuisance call is received, the system evaluates the risk of the phone number and displays it on the incoming call screen. For example, it displays "risk: high". This function allows users to understand the risk of the call in advance and decide how to respond. In this way, by utilizing the system, it is possible to provide effective measures against nuisance calls and ensure the safety and security of users. The spam and fraud call prevention system can collect information on nuisance calls, learn from them, automatically respond, and display a risk ranking.

[0029] The spam / fraudulent call countermeasure system according to the embodiment includes a collection unit, a learning unit, a response unit, and a display unit. The collection unit collects information on nuisance calls. The information on nuisance calls includes, for example, telephone numbers, call contents, and caller information, but is not limited to such examples. The collection unit, for example, registers the telephone numbers of nuisance calls in a database and analyzes the call contents. The collection unit can also collect caller information and identify nuisance call patterns. For example, the collection unit automatically detects nuisance call telephone numbers and registers them in a database. The call contents are converted into text data using voice recognition technology and analyzed. The caller information collects attribute information such as the region and industry of the caller, and identifies the nuisance call pattern. The learning unit learns the nuisance call pattern based on the information collected by the collection unit. The learning is performed, for example, using a machine learning algorithm, but is not limited to such examples. For example, the learning unit uses a neural network to learn the nuisance call pattern. The learning unit can also learn the nuisance call pattern using a support vector machine. The learning unit can also learn patterns of nuisance calls based on past nuisance call data. For example, the learning unit inputs past nuisance call data into a neural network to learn patterns of nuisance calls. The support vector machine is used to classify patterns of nuisance calls. The past nuisance call data includes information such as the source of the nuisance call and the contents of the call. The response unit automatically responds to the nuisance call based on the patterns learned by the learning unit. The automatic response is performed, for example, based on a method of selecting a response phrase and a timing of the response, but is not limited to such examples. For example, the response unit automatically responds to the nuisance call using a fixed phrase. The response unit can also automatically respond using a response phrase that can be customized by the user. The response unit can also dynamically generate a response phrase based on the contents of the nuisance call. For example, the response unit automatically responds to the nuisance call using a fixed phrase such as "This call has been determined to be a nuisance call. Please refrain from contacting us in the future." The response phrase that can be customized by the user is set within a range that can be edited by the user.The dynamically generated response phrase is generated in real time based on the contents of the nuisance call. The display unit displays a risk ranking on the incoming call screen based on the pattern learned by the learning unit. The risk ranking is displayed based on, for example, an evaluation item or a scoring method, but is not limited to such examples. For example, the display unit displays the risk of the nuisance call as a rank such as "high", "medium", or "low". The display unit can also visually display the risk ranking using color coding or icons. The display unit can also update the risk ranking in real time. For example, the display unit displays the risk of the nuisance call as a color coding of red, yellow, or green. The icon displays a warning icon for a nuisance call with a high risk. The risk ranking, which is updated in real time, is displayed based on the latest nuisance call information. As a result, the spam / fraudulent call countermeasure system according to the embodiment can collect and learn information on nuisance calls, automatically respond to the calls, and display the risk ranking.

[0030] The collection unit collects information on nuisance calls. The information on nuisance calls includes, for example, telephone numbers, call contents, and caller information, but is not limited to such examples. For example, the collection unit registers the telephone numbers of nuisance calls in a database and analyzes the call contents. The collection unit can also collect caller information and identify patterns of nuisance calls. For example, the collection unit automatically detects telephone numbers of nuisance calls and registers them in a database. The call contents are converted into text data using voice recognition technology and analyzed. For caller information, attribute information such as the region and industry of the caller is collected, and the pattern of nuisance calls is identified. In order to efficiently collect this information, the collection unit uses a combination of multiple technologies. For example, to collect telephone numbers, the database of a communication carrier and reports from users are utilized. To analyze the call contents, not only voice recognition technology but also natural language processing technology is used to analyze the meaning of the call contents and extract the characteristics of nuisance calls. To collect caller information, public information on the Internet and corporate databases are utilized to obtain detailed information on the caller. This allows the collection unit to collect nuisance call information from multiple angles and register it in a database. Furthermore, the collection unit updates the collected information in real time, and can always maintain the latest nuisance call information. For example, newly reported nuisance call information can be instantly reflected in the database and made available to other departments. The collection unit can also enhance the effectiveness of nuisance call countermeasures by linking the collected information with other systems and services. For example, the collected nuisance call information can be shared with other security systems to realize comprehensive security measures. This enables the collection unit to collect nuisance call information efficiently and effectively, and improve the performance of the entire system.

[0031] The learning unit learns the patterns of nuisance calls based on the information collected by the collection unit. The learning is performed, for example, using a machine learning algorithm, but is not limited to such an example. For example, the learning unit uses a neural network to learn the patterns of nuisance calls. The learning unit can also learn the patterns of nuisance calls using a support vector machine. The learning unit can also learn the patterns of nuisance calls based on past nuisance call data. For example, the learning unit inputs past nuisance call data into a neural network to learn the patterns of nuisance calls. The support vector machine is used to classify the patterns of nuisance calls. The past nuisance call data includes information such as the call source and the contents of the call. The learning unit uses these data to extract the characteristics of the nuisance calls and identify the patterns. For example, the neural network analyzes the text data of the call contents and learns phrases and expressions specific to nuisance calls. The support vector machine classifies the patterns of nuisance calls based on the attribute information of the telephone number and the call source. This allows the learning unit to learn the patterns of nuisance calls with high accuracy and predict future nuisance calls. Furthermore, the learning unit can continuously update the learning results to respond to the latest nuisance call patterns. For example, the learning model can be retrained based on newly collected nuisance call information to improve accuracy. The learning unit can also use an anomaly detection algorithm to quickly detect nuisance call patterns that differ from normal. This allows the learning unit to quickly and accurately learn nuisance call patterns, improving the reliability and safety of the entire system.

[0032] The response unit automatically responds to the nuisance call based on the pattern learned by the learning unit. The automatic response is performed, for example, based on the selection method of the response phrase and the timing of the response, but is not limited to such examples. For example, the response unit automatically responds to the nuisance call using a fixed phrase. The response unit can also automatically respond using a response phrase that is customizable by the user. The response unit can also dynamically generate a response phrase based on the content of the nuisance call. For example, the response unit automatically responds to the nuisance call using a fixed phrase such as "This call has been determined to be a nuisance call. Please refrain from contacting us in the future." The response phrase that is customizable by the user is set within a range that the user can edit. The dynamically generated response phrase is generated in real time based on the content of the nuisance call. The response unit issues these response phrases at an appropriate time to effectively respond to the caller of the nuisance call. For example, if the content of the nuisance call is suspected to be fraudulent, the response unit automatically issues a warning phrase such as "This call is being recorded. Fraudulent acts are punishable by law." The response unit can also automatically block the nuisance call according to the user's settings. For example, calls from specific numbers or callers may be automatically rejected and the user may be notified. In addition, the response unit may record the response history of nuisance calls and provide the data for later analysis. This allows the response unit to respond to nuisance calls quickly and effectively, reducing the burden on the user.

[0033] The display unit displays a risk ranking on the incoming call screen based on the pattern learned by the learning unit. The risk ranking is displayed based on, for example, an evaluation item or a scoring method, but is not limited to such examples. For example, the display unit displays the risk of nuisance calls in ranks such as "high", "medium", and "low". The display unit can also visually display the risk ranking using color coding or icons. The display unit can also update the risk ranking in real time. For example, the display unit displays the risk of nuisance calls in red, yellow, and green. The icon displays a warning icon for nuisance calls with a high risk. The risk ranking, which is updated in real time, is displayed based on the latest nuisance call information. The display unit employs an intuitive interface to provide such information to the user in an easy-to-understand manner. For example, a large icon or a color-coded bar is displayed on the incoming call screen so that the user can determine the risk at a glance. The display unit can also display detailed information about the nuisance call. For example, the display unit displays the phone number, the area of ​​origin, an overview of the call content, and the like so that the user can check the content of the nuisance call. Furthermore, the display unit provides a function that allows the user to check the history of past nuisance calls. For example, a list of nuisance calls received in the past can be displayed, allowing the user to check the risk level and detailed information of each call. This allows the display unit to provide the user with information about nuisance calls in an easy-to-understand manner, and to support a quick response.

[0034] The response unit can automatically respond to nuisance calls using a response phrase that can be customized by the user. The response unit automatically responds to nuisance calls using, for example, a response phrase that can be customized by the user. For example, the response unit can set a response phrase within a range that can be edited by the user. The response unit can also provide a default response phrase, which can be customized by the user. For example, the response unit provides a default response phrase such as "This call has been determined to be a nuisance call. Please refrain from contacting us in the future." The user can edit this default response phrase and customize it to his or her liking. This allows for more flexible responses by using a response phrase customized by the user. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input a response phrase customized by the user to the AI, and the AI ​​can automatically respond to nuisance calls based on the response phrase.

[0035] The learning unit can update the database based on feedback from the user. The learning unit updates the database based on, for example, feedback from the user. For example, the learning unit collects evaluations and comments made by the user on nuisance calls and reflects them in the database. The learning unit can also update the database taking into consideration the timing of the feedback made by the user on the nuisance calls. For example, when the user evaluates a nuisance call as "very nuisance," "slightly nuisance," or "not nuisance," the learning unit reflects the evaluation in the database. The user's comments include information about the content and source of the nuisance call. The feedback is collected at an appropriate timing, such as immediately after the user receives a nuisance call or after a certain period of time. As a result, the accuracy of the database is improved by reflecting the user's feedback. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's feedback into the AI, and the AI ​​can update the database based on the feedback.

[0036] The display unit can visually display the danger ranking by using colors or icons. The display unit visually displays the danger ranking by using colors or icons, for example. For example, the display unit can display the danger of nuisance calls by using the colors red, yellow, and green. The display unit can also display a warning icon for a nuisance call with a high danger level. For example, the display unit displays a red warning icon for a nuisance call with a high danger level, a yellow warning icon for a nuisance call with a medium danger level, and a green warning icon for a nuisance call with a low danger level. This allows the user to intuitively grasp the danger level by visually displaying the danger level. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the danger level of a nuisance call into AI, and the AI ​​can display it using colors or icons based on the danger level.

[0037] The response unit may include a setting for automatically responding when the danger level is high. The response unit may include, for example, a setting for automatically responding when the danger level is high. For example, the response unit may automatically respond using a response phrase when the danger level of a nuisance call is determined to be high. The response unit may also perform an automatic response based on a danger level threshold set by the user. For example, the response unit may be set to automatically respond to nuisance calls evaluated as having a "high" danger level, and to manually respond to nuisance calls evaluated as having a "medium" or "low" danger level. This reduces the burden on the user by automatically responding when the danger level is high. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit may input the danger level of a nuisance call into AI, and the AI ​​may perform an automatic response based on the danger level.

[0038] The learning unit can evaluate the risk level based on the frequency and content of nuisance calls. The learning unit evaluates the risk level based on, for example, the frequency and content of nuisance calls. For example, the learning unit can evaluate the risk level based on the number of nuisance calls and keywords in the call content. The learning unit can also evaluate the risk level based on information on the source of the nuisance calls and past call history. For example, the learning unit evaluates the risk level high when the number of nuisance calls is large. The learning unit can also evaluate the risk level high when the call content includes keywords such as "fraud" and "solicitation". Furthermore, the learning unit can also evaluate the risk level high when the source of the nuisance calls is concentrated in a specific region or industry. As a result, by evaluating the risk level based on the frequency and content of nuisance calls, a more accurate risk evaluation is possible. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the frequency and content of nuisance calls into AI, and the AI ​​can evaluate the risk level based on the information.

[0039] The collection unit can analyze the user's past call history and select the optimal collection method when collecting information on nuisance calls. For example, the collection unit analyzes the user's past call history and selects the optimal collection method when collecting information on nuisance calls. For example, the collection unit analyzes the pattern of nuisance calls received by the user in the past, and preferentially collects calls with a similar pattern. In addition, if the collection unit determines from the user's call history that many nuisance calls are made during a specific time period, it can also concentrate information collection during that time period. Furthermore, the collection unit can also preferentially collect calls from a specific caller based on the user's call history. This makes it possible to select the optimal information collection method by analyzing the past call history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past call history into AI, and the AI ​​can select the optimal collection method based on the call history.

[0040] The collection unit can perform filtering based on the user's current location information and time zone when collecting information on nuisance calls. For example, the collection unit performs filtering based on the user's current location information and time zone when collecting information on nuisance calls. For example, when the user is in a specific area, the collection unit preferentially collects information on nuisance calls that occur frequently in that area. In addition, when the user receives a nuisance call at night, the collection unit can also collect information on nuisance calls that occur frequently at night. Furthermore, when the user is moving, the collection unit can also collect information on nuisance calls that occur frequently at the user's destination based on the current location information. This makes it possible to collect information more effectively by filtering based on the current location information and time zone. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current location information and time zone into AI, and the AI ​​can perform filtering based on the information.

[0041] The collection unit can analyze the social media activity of the user when collecting nuisance call information, and collect related nuisance call information. For example, when collecting nuisance call information, the collection unit can analyze the social media activity of the user and collect related nuisance call information. For example, when the user posts on social media about nuisance calls, the collection unit can collect related nuisance call information based on the content. In addition, when a social media friend of the user posts on nuisance calls, the collection unit can also collect nuisance call information based on the information. Furthermore, when the user shares information about a specific nuisance call on social media, the collection unit can also collect nuisance call information based on the information. In this way, by analyzing social media activity, related nuisance call information can be effectively collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI, and the AI ​​can collect related nuisance call information based on the activity.

[0042] The collection unit can refer to the user's contact list when collecting information on nuisance calls and preferentially collect known nuisance phone numbers. For example, the collection unit refers to the user's contact list when collecting information on nuisance calls and preferentially collects known nuisance phone numbers. For example, the collection unit preferentially collects nuisance phone numbers that match numbers registered in the user's contact list. The collection unit can also preferentially collect nuisance calls from numbers registered in the user's contact list. Furthermore, the collection unit can also preferentially collect nuisance phone numbers similar to numbers registered in the user's contact list. This allows known nuisance phone numbers to be preferentially collected by referring to the contact list. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit inputs the user's contact list into the AI, and the AI ​​can preferentially collect known nuisance phone numbers based on the list.

[0043] The learning unit can optimize the learning algorithm by referring to past nuisance call data during learning. The learning unit can optimize the learning algorithm by referring to past nuisance call data during learning, for example. For example, the learning unit learns nuisance call patterns based on past nuisance call data and optimizes the algorithm. The learning unit can also learn patterns of specific call sources from past nuisance call data and optimize the algorithm. Furthermore, the learning unit can analyze past nuisance call data, discover new nuisance call patterns, and optimize the algorithm. In this way, the learning algorithm can be optimized by referring to past nuisance call data. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input past nuisance call data into AI, and the AI ​​can optimize the learning algorithm based on the data.

[0044] The learning unit can learn based on attribute information of the caller of the nuisance call during learning. The learning unit, for example, learns based on attribute information of the caller of the nuisance call during learning. For example, the learning unit learns nuisance call patterns for each region based on regional information of the caller of the nuisance call. The learning unit can also learn nuisance call patterns for each industry based on industry information of the caller of the nuisance call. Furthermore, the learning unit can also learn nuisance call patterns for each time period based on time period information of the caller of the nuisance call. This makes it possible to learn more accurately by taking into account the attribute information of the caller. Part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input attribute information of the caller of the nuisance call to AI, and the AI ​​can learn based on that information.

[0045] The learning unit can weight the learning data based on the time zone of the nuisance call during learning. The learning unit, for example, weights the learning data based on the time zone of the nuisance call during learning. For example, the learning unit weights data of time zones where nuisance calls are frequent to strengthen learning. The learning unit can also lightly weight data of time zones where nuisance calls are few to make learning more efficient. Furthermore, the learning unit can weight data of nuisance calls that are concentrated in a specific time zone to optimize learning. As a result, weighting the learning data based on the time zone of the call improves the efficiency of learning. Part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time zone of the nuisance call to AI, and the AI ​​can weight the learning data based on that time zone.

[0046] The learning unit can perform text analysis of the contents of the nuisance call during learning and perform learning based on specific keywords. For example, the learning unit performs text analysis of the contents of the nuisance call during learning and performs learning based on specific keywords. For example, the learning unit performs text analysis of the contents of the nuisance call and performs learning based on specific keywords (e.g., fraud, solicitation). The learning unit can also extract frequently occurring keywords from the contents of the nuisance call and use them as learning data. Furthermore, the learning unit can analyze the contents of the nuisance call, discover new keywords, and add them to the learning data. This makes it possible to perform learning based on text analysis and perform more accurate learning. Part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the contents of the nuisance call into AI, and the AI ​​can extract specific keywords based on the contents and perform learning.

[0047] The response unit can dynamically generate a response phrase based on the contents of the nuisance call when responding. The response unit dynamically generates a response phrase based on the contents of the nuisance call when responding, for example. For example, the response unit analyzes the contents of the nuisance call and generates a response phrase including a warning if there is a possibility of fraud. The response unit can also generate a refusal response phrase if there is a possibility of solicitation from the contents of the nuisance call. Furthermore, the response unit can generate a response phrase corresponding to a specific keyword based on the contents of the nuisance call. This allows for more flexible responses by generating a response phrase based on the contents of the nuisance call. Part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the contents of the nuisance call into AI, and the AI ​​can dynamically generate a response phrase based on the contents.

[0048] The response unit can respond by considering the attribute information of the caller of the nuisance call when responding. The response unit, for example, responds by considering the attribute information of the caller of the nuisance call when responding. For example, if the caller of the nuisance call is from a specific area, the response unit uses a response phrase specialized for that area. In addition, if the caller of the nuisance call is from a specific industry, the response unit can also use a response phrase specialized for that industry. Furthermore, the response unit can select an appropriate response phrase based on time zone information of the caller of the nuisance call. This makes it possible to respond more appropriately by considering the attribute information of the caller. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input attribute information of the caller of the nuisance call to AI, and the AI ​​can select a response phrase based on that information.

[0049] The response unit can select a response phrase based on the geographical location information of the caller of the nuisance call when responding. For example, the response unit selects a response phrase based on the geographical location information of the caller of the nuisance call when responding. For example, when the caller of the nuisance call is from a specific region, the response unit uses a response phrase specialized for that region. In addition, when the caller of the nuisance call is from a specific country, the response unit can also generate a response phrase in the language of that country. Furthermore, the response unit can select an appropriate response phrase based on the geographical location information of the caller of the nuisance call. In this way, by taking into account the geographical location information of the caller, a more appropriate response phrase can be selected. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the geographical location information of the caller of the nuisance call to AI, and the AI ​​can select a response phrase based on that information.

[0050] The response unit can perform text analysis of the contents of the nuisance call at the time of response and select a response phrase based on a specific keyword. For example, the response unit performs text analysis of the contents of the nuisance call at the time of response and selects a response phrase based on a specific keyword. For example, the response unit performs text analysis of the contents of the nuisance call and selects a response phrase including a warning if there is a possibility of fraud. In addition, the response unit can select a refusal response phrase if there is a possibility of solicitation from the contents of the nuisance call. Furthermore, the response unit can select a response phrase corresponding to a specific keyword based on the contents of the nuisance call. This makes it possible to respond more appropriately by selecting a response phrase based on text analysis. Part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the contents of the nuisance call into AI, and the AI ​​can extract a specific keyword based on the contents and select a response phrase.

[0051] The display unit can optimize the display algorithm by referring to past nuisance call data when displaying the risk ranking. The display unit optimizes the display algorithm by referring to past nuisance call data when displaying the risk ranking, for example. For example, the display unit optimizes the display algorithm of the risk ranking based on the past nuisance call data. The display unit can also evaluate the risk of a specific caller from the past nuisance call data and optimize the display algorithm. Furthermore, the display unit can analyze the past nuisance call data, introduce new risk evaluation criteria, and optimize the display algorithm. In this way, the display algorithm can be optimized by referring to the past nuisance call data. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the past nuisance call data into AI, and the AI ​​can optimize the display algorithm based on the data.

[0052] The display unit can perform display based on attribute information of the caller of the nuisance call when displaying the risk ranking. The display unit performs display based on attribute information of the caller of the nuisance call when displaying the risk ranking. For example, the display unit displays a risk ranking for each region based on regional information of the caller of the nuisance call. The display unit can also display a risk ranking for each industry based on industry information of the caller of the nuisance call. Furthermore, the display unit can display a risk ranking for each time period based on time period information of the caller of the nuisance call. In this way, by taking into account the attribute information of the caller, a more accurate risk ranking can be displayed. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input attribute information of the caller of the nuisance call to AI, and the AI ​​can perform display based on that information.

[0053] The display unit can weight the display data based on the time zone of the nuisance call when displaying the risk ranking. The display unit, for example, weights the display data based on the time zone of the nuisance call when displaying the risk ranking. For example, the display unit weights data of time zones where nuisance calls are frequent to strengthen the display. The display unit can also lightly weight data of time zones where nuisance calls are few to make the display more efficient. Furthermore, the display unit can weight data of nuisance calls that are concentrated in a specific time zone to optimize the display. Thus, by weighting the display data based on the time zone of the call, the efficiency of the display is improved. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the time zone of the nuisance call to AI, and the AI ​​can weight the display data based on the time zone.

[0054] The display unit can perform text analysis of the contents of the nuisance call and display based on a specific keyword when displaying the risk ranking. For example, the display unit can perform text analysis of the contents of the nuisance call and display based on a specific keyword when displaying the risk ranking. For example, the display unit can perform text analysis of the contents of the nuisance call and display including a warning if there is a possibility of fraud. In addition, the display unit can also display a refusal if there is a possibility of solicitation from the contents of the nuisance call. Furthermore, the display unit can also display corresponding to a specific keyword based on the contents of the nuisance call. As a result, a more accurate risk ranking can be displayed by displaying based on the text analysis. Part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the contents of the nuisance call into AI, and the AI ​​can extract specific keywords based on the contents and display them.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The collection unit can analyze the user's call history and identify patterns of nuisance calls. For example, the collection unit can analyze information on the time periods and call sources of nuisance calls received by the user in the past, and preferentially collect calls with similar patterns. The collection unit can also extract calls containing specific keywords from the user's call history, and identify patterns of nuisance calls based on that information. Furthermore, the collection unit can preferentially collect nuisance calls from specific regions or industries based on the user's call history. This makes it possible to take more effective measures against nuisance calls by utilizing the user's call history.

[0057] The response unit can dynamically generate a response phrase based on the content of the nuisance call. For example, the response unit analyzes the content of the nuisance call and generates a response phrase including a warning if there is a possibility of fraud. The response unit can also generate a refusal response phrase if there is a possibility of solicitation based on the content of the nuisance call. Furthermore, the response unit can generate a response phrase corresponding to a specific keyword based on the content of the nuisance call. This allows for more flexible responses by generating response phrases based on the content of the nuisance call.

[0058] The learning unit can perform learning based on attribute information of the caller of the nuisance call. For example, the learning unit learns nuisance call patterns for each region based on regional information of the caller of the nuisance call. The learning unit can also learn nuisance call patterns for each industry based on industry information of the caller of the nuisance call. Furthermore, the learning unit can learn nuisance call patterns for each time period based on time period information of the caller of the nuisance call. This allows for more accurate learning by taking into account the caller's attribute information.

[0059] When displaying the risk rankings, the display unit can perform the display based on attribute information of the callers of the nuisance calls. For example, the display unit can display a risk ranking for each region based on regional information of the callers of the nuisance calls. The display unit can also display a risk ranking for each industry based on industry information of the callers of the nuisance calls. Furthermore, the display unit can display a risk ranking for each time period based on time period information of the callers of the nuisance calls. This allows a more accurate risk ranking to be displayed by taking into account the caller's attribute information.

[0060] When collecting information on nuisance calls, the collection unit can analyze the user's social media activity and collect related nuisance call information. For example, if a user posts on social media about nuisance calls, the collection unit can collect related nuisance call information based on the content of the post. In addition, if a user's social media friend posts about nuisance calls, the collection unit can also collect nuisance call information based on that information. Furthermore, if a user shares information about a specific nuisance call on social media, the collection unit can also collect nuisance call information based on that information. This makes it possible to effectively collect related nuisance call information by analyzing social media activity.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects information on nuisance calls. Information on nuisance calls includes, for example, telephone numbers, call content, and caller information. The collection unit registers the telephone numbers of nuisance calls in a database and analyzes the content of the calls. It can also collect caller information and identify patterns of nuisance calls. For example, the collection unit automatically detects telephone numbers of nuisance calls and registers them in a database. The content of the calls is converted into text data using voice recognition technology and analyzed. Caller information is collected from attribute information such as the region and industry of the caller, and nuisance call patterns are identified. Step 2: The learning unit learns nuisance call patterns based on the information collected by the collection unit. Learning is performed, for example, using a machine learning algorithm. For example, the learning unit uses a neural network or a support vector machine to learn nuisance call patterns. It is also possible to learn nuisance call patterns based on past nuisance call data. Past nuisance call data includes information such as the source of the nuisance call and the content of the call. Step 3: The response unit automatically responds to the nuisance call based on the pattern learned by the learning unit. The automatic response is performed based on, for example, the method of selecting a response phrase and the timing of the response. For example, the response unit automatically responds to the nuisance call using a fixed phrase. It is also possible to automatically respond using a response phrase that can be customized by the user. Furthermore, it is also possible to dynamically generate a response phrase based on the content of the nuisance call. Step 4: The display unit displays a risk ranking on the incoming call screen based on the patterns learned by the learning unit. The risk ranking is displayed based on, for example, evaluation items or a scoring method. For example, the display unit displays the risk of nuisance calls as a rank such as "high," "medium," or "low." The risk ranking can also be displayed visually using colors or icons. Furthermore, the risk ranking can be updated in real time.

[0063] (Example 2) The spam and fraud call countermeasure system according to the embodiment of the present invention is a system that collects information on nuisance calls, learns from them, automatically responds to them, and displays a risk ranking. This system ensures the safety and security of users by collecting information on nuisance calls, learning from them, automatically responding to them, and displaying a risk ranking. For example, there is an automatic response function for nuisance calls. When a user receives a nuisance call, the system automatically responds and responds using a fixed phrase. For example, a phrase such as "This call has been determined to be a nuisance call. Please refrain from contacting us in the future" is used. This function eliminates the need for users to respond directly to nuisance calls. Next, there is the operation of a database for eliminating nuisance calls. Information on nuisance calls is collected and stored in a database. The system refers to this database and learns the patterns of nuisance calls. This makes it possible to quickly respond to new nuisance calls. In addition, there is a function to display a risk ranking on the incoming call screen. When a call that may be a nuisance call is received, the system evaluates the risk of the phone number and displays it on the incoming call screen. For example, it displays "risk: high". This function allows users to understand the risk of the call in advance and decide how to respond. In this way, by utilizing the system, it is possible to provide effective measures against nuisance calls and ensure the safety and security of users. The spam and fraud call prevention system can collect information on nuisance calls, learn from them, automatically respond, and display a risk ranking.

[0064] The spam / fraudulent call countermeasure system according to the embodiment includes a collection unit, a learning unit, a response unit, and a display unit. The collection unit collects information on nuisance calls. The information on nuisance calls includes, for example, telephone numbers, call contents, and caller information, but is not limited to such examples. The collection unit, for example, registers the telephone numbers of nuisance calls in a database and analyzes the call contents. The collection unit can also collect caller information and identify nuisance call patterns. For example, the collection unit automatically detects nuisance call telephone numbers and registers them in a database. The call contents are converted into text data using voice recognition technology and analyzed. The caller information collects attribute information such as the region and industry of the caller, and identifies the nuisance call pattern. The learning unit learns the nuisance call pattern based on the information collected by the collection unit. The learning is performed, for example, using a machine learning algorithm, but is not limited to such examples. For example, the learning unit uses a neural network to learn the nuisance call pattern. The learning unit can also learn the nuisance call pattern using a support vector machine. The learning unit can also learn patterns of nuisance calls based on past nuisance call data. For example, the learning unit inputs past nuisance call data into a neural network to learn patterns of nuisance calls. The support vector machine is used to classify patterns of nuisance calls. The past nuisance call data includes information such as the source of the nuisance call and the contents of the call. The response unit automatically responds to the nuisance call based on the patterns learned by the learning unit. The automatic response is performed, for example, based on a method of selecting a response phrase and a timing of the response, but is not limited to such examples. For example, the response unit automatically responds to the nuisance call using a fixed phrase. The response unit can also automatically respond using a response phrase that can be customized by the user. The response unit can also dynamically generate a response phrase based on the contents of the nuisance call. For example, the response unit automatically responds to the nuisance call using a fixed phrase such as "This call has been determined to be a nuisance call. Please refrain from contacting us in the future." The response phrase that can be customized by the user is set within a range that can be edited by the user.The dynamically generated response phrase is generated in real time based on the contents of the nuisance call. The display unit displays a risk ranking on the incoming call screen based on the pattern learned by the learning unit. The risk ranking is displayed based on, for example, an evaluation item or a scoring method, but is not limited to such examples. For example, the display unit displays the risk of the nuisance call as a rank such as "high", "medium", or "low". The display unit can also visually display the risk ranking using color coding or icons. The display unit can also update the risk ranking in real time. For example, the display unit displays the risk of the nuisance call as a color coding of red, yellow, or green. The icon displays a warning icon for a nuisance call with a high risk. The risk ranking, which is updated in real time, is displayed based on the latest nuisance call information. As a result, the spam / fraudulent call countermeasure system according to the embodiment can collect and learn information on nuisance calls, automatically respond to the calls, and display the risk ranking.

[0065] The collection unit collects information on nuisance calls. The information on nuisance calls includes, for example, telephone numbers, call contents, and caller information, but is not limited to such examples. For example, the collection unit registers the telephone numbers of nuisance calls in a database and analyzes the call contents. The collection unit can also collect caller information and identify patterns of nuisance calls. For example, the collection unit automatically detects telephone numbers of nuisance calls and registers them in a database. The call contents are converted into text data using voice recognition technology and analyzed. For caller information, attribute information such as the region and industry of the caller is collected, and the pattern of nuisance calls is identified. In order to efficiently collect this information, the collection unit uses a combination of multiple technologies. For example, to collect telephone numbers, the database of a communication carrier and reports from users are utilized. To analyze the call contents, not only voice recognition technology but also natural language processing technology is used to analyze the meaning of the call contents and extract the characteristics of nuisance calls. To collect caller information, public information on the Internet and corporate databases are utilized to obtain detailed information on the caller. This allows the collection unit to collect nuisance call information from multiple angles and register it in a database. Furthermore, the collection unit updates the collected information in real time, and can always maintain the latest nuisance call information. For example, newly reported nuisance call information can be instantly reflected in the database and made available to other departments. The collection unit can also enhance the effectiveness of nuisance call countermeasures by linking the collected information with other systems and services. For example, the collected nuisance call information can be shared with other security systems to realize comprehensive security measures. This enables the collection unit to collect nuisance call information efficiently and effectively, and improve the performance of the entire system.

[0066] The learning unit learns the patterns of nuisance calls based on the information collected by the collection unit. The learning is performed, for example, using a machine learning algorithm, but is not limited to such an example. For example, the learning unit uses a neural network to learn the patterns of nuisance calls. The learning unit can also learn the patterns of nuisance calls using a support vector machine. The learning unit can also learn the patterns of nuisance calls based on past nuisance call data. For example, the learning unit inputs past nuisance call data into a neural network to learn the patterns of nuisance calls. The support vector machine is used to classify the patterns of nuisance calls. The past nuisance call data includes information such as the call source and the contents of the call. The learning unit uses these data to extract the characteristics of the nuisance calls and identify the patterns. For example, the neural network analyzes the text data of the call contents and learns phrases and expressions specific to nuisance calls. The support vector machine classifies the patterns of nuisance calls based on the attribute information of the telephone number and the call source. This allows the learning unit to learn the patterns of nuisance calls with high accuracy and predict future nuisance calls. Furthermore, the learning unit can continuously update the learning results to respond to the latest nuisance call patterns. For example, the learning model can be retrained based on newly collected nuisance call information to improve accuracy. The learning unit can also use an anomaly detection algorithm to quickly detect nuisance call patterns that differ from normal. This allows the learning unit to quickly and accurately learn nuisance call patterns, improving the reliability and safety of the entire system.

[0067] The response unit automatically responds to the nuisance call based on the pattern learned by the learning unit. The automatic response is performed, for example, based on the selection method of the response phrase and the timing of the response, but is not limited to such examples. For example, the response unit automatically responds to the nuisance call using a fixed phrase. The response unit can also automatically respond using a response phrase that is customizable by the user. The response unit can also dynamically generate a response phrase based on the content of the nuisance call. For example, the response unit automatically responds to the nuisance call using a fixed phrase such as "This call has been determined to be a nuisance call. Please refrain from contacting us in the future." The response phrase that is customizable by the user is set within a range that the user can edit. The dynamically generated response phrase is generated in real time based on the content of the nuisance call. The response unit issues these response phrases at an appropriate time to effectively respond to the caller of the nuisance call. For example, if the content of the nuisance call is suspected to be fraudulent, the response unit automatically issues a warning phrase such as "This call is being recorded. Fraudulent acts are punishable by law." The response unit can also automatically block the nuisance call according to the user's settings. For example, calls from specific numbers or callers may be automatically rejected and the user may be notified. In addition, the response unit may record the response history of nuisance calls and provide the data for later analysis. This allows the response unit to respond to nuisance calls quickly and effectively, reducing the burden on the user.

[0068] The display unit displays a risk ranking on the incoming call screen based on the pattern learned by the learning unit. The risk ranking is displayed based on, for example, an evaluation item or a scoring method, but is not limited to such examples. For example, the display unit displays the risk of nuisance calls in ranks such as "high", "medium", and "low". The display unit can also visually display the risk ranking using color coding or icons. The display unit can also update the risk ranking in real time. For example, the display unit displays the risk of nuisance calls in red, yellow, and green. The icon displays a warning icon for nuisance calls with a high risk. The risk ranking, which is updated in real time, is displayed based on the latest nuisance call information. The display unit employs an intuitive interface to provide such information to the user in an easy-to-understand manner. For example, a large icon or a color-coded bar is displayed on the incoming call screen so that the user can determine the risk at a glance. The display unit can also display detailed information about the nuisance call. For example, the display unit displays the phone number, the area of ​​origin, an overview of the call content, and the like so that the user can check the content of the nuisance call. Furthermore, the display unit provides a function that allows the user to check the history of past nuisance calls. For example, a list of nuisance calls received in the past can be displayed, allowing the user to check the risk level and detailed information of each call. This allows the display unit to provide the user with information about nuisance calls in an easy-to-understand manner, and to support a quick response.

[0069] The response unit can automatically respond to nuisance calls using a response phrase that can be customized by the user. The response unit automatically responds to nuisance calls using, for example, a response phrase that can be customized by the user. For example, the response unit can set a response phrase within a range that can be edited by the user. The response unit can also provide a default response phrase, which can be customized by the user. For example, the response unit provides a default response phrase such as "This call has been determined to be a nuisance call. Please refrain from contacting us in the future." The user can edit this default response phrase and customize it to his or her liking. This allows for more flexible responses by using a response phrase customized by the user. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input a response phrase customized by the user to the AI, and the AI ​​can automatically respond to nuisance calls based on the response phrase.

[0070] The learning unit can update the database based on feedback from the user. The learning unit updates the database based on, for example, feedback from the user. For example, the learning unit collects evaluations and comments made by the user on nuisance calls and reflects them in the database. The learning unit can also update the database taking into consideration the timing of the feedback made by the user on the nuisance calls. For example, when the user evaluates a nuisance call as "very nuisance," "slightly nuisance," or "not nuisance," the learning unit reflects the evaluation in the database. The user's comments include information about the content and source of the nuisance call. The feedback is collected at an appropriate timing, such as immediately after the user receives a nuisance call or after a certain period of time. As a result, the accuracy of the database is improved by reflecting the user's feedback. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's feedback into the AI, and the AI ​​can update the database based on the feedback.

[0071] The display unit can visually display the danger ranking by using colors or icons. The display unit visually displays the danger ranking by using colors or icons, for example. For example, the display unit can display the danger of nuisance calls by using the colors red, yellow, and green. The display unit can also display a warning icon for a nuisance call with a high danger level. For example, the display unit displays a red warning icon for a nuisance call with a high danger level, a yellow warning icon for a nuisance call with a medium danger level, and a green warning icon for a nuisance call with a low danger level. This allows the user to intuitively grasp the danger level by visually displaying the danger level. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the danger level of a nuisance call into AI, and the AI ​​can display it using colors or icons based on the danger level.

[0072] The response unit may include a setting for automatically responding when the danger level is high. The response unit may include, for example, a setting for automatically responding when the danger level is high. For example, the response unit may automatically respond using a response phrase when the danger level of a nuisance call is determined to be high. The response unit may also perform an automatic response based on a danger level threshold set by the user. For example, the response unit may be set to automatically respond to nuisance calls evaluated as having a "high" danger level, and to manually respond to nuisance calls evaluated as having a "medium" or "low" danger level. This reduces the burden on the user by automatically responding when the danger level is high. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit may input the danger level of a nuisance call into AI, and the AI ​​may perform an automatic response based on the danger level.

[0073] The learning unit can evaluate the risk level based on the frequency and content of nuisance calls. The learning unit evaluates the risk level based on, for example, the frequency and content of nuisance calls. For example, the learning unit can evaluate the risk level based on the number of nuisance calls and keywords in the call content. The learning unit can also evaluate the risk level based on information on the source of the nuisance calls and past call history. For example, the learning unit evaluates the risk level high when the number of nuisance calls is large. The learning unit can also evaluate the risk level high when the call content includes keywords such as "fraud" and "solicitation". Furthermore, the learning unit can also evaluate the risk level high when the source of the nuisance calls is concentrated in a specific region or industry. As a result, by evaluating the risk level based on the frequency and content of nuisance calls, a more accurate risk evaluation is possible. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the frequency and content of nuisance calls into AI, and the AI ​​can evaluate the risk level based on the information.

[0074] The collection unit can estimate the user's emotions and adjust the timing of collecting information on nuisance calls based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting information on nuisance calls based on the estimated user's emotions. For example, when the user is stressed, the collection unit delays the collection of information on nuisance calls and collects it when the user is relaxed. In addition, when the user is relaxed, the collection unit can immediately collect information on nuisance calls and add it to the database. Furthermore, when the user is busy, the collection unit can postpone the collection of information on nuisance calls and collect it when the user is calm. This reduces the burden on the user by adjusting the information collection timing according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotional data to the generation AI, and the generation AI can adjust the timing of information collection based on the emotional data.

[0075] The collection unit can analyze the user's past call history and select the optimal collection method when collecting information on nuisance calls. For example, the collection unit analyzes the user's past call history and selects the optimal collection method when collecting information on nuisance calls. For example, the collection unit analyzes the pattern of nuisance calls received by the user in the past, and preferentially collects calls with a similar pattern. In addition, if the collection unit determines from the user's call history that many nuisance calls are made during a specific time period, it can also concentrate information collection during that time period. Furthermore, the collection unit can also preferentially collect calls from a specific caller based on the user's call history. This makes it possible to select the optimal information collection method by analyzing the past call history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past call history into AI, and the AI ​​can select the optimal collection method based on the call history.

[0076] The collection unit can perform filtering based on the user's current location information and time zone when collecting information on nuisance calls. For example, the collection unit performs filtering based on the user's current location information and time zone when collecting information on nuisance calls. For example, when the user is in a specific area, the collection unit preferentially collects information on nuisance calls that occur frequently in that area. In addition, when the user receives a nuisance call at night, the collection unit can also collect information on nuisance calls that occur frequently at night. Furthermore, when the user is moving, the collection unit can also collect information on nuisance calls that occur frequently at the user's destination based on the current location information. This makes it possible to collect information more effectively by filtering based on the current location information and time zone. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current location information and time zone into AI, and the AI ​​can perform filtering based on the information.

[0077] The collection unit can estimate the user's emotion and determine the priority of the nuisance call information to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of the nuisance call information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit postpones nuisance call information of low importance in order to reduce stress. In addition, when the user is relaxed, the collection unit can also collect all nuisance call information evenly. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect nuisance call information of high importance. This reduces the burden on the user by determining the priority of information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of information based on the emotion data.

[0078] The collection unit can analyze the social media activity of the user when collecting nuisance call information, and collect related nuisance call information. For example, when collecting nuisance call information, the collection unit can analyze the social media activity of the user and collect related nuisance call information. For example, when the user posts on social media about nuisance calls, the collection unit can collect related nuisance call information based on the content. In addition, when a social media friend of the user posts on nuisance calls, the collection unit can also collect nuisance call information based on the information. Furthermore, when the user shares information about a specific nuisance call on social media, the collection unit can also collect nuisance call information based on the information. In this way, by analyzing social media activity, related nuisance call information can be effectively collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI, and the AI ​​can collect related nuisance call information based on the activity.

[0079] The collection unit can refer to the user's contact list when collecting information on nuisance calls and preferentially collect known nuisance phone numbers. For example, the collection unit refers to the user's contact list when collecting information on nuisance calls and preferentially collects known nuisance phone numbers. For example, the collection unit preferentially collects nuisance phone numbers that match numbers registered in the user's contact list. The collection unit can also preferentially collect nuisance calls from numbers registered in the user's contact list. Furthermore, the collection unit can also preferentially collect nuisance phone numbers similar to numbers registered in the user's contact list. This allows known nuisance phone numbers to be preferentially collected by referring to the contact list. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit inputs the user's contact list into the AI, and the AI ​​can preferentially collect known nuisance phone numbers based on the list.

[0080] The learning unit can estimate the user's emotion and select learning data based on the estimated user's emotion. The learning unit, for example, estimates the user's emotion and selects learning data based on the estimated user's emotion. For example, when the user is feeling stressed, the learning unit postpones learning data with low importance in order to reduce stress. In addition, when the user is relaxed, the learning unit can select all learning data equally. Furthermore, when the user is in a hurry, the learning unit can preferentially select learning data with high importance. Thereby, by selecting learning data according to the user's emotion, learning efficiency is improved. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit inputs the user's emotion data to the generation AI, and the generation AI can select learning data based on the emotion data.

[0081] The learning unit can optimize the learning algorithm by referring to past nuisance call data during learning. The learning unit can optimize the learning algorithm by referring to past nuisance call data during learning, for example. For example, the learning unit learns nuisance call patterns based on past nuisance call data and optimizes the algorithm. The learning unit can also learn patterns of specific call sources from past nuisance call data and optimize the algorithm. Furthermore, the learning unit can analyze past nuisance call data, discover new nuisance call patterns, and optimize the algorithm. In this way, the learning algorithm can be optimized by referring to past nuisance call data. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input past nuisance call data into AI, and the AI ​​can optimize the learning algorithm based on the data.

[0082] The learning unit can learn based on attribute information of the caller of the nuisance call during learning. The learning unit, for example, learns based on attribute information of the caller of the nuisance call during learning. For example, the learning unit learns nuisance call patterns for each region based on regional information of the caller of the nuisance call. The learning unit can also learn nuisance call patterns for each industry based on industry information of the caller of the nuisance call. Furthermore, the learning unit can also learn nuisance call patterns for each time period based on time period information of the caller of the nuisance call. This makes it possible to learn more accurately by taking into account the attribute information of the caller. Part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input attribute information of the caller of the nuisance call to AI, and the AI ​​can learn based on that information.

[0083] The learning unit can estimate the user's emotion and adjust the frequency of learning based on the estimated user's emotion. The learning unit, for example, estimates the user's emotion and adjusts the frequency of learning based on the estimated user's emotion. For example, when the user is stressed, the learning unit reduces the frequency of learning to reduce the burden on the user. In addition, when the user is relaxed, the learning unit can also increase the frequency of learning to efficiently perform learning. Furthermore, when the user is in a hurry, the learning unit can adjust the frequency of learning and prioritize learning important data. In this way, the burden on the user is reduced by adjusting the frequency of learning according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data to the generation AI, and the generation AI can adjust the frequency of learning based on the emotion data.

[0084] The learning unit can weight the learning data based on the time zone of the nuisance call during learning. The learning unit, for example, weights the learning data based on the time zone of the nuisance call during learning. For example, the learning unit weights data of time zones where nuisance calls are frequent to strengthen learning. The learning unit can also lightly weight data of time zones where nuisance calls are few to make learning more efficient. Furthermore, the learning unit can weight data of nuisance calls that are concentrated in a specific time zone to optimize learning. As a result, weighting the learning data based on the time zone of the call improves the efficiency of learning. Part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time zone of the nuisance call to AI, and the AI ​​can weight the learning data based on that time zone.

[0085] The learning unit can perform text analysis of the contents of the nuisance call during learning and perform learning based on specific keywords. For example, the learning unit performs text analysis of the contents of the nuisance call during learning and performs learning based on specific keywords. For example, the learning unit performs text analysis of the contents of the nuisance call and performs learning based on specific keywords (e.g., fraud, solicitation). The learning unit can also extract frequently occurring keywords from the contents of the nuisance call and use them as learning data. Furthermore, the learning unit can analyze the contents of the nuisance call, discover new keywords, and add them to the learning data. This makes it possible to perform learning based on text analysis and perform more accurate learning. Part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the contents of the nuisance call into AI, and the AI ​​can extract specific keywords based on the contents and perform learning.

[0086] The response unit can estimate the user's emotion and select a response phrase based on the estimated user's emotion. The response unit, for example, estimates the user's emotion and selects a response phrase based on the estimated user's emotion. For example, when the user is stressed, the response unit selects a gentle response phrase to reduce stress. In addition, when the user is relaxed, the response unit can also select a standard response phrase. Furthermore, when the user is in a hurry, the response unit can also select a short response phrase for a quick response. This enables a more appropriate response by selecting a response phrase according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit inputs the user's emotion data to the generation AI, and the generation AI can select a response phrase based on the emotion data.

[0087] The response unit can dynamically generate a response phrase based on the contents of the nuisance call when responding. The response unit dynamically generates a response phrase based on the contents of the nuisance call when responding, for example. For example, the response unit analyzes the contents of the nuisance call and generates a response phrase including a warning if there is a possibility of fraud. The response unit can also generate a refusal response phrase if there is a possibility of solicitation from the contents of the nuisance call. Furthermore, the response unit can generate a response phrase corresponding to a specific keyword based on the contents of the nuisance call. This allows for more flexible responses by generating a response phrase based on the contents of the nuisance call. Part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the contents of the nuisance call into AI, and the AI ​​can dynamically generate a response phrase based on the contents.

[0088] The response unit can respond by considering the attribute information of the caller of the nuisance call when responding. The response unit, for example, responds by considering the attribute information of the caller of the nuisance call when responding. For example, if the caller of the nuisance call is from a specific area, the response unit uses a response phrase specialized for that area. In addition, if the caller of the nuisance call is from a specific industry, the response unit can also use a response phrase specialized for that industry. Furthermore, the response unit can select an appropriate response phrase based on time zone information of the caller of the nuisance call. This makes it possible to respond more appropriately by considering the attribute information of the caller. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input attribute information of the caller of the nuisance call to AI, and the AI ​​can select a response phrase based on that information.

[0089] The response unit can estimate the user's emotion and determine the priority of responses based on the estimated user's emotion. The response unit, for example, estimates the user's emotion and determines the priority of responses based on the estimated user's emotion. For example, when the user is feeling stressed, the response unit postpones responses with low importance in order to reduce stress. The response unit can also process all responses equally when the user is relaxed. Furthermore, when the user is in a hurry, the response unit can also process responses with high importance preferentially. This reduces the burden on the user by determining the priority of responses according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of responses based on the emotion data.

[0090] The response unit can select a response phrase based on the geographical location information of the caller of the nuisance call when responding. For example, the response unit selects a response phrase based on the geographical location information of the caller of the nuisance call when responding. For example, when the caller of the nuisance call is from a specific region, the response unit uses a response phrase specialized for that region. In addition, when the caller of the nuisance call is from a specific country, the response unit can also generate a response phrase in the language of that country. Furthermore, the response unit can select an appropriate response phrase based on the geographical location information of the caller of the nuisance call. In this way, by taking into account the geographical location information of the caller, a more appropriate response phrase can be selected. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the geographical location information of the caller of the nuisance call to AI, and the AI ​​can select a response phrase based on that information.

[0091] The response unit can perform text analysis of the contents of the nuisance call at the time of response and select a response phrase based on a specific keyword. For example, the response unit performs text analysis of the contents of the nuisance call at the time of response and selects a response phrase based on a specific keyword. For example, the response unit performs text analysis of the contents of the nuisance call and selects a response phrase including a warning if there is a possibility of fraud. In addition, the response unit can select a refusal response phrase if there is a possibility of solicitation from the contents of the nuisance call. Furthermore, the response unit can select a response phrase corresponding to a specific keyword based on the contents of the nuisance call. This makes it possible to respond more appropriately by selecting a response phrase based on text analysis. Part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the contents of the nuisance call into AI, and the AI ​​can extract a specific keyword based on the contents and select a response phrase.

[0092] The display unit can estimate the user's emotion and adjust the display method of the danger ranking based on the estimated user's emotion. The display unit, for example, estimates the user's emotion and adjusts the display method of the danger ranking based on the estimated user's emotion. For example, the display unit provides a simple and highly visible display method when the user is stressed. In addition, the display unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the display unit can also provide a display method that focuses on the main points when the user is in a hurry. This reduces the burden on the user by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the generation AI, and the generation AI can adjust the display method based on the emotion data.

[0093] The display unit can optimize the display algorithm by referring to past nuisance call data when displaying the risk ranking. The display unit optimizes the display algorithm by referring to past nuisance call data when displaying the risk ranking, for example. For example, the display unit optimizes the display algorithm of the risk ranking based on the past nuisance call data. The display unit can also evaluate the risk of a specific caller from the past nuisance call data and optimize the display algorithm. Furthermore, the display unit can analyze the past nuisance call data, introduce new risk evaluation criteria, and optimize the display algorithm. In this way, the display algorithm can be optimized by referring to the past nuisance call data. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the past nuisance call data into AI, and the AI ​​can optimize the display algorithm based on the data.

[0094] The display unit can perform display based on attribute information of the caller of the nuisance call when displaying the risk ranking. The display unit performs display based on attribute information of the caller of the nuisance call when displaying the risk ranking. For example, the display unit displays a risk ranking for each region based on regional information of the caller of the nuisance call. The display unit can also display a risk ranking for each industry based on industry information of the caller of the nuisance call. Furthermore, the display unit can display a risk ranking for each time period based on time period information of the caller of the nuisance call. In this way, by taking into account the attribute information of the caller, a more accurate risk ranking can be displayed. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input attribute information of the caller of the nuisance call to AI, and the AI ​​can perform display based on that information.

[0095] The display unit can estimate the user's emotion and determine the priority of the danger ranking based on the estimated user's emotion. The display unit, for example, estimates the user's emotion and determines the priority of the danger ranking based on the estimated user's emotion. For example, when the user is feeling stressed, the display unit postpones the danger ranking with a low importance in order to reduce the stress. In addition, when the user is relaxed, the display unit can also display all the danger rankings evenly. Furthermore, when the user is in a hurry, the display unit can also display the danger ranking with a high importance preferentially. In this way, the priority is determined according to the user's emotion, thereby reducing the burden on the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority based on the emotion data.

[0096] The display unit can weight the display data based on the time zone of the nuisance call when displaying the risk ranking. The display unit, for example, weights the display data based on the time zone of the nuisance call when displaying the risk ranking. For example, the display unit weights data of time zones where nuisance calls are frequent to strengthen the display. The display unit can also lightly weight data of time zones where nuisance calls are few to make the display more efficient. Furthermore, the display unit can weight data of nuisance calls that are concentrated in a specific time zone to optimize the display. Thus, by weighting the display data based on the time zone of the call, the efficiency of the display is improved. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the time zone of the nuisance call to AI, and the AI ​​can weight the display data based on the time zone.

[0097] The display unit can perform text analysis of the contents of the nuisance call and display based on a specific keyword when displaying the risk ranking. For example, the display unit can perform text analysis of the contents of the nuisance call and display based on a specific keyword when displaying the risk ranking. For example, the display unit can perform text analysis of the contents of the nuisance call and display including a warning if there is a possibility of fraud. In addition, the display unit can also display a refusal if there is a possibility of solicitation from the contents of the nuisance call. Furthermore, the display unit can also display corresponding to a specific keyword based on the contents of the nuisance call. As a result, a more accurate risk ranking can be displayed by displaying based on the text analysis. Part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the contents of the nuisance call into AI, and the AI ​​can extract specific keywords based on the contents and display them.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The collection unit can analyze the user's call history and identify patterns of nuisance calls. For example, the collection unit can analyze information on the time periods and call sources of nuisance calls received by the user in the past, and preferentially collect calls with similar patterns. The collection unit can also extract calls containing specific keywords from the user's call history, and identify patterns of nuisance calls based on that information. Furthermore, the collection unit can preferentially collect nuisance calls from specific regions or industries based on the user's call history. This makes it possible to take more effective measures against nuisance calls by utilizing the user's call history.

[0100] The response unit can dynamically generate a response phrase based on the content of the nuisance call. For example, the response unit analyzes the content of the nuisance call and generates a response phrase including a warning if there is a possibility of fraud. The response unit can also generate a refusal response phrase if there is a possibility of solicitation based on the content of the nuisance call. Furthermore, the response unit can generate a response phrase corresponding to a specific keyword based on the content of the nuisance call. This allows for more flexible responses by generating response phrases based on the content of the nuisance call.

[0101] The learning unit can perform learning based on attribute information of the caller of the nuisance call. For example, the learning unit learns nuisance call patterns for each region based on regional information of the caller of the nuisance call. The learning unit can also learn nuisance call patterns for each industry based on industry information of the caller of the nuisance call. Furthermore, the learning unit can learn nuisance call patterns for each time period based on time period information of the caller of the nuisance call. This allows for more accurate learning by taking into account the caller's attribute information.

[0102] When displaying the risk rankings, the display unit can perform the display based on attribute information of the callers of the nuisance calls. For example, the display unit can display a risk ranking for each region based on regional information of the callers of the nuisance calls. The display unit can also display a risk ranking for each industry based on industry information of the callers of the nuisance calls. Furthermore, the display unit can display a risk ranking for each time period based on time period information of the callers of the nuisance calls. This allows a more accurate risk ranking to be displayed by taking into account the caller's attribute information.

[0103] When collecting information on nuisance calls, the collection unit can analyze the user's social media activity and collect related nuisance call information. For example, if a user posts on social media about nuisance calls, the collection unit can collect related nuisance call information based on the content of the post. In addition, if a user's social media friend posts about nuisance calls, the collection unit can also collect nuisance call information based on that information. Furthermore, if a user shares information about a specific nuisance call on social media, the collection unit can also collect nuisance call information based on that information. This makes it possible to effectively collect related nuisance call information by analyzing social media activity.

[0104] The collection unit can estimate the user's emotions and adjust the timing of collecting information on nuisance calls based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can delay collecting information on nuisance calls and collect it when the user is relaxed. Also, if the user is relaxed, the collection unit can instantly collect information on nuisance calls and add it to the database. Furthermore, if the user is busy, the collection unit can postpone collecting information on nuisance calls and collect it when the user is calm. In this way, the burden on the user is reduced by adjusting the timing of information collection according to the user's emotions.

[0105] The learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. For example, when the user is feeling stressed, the learning unit postpones learning data with low importance in order to reduce stress. Also, when the user is relaxed, the learning unit can select all learning data equally. Furthermore, when the user is in a hurry, the learning unit can preferentially select learning data with high importance. In this way, the efficiency of learning is improved by selecting learning data according to the user's emotions.

[0106] The response unit can estimate the user's emotion and select a response phrase based on the estimated user's emotion. For example, if the user is feeling stressed, the response unit can select a gentle response phrase to reduce the stress. Also, if the user is relaxed, the response unit can select a standard response phrase. Furthermore, if the user is in a hurry, the response unit can select a short response phrase to respond quickly. This allows for a more appropriate response by selecting a response phrase according to the user's emotion.

[0107] The display unit can estimate the user's emotions and adjust the display method of the risk ranking based on the estimated user's emotions. For example, when the user is feeling stressed, the display unit provides a simple and highly visible display method. When the user is relaxed, the display unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the display unit can also provide a display method that focuses on the main points. In this way, the burden on the user is reduced by adjusting the display method according to the user's emotions.

[0108] The display unit can estimate the user's emotions and determine the priority of the danger rankings based on the estimated user's emotions. For example, when the user is feeling stressed, the display unit postpones risk rankings with low importance in order to reduce stress. Also, when the user is relaxed, the display unit can display all risk rankings evenly. Furthermore, when the user is in a hurry, the display unit can preferentially display risk rankings with high importance. In this way, the burden on the user is reduced by determining the priority according to the user's emotions.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects information on nuisance calls. Information on nuisance calls includes, for example, telephone numbers, call content, and caller information. The collection unit registers the telephone numbers of nuisance calls in a database and analyzes the content of the calls. It can also collect caller information and identify patterns of nuisance calls. For example, the collection unit automatically detects telephone numbers of nuisance calls and registers them in a database. The content of the calls is converted into text data using voice recognition technology and analyzed. Caller information is collected from attribute information such as the region and industry of the caller, and nuisance call patterns are identified. Step 2: The learning unit learns nuisance call patterns based on the information collected by the collection unit. Learning is performed, for example, using a machine learning algorithm. For example, the learning unit uses a neural network or a support vector machine to learn nuisance call patterns. It is also possible to learn nuisance call patterns based on past nuisance call data. Past nuisance call data includes information such as the source of the nuisance call and the content of the call. Step 3: The response unit automatically responds to the nuisance call based on the pattern learned by the learning unit. The automatic response is performed based on, for example, the method of selecting a response phrase and the timing of the response. For example, the response unit automatically responds to the nuisance call using a fixed phrase. It is also possible to automatically respond using a response phrase that can be customized by the user. Furthermore, it is also possible to dynamically generate a response phrase based on the content of the nuisance call. Step 4: The display unit displays a risk ranking on the incoming call screen based on the patterns learned by the learning unit. The risk ranking is displayed based on, for example, evaluation items or a scoring method. For example, the display unit displays the risk of nuisance calls as a rank such as "high," "medium," or "low." The risk ranking can also be displayed visually using colors or icons. Furthermore, the risk ranking can be updated in real time.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0112] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the collection unit, learning unit, response unit, and display unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on nuisance calls using the camera 42 and microphone 38B of the smart device 14, and registers the information in a database by the control unit 46A. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the pattern of nuisance calls based on the collected information. The response unit is realized, for example, by the control unit 46A of the smart device 14, and automatically responds to nuisance calls based on the learned pattern. The display unit is realized, for example, by the display 40A of the smart device 14, and displays a danger ranking on the incoming call screen. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0118] 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.

[0119] 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 the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] 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 performed in a secure state.

[0122] Fig. 4 shows an example of 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.

[0123] 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.

[0124] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0128] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, learning unit, response unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information on nuisance calls using the camera 42 and microphone 238 of the smart glasses 214, and registers the information in a database by the control unit 46A. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the pattern of nuisance calls based on the collected information. The response unit is realized, for example, by the control unit 46A of the smart glasses 214, and automatically responds to nuisance calls based on the learned pattern. The display unit is realized, for example, by the display of the smart glasses 214, and displays a risk ranking on the incoming call screen. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0134] 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.

[0135] 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 the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] 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 performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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.

[0140] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0142] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 voice indicating a user input for 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.

[0144] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, learning unit, response unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information on nuisance calls using the camera 42 and microphone 238 of the headset type terminal 314, and registers the information in a database by the control unit 46A. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the pattern of nuisance calls based on the collected information. The response unit is realized, for example, by the control unit 46A of the headset type terminal 314, and automatically responds to nuisance calls based on the learned pattern. The display unit is realized, for example, by the display 343 of the headset type terminal 314, and displays a danger ranking on the incoming call screen. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0150] 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.

[0151] 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 the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[0156] 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.

[0157] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 a voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0163] Each of the multiple elements including the above-mentioned collection unit, learning unit, response unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on nuisance calls using the camera 42 and microphone 238 of the robot 414, and registers the information in a database by the control unit 46A. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the pattern of nuisance calls based on the collected information. The response unit is realized, for example, by the control unit 46A of the robot 414, and automatically responds to nuisance calls based on the learned pattern. The display unit is realized, for example, by the display of the robot 414, and displays a danger ranking on the incoming call screen. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] 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 Universal Serial Bus (USB) 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.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0177] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection department that collects information on nuisance calls; a learning unit that learns patterns of nuisance calls based on the information collected by the collecting unit; a response unit that automatically responds to nuisance calls based on the patterns learned by the learning unit; a display unit that displays a risk ranking on an incoming call screen based on the pattern learned by the learning unit. A system characterized by: (Appendix 2) The response unit: Automatically respond to nuisance calls with user-customizable response phrases 2. The system of claim 1, (Appendix 3) The learning unit is Update the database based on user feedback 2. The system of claim 1, (Appendix 4) The display unit is Specify the specific method for visually displaying the risk ranking using colors or icons. 2. The system of claim 1, (Appendix 5) The response unit: Includes settings for automatic response in high-risk cases 2. The system of claim 1, (Appendix 6) The learning unit is Evaluate risk based on frequency and content of nuisance calls 2. The system of claim 1, (Appendix 7) The collecting unit includes: We present a method to estimate user emotions and specifically adjust the timing of collecting information on nuisance calls based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting information on nuisance calls, analyze the user's past call history and select the appropriate collection method. 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting information on nuisance calls, filtering is performed based on the user's current location and time of day. 2. The system of claim 1, (Appendix 10) The collecting unit includes: We present a method for predicting user emotions and determining the priority of nuisance call information to be collected based on the predicted user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting information on nuisance calls, the social media activity of users is analyzed to collect relevant nuisance call information. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting information on nuisance calls, the system refers to the user's contact list and prioritizes collection of known nuisance phone numbers. 2. The system of claim 1, (Appendix 13) The learning unit is We present a method to estimate user emotions and select learning data based on the estimated user emotions. 2. The system of claim 1, (Appendix 14) The learning unit is During learning, the learning algorithm is optimized by referring to past nuisance call data. 2. The system of claim 1, (Appendix 15) The learning unit is When learning, the system learns based on attribute information of the source of nuisance calls. 2. The system of claim 1, (Appendix 16) The learning unit is We present a method for predicting user emotions and specifically adjusting the frequency of learning based on the predicted user emotions. 2. The system of claim 1, (Appendix 17) The learning unit is During training, the training data is weighted based on the time of day that nuisance calls are made 2. The system of claim 1, (Appendix 18) The learning unit is During learning, the content of nuisance calls is text-analyzed and learning is performed based on specific keywords. 2. The system of claim 1, (Appendix 19) The response unit: We present a method for estimating a user's emotions and selecting a response phrase based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The response unit: When answering, dynamically generate a response phrase based on the content of the unwanted call 2. The system of claim 1, (Appendix 21) The response unit: When answering, respond based on attribute information of the nuisance caller 2. The system of claim 1, (Appendix 22) The response unit: We demonstrate how to estimate user emotions and specifically prioritize responses based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The response unit: When answering, select a response phrase based on the geographic location of the spam call originating from 2. The system of claim 1, (Appendix 24) The response unit: When answering, the system performs text analysis of the spam call and selects a response phrase based on specific keywords. 2. The system of claim 1, (Appendix 25) The display unit is We present a method for estimating user emotions and specifically adjusting the display method of risk rankings based on the estimated user emotions. 2. The system of claim 1, (Appendix 26) The display unit is When displaying risk rankings, the display algorithm is optimized by referencing past nuisance call data. 2. The system of claim 1, (Appendix 27) The display unit is When displaying risk rankings, the display will be based on attribute information of the caller of the nuisance call 2. The system of claim 1, (Appendix 28) The display unit is We present a method for estimating user emotions and determining the priority of risk rankings based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The display unit is When displaying risk rankings, the displayed data is weighted based on the time period in which the nuisance calls were made. 2. The system of claim 1, (Appendix 30) The display unit is When displaying the risk ranking, the content of the nuisance call is analyzed and displayed based on specific keywords. 2. The system of claim 1, [Explanation of symbols]

[0183] 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 collection department that collects information on nuisance calls; a learning unit that learns patterns of nuisance calls based on the information collected by the collecting unit; a response unit that automatically responds to nuisance calls based on the patterns learned by the learning unit; a display unit that displays a risk ranking on an incoming call screen based on the pattern learned by the learning unit. A system characterized by:

2. The response unit: Automatically respond to nuisance calls with user-customizable response phrases 2. The system of claim 1 .

3. The learning unit is Update the database based on user feedback 2. The system of claim 1 .

4. The display unit is Specify the specific method for visually displaying the risk ranking using colors or icons.

2. The system of claim 1 .

5. The response unit: Includes settings for automatic response in high-risk cases 2. The system of claim 1 .

6. The learning unit is Evaluate risk based on frequency and content of nuisance calls 2. The system of claim 1 .

7. The collecting unit includes: We present a method to estimate user emotions and specifically adjust the timing of collecting information on nuisance calls based on the estimated user emotions.

2. The system of claim 1 .

8. The collecting unit includes: When collecting information on nuisance calls, analyze the user's past call history and select the appropriate collection method.

2. The system of claim 1 .

9. The collecting unit includes: When collecting information on nuisance calls, filtering is performed based on the user's current location and time of day.

2. The system of claim 1 .

10. The collecting unit includes: We present a method for predicting user emotions and determining the priority of nuisance call information to be collected based on the predicted user emotions.

2. The system of claim 1 .

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