system

Abstract

The system according to the embodiment aims to provide an effective measure against nuisance calls and fraudulent calls. [Solution] The system according to the embodiment comprises a response unit, a collection unit, an operation unit, and a display unit. The response unit automatically responds to nuisance calls. The collection unit collects information on nuisance calls based on the content of the response provided by the response unit. The operation unit analyzes the information collected by the collection unit and operates a database of nuisance calls. The display unit displays a risk ranking on the incoming call screen based on the database operated by the operation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient countermeasures against nuisance calls and fraudulent calls, and users may be harmed.

[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 comprises a response unit, a collection unit, an operation unit, and a display unit. The response unit automatically responds to nuisance calls. The collection unit collects information on nuisance calls based on the content of the response provided by the response unit. The operation unit analyzes the information collected by the collection unit and operates a database of nuisance calls. The display unit displays a risk ranking on the incoming call screen based on the database operated by the operation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an effective measure against nuisance calls and fraudulent calls. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The spam and fraud call prevention system of an embodiment of the present invention uses a generation AI to deal with nuisance calls. When a nuisance call is received, the system activates an automatic response function, responding with a fixed phrase. For example, the generation AI automatically selects and responds with phrases such as "I'm away from my desk right now. Please call back later" or "I'm not available at the moment. Please leave a message." This eliminates the need for users to respond directly to nuisance calls. Next, a nuisance call prevention database is operated. This database stores information on previously reported nuisance calls, which the generation AI references to identify and respond to nuisance calls. A system is provided for users to report nuisance calls, and the reported data is analyzed by the generation AI and stored in the database. This allows for more accurate identification and response of nuisance calls. Furthermore, a risk ranking is displayed on the incoming call screen. The generation AI references a nuisance call database and evaluates the risk level of the incoming number. For example, if a call comes from a number with many past reports, it is deemed high risk, and the incoming call screen displays "Risk: High," "Risk: Medium," or "Risk: Low." The evaluation criteria take into account the number of reports and the seriousness of the reports. This allows users to check the risk level of incoming calls at a glance. This service protects users from nuisance and fraudulent calls, allowing them to use the phone with peace of mind. This enables the spam and fraud call prevention system to automatically respond to nuisance calls, collect information, operate a database, and display risk rankings.

[0029] A spam and fraud call prevention system according to an embodiment includes a response unit, a collection unit, an operation unit, and a display unit. The response unit automatically responds to nuisance calls. For example, the response unit uses a generation AI to select a fixed phrase and respond automatically. The generation AI uses a text generation AI (e.g., LLM) to generate an appropriate phrase for nuisance calls. For example, the response unit automatically selects and responds with a phrase such as "I'm away from my desk right now. Please call back later" or "I'm not currently available. Please leave a message." The collection unit collects information about nuisance calls based on the content of the response provided by the response unit. For example, the collection unit collects information about nuisance calls reported by users. The collection unit uses the generation AI to analyze the information about nuisance calls reported by users and store it in a database. The operation unit analyzes the information collected by the collection unit and operates a nuisance call database. For example, the operation unit analyzes the collected information about nuisance calls and stores it in a database. The operations unit uses a generation AI to analyze the collected nuisance call information and store it in a database. The display unit displays a risk ranking on the incoming call screen based on the database operated by the operations unit. For example, the display unit evaluates the risk of incoming numbers based on past report data and displays "Risk: High," "Risk: Medium," or "Risk: Low" on the incoming call screen. This enables the spam and fraud call prevention system of the embodiment to automatically respond to nuisance calls, collect information, operate a database, and display risk rankings.

[0030] The response unit can select a fixed phrase using the generation AI and perform an automatic response. The response unit, for example, uses the generation AI to select a fixed phrase and perform an automatic response. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate an appropriate phrase for a nuisance call. For example, the response unit automatically selects and responds with a phrase such as "I am currently away from my desk. Please call back later" or "I am not currently available. Please leave a message." This enables automatic responses to nuisance calls by having the generation AI select a fixed phrase. Some or all of the above-described processing in the response unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the response unit can input a prompt to the generation AI, such as "Please generate an appropriate phrase for a nuisance call," and perform an automatic response using the phrase generated by the generation AI.

[0031] The collection unit can collect information on nuisance calls reported by users. For example, the collection unit collects information on nuisance calls reported by users. The collection unit uses a generation AI to analyze the information on nuisance calls reported by users and store it in a database. For example, the collection unit has a mechanism whereby users provide information on nuisance calls through a reporting form. The information reported by users includes the content of the call, the call origin number, the duration of the call, etc. The collection unit analyzes this information and uses it to identify nuisance calls. In this way, collecting information on nuisance calls reported by users improves the accuracy of the database. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input information on nuisance calls reported by users into the generation AI and store the results of the analysis by the generation AI in a database.

[0032] The operations department can analyze the collected information on nuisance calls and store it in a database. The operations department, for example, analyzes the collected information on nuisance calls and stores it in a database. The operations department uses a generation AI to analyze the collected information on nuisance calls and store it in a database. For example, the operations department uses text mining technology to analyze the content of nuisance calls. The operations department uses a machine learning algorithm to identify patterns of nuisance calls and store them in a database. In this way, by analyzing the collected information on nuisance calls and storing it in a database, the accuracy of identifying nuisance calls is improved. Some or all of the above-mentioned processing in the operations department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the collected information on nuisance calls into a generation AI and store the results of the generation AI's analysis in a database.

[0033] The display unit can evaluate the risk level of the incoming call number based on past report data and display "Risk Level: High," "Risk Level: Medium," or "Risk Level: Low" on the incoming call screen. For example, the display unit evaluates the risk level of the incoming call number based on past report data and displays "Risk Level: High," "Risk Level: Medium," or "Risk Level: Low" on the incoming call screen. The display unit uses a generation AI to analyze past report data and evaluate the risk level of the incoming call number. For example, if the call is from a phone number that has received many past reports, the display unit determines that the risk level is high and displays "Risk Level: High" on the incoming call screen. The display unit evaluates the risk level taking into account the number of reports, the seriousness of the report content, etc. In this way, by evaluating the risk level of the incoming call number based on past report data and displaying it on the incoming call screen, the user can check the risk level of a nuisance call at a glance. Some or all of the above-mentioned processing in the display unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the display unit can input past report data into the generation AI and display the risk level evaluated by the generation AI on the incoming call screen.

[0034] The response unit can generate different fixed phrases depending on the type of nuisance call. For example, the response unit generates different fixed phrases depending on the type of nuisance call. The response unit uses a generation AI to identify the type of nuisance call and generate an appropriate phrase. For example, in the case of a sales call, the generation AI generates a phrase such as "We are currently not accepting sales calls." In the case of a fraudulent call, the generation AI generates a warning phrase such as "This call may be fraudulent. Please be careful." In the case of a survey, the generation AI generates a phrase such as "You are currently not able to respond to the survey." This allows for more effective responses by generating different fixed phrases depending on the type of nuisance call. Some or all of the above-described processing in the response unit may be performed using, or without, the generation AI. For example, the response unit can input the type of nuisance call into the generation AI and respond using a phrase generated by the generation AI.

[0035] When selecting a response phrase, the response unit can refer to past response history to select an appropriate phrase. For example, when selecting a response phrase, the response unit refers to past response history to select the optimal phrase. The response unit uses a generation AI to analyze past response history and select an appropriate phrase. For example, if there has been a nuisance call from the same number in the past, the response unit has the generation AI reuse the response phrase used at that time. In the response unit, the generation AI selects the most effective phrase from the past response history. The response unit can also analyze the past response history and have the generation AI suggest a new phrase. In this way, the optimal phrase can be selected by referring to the past response history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the response unit can input past response history into the generation AI and select an appropriate phrase based on the analysis results of the generation AI.

[0036] The response unit can use region-specific phrases by taking into account the user's geographical location information when selecting a response phrase. For example, the response unit can use region-specific phrases by taking into account the user's geographical location information when selecting a response phrase. The response unit uses a generation AI to obtain the user's geographical location information and select an appropriate phrase. For example, if the user is in Tokyo, the generation AI can use a phrase such as "I'm currently in Tokyo, so please call me back later." If the user is in Osaka, the generation AI can use a phrase such as "I'm currently in Osaka, so please call me back later." If the user is in Hokkaido, the generation AI can use a phrase such as "I'm currently in Hokkaido, so please call me back later." This allows region-specific phrases to be used by taking into account the user's geographical location information. Some or all of the above-described processing in the response unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the response unit can input the user's geographical location information into the generation AI and use region-specific phrases selected by the generation AI.

[0037] The response unit can analyze the user's social media activity and use relevant phrases when selecting a response phrase. For example, the response unit can analyze the user's social media activity and use relevant phrases when selecting a response phrase. The response unit uses a generation AI to analyze the user's social media activity and select appropriate phrases. For example, if the user has recently posted about their travels on social media, the generation AI can use a phrase such as "I'm currently traveling, please call me back later." If the user has recently posted about their work on social media, the generation AI can use a phrase such as "I'm currently at work, please call me back later." If the user has recently posted about their family on social media, the generation AI can use a phrase such as "I'm currently spending time with my family, please call me back later." This allows relevant phrases to be used by analyzing the user's social media activity. Some or all of the above-described processing in the response unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the response unit can input the user's social media activity into the generation AI and use the phrases selected by the generation AI.

[0038] The collection unit can analyze the source information of nuisance calls and collect detailed data during collection. For example, the collection unit analyzes the source information of nuisance calls and collects detailed data during collection. The collection unit analyzes the source information of nuisance calls using a generation AI. For example, the collection unit analyzes the phone number of the source of the nuisance call and collects regional information of the source. The collection unit analyzes the business information of the source of the nuisance call and collects detailed data about the business. The collection unit analyzes the call history of the source of the nuisance call and collects past call data. In this way, detailed data can be collected by analyzing the source information of the nuisance call. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the source information of the nuisance call into a generation AI and collect detailed data based on the analysis results of the generation AI.

[0039] The collection unit can prioritize collecting region-specific nuisance call information by taking into account the user's geographical location information when collecting. For example, the collection unit prioritizes collecting region-specific nuisance call information by taking into account the user's geographical location information when collecting. The collection unit uses the generation AI to obtain the user's geographical location information and collect appropriate nuisance call information. For example, if the user is in Tokyo, the collection unit prioritizes collecting Tokyo-specific nuisance call information. If the user is in Osaka, the collection unit prioritizes collecting Osaka-specific nuisance call information. If the user is in Hokkaido, the collection unit prioritizes collecting Hokkaido-specific nuisance call information. This allows region-specific nuisance call information to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI and update the database based on the region-specific nuisance call information collected by the generation AI.

[0040] The collection unit can collect nuisance call information by referring to the user's past movement history when collecting the information. For example, the collection unit collects nuisance call information by referring to the user's past movement history when collecting the information. The collection unit uses a generation AI to analyze the user's past movement history and collect appropriate nuisance call information. For example, the collection unit collects nuisance call information from places the user has visited in the past. The collection unit preferentially collects nuisance call information from specific areas from the user's past movement history. The collection unit analyzes the user's past movement history and collects the most relevant nuisance call information. In this way, by referring to the user's past movement history, it is possible to collect highly relevant nuisance call information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past movement history into the generation AI and collect nuisance call information based on the analysis results of the generation AI.

[0041] The collection unit can analyze the user's social media activity and collect relevant nuisance call information at the time of collection. For example, the collection unit can analyze the user's social media activity and collect relevant nuisance call information at the time of collection. The collection unit can use a generation AI to analyze the user's social media activity and collect appropriate nuisance call information. For example, the collection unit can collect nuisance call information recently reported by the user on social media. The collection unit can prioritize the collection of specific nuisance call information from the user's social media activity. The collection unit can analyze the user's social media activity and collect the most relevant nuisance call information. This makes it possible to collect relevant nuisance call information by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's social media activity into the generation AI and collect nuisance call information based on the analysis results of the generation AI.

[0042] The operations department can analyze the collected data and identify patterns of nuisance calls. The operations department, for example, analyzes the collected data and identifies patterns of nuisance calls. The operations department analyzes the collected data using a generation AI. For example, the operations department identifies patterns of nuisance calls that occur frequently during specific time periods. The operations department identifies patterns of nuisance calls that occur frequently in specific areas. The operations department identifies patterns of nuisance calls from specific callers. In this way, by analyzing the collected data, it is possible to identify patterns of nuisance calls. Some or all of the above-mentioned processing in the operations department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the collected data into a generation AI and identify patterns of nuisance calls based on the results of the analysis by the generation AI.

[0043] When operating the database, the operations department can refer to past data to improve the accuracy of identifying new nuisance calls. For example, when operating the database, the operations department can refer to past data to improve the accuracy of identifying new nuisance calls. The operations department analyzes past data using the generation AI. For example, the operations department refers to past call records and report data, and the generation AI improves the accuracy of identifying new nuisance calls. The operations department analyzes past data, and the generation AI improves the algorithm for identifying nuisance calls. Based on the past data, the operations department discovers new patterns that the generation AI will use to improve its accuracy in identifying nuisance calls. In this way, by referring to past data, the accuracy of identifying new nuisance calls can be improved. Some or all of the above-mentioned processing in the operations department may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the operations department can input past data into the generation AI and improve the accuracy of identifying new nuisance calls based on the results of the analysis by the generation AI.

[0044] When operating the database, the operations unit can prioritize displaying region-specific nuisance call information by taking into account the user's geographical location information. For example, when operating the database, the operations unit prioritizes displaying region-specific nuisance call information by taking into account the user's geographical location information. The operations unit uses the generation AI to obtain the user's geographical location information and display appropriate nuisance call information. For example, if the user is in Tokyo, the operations unit prioritizes displaying Tokyo-specific nuisance call information. If the user is in Osaka, the operations unit prioritizes displaying Osaka-specific nuisance call information. If the user is in Hokkaido, the operations unit prioritizes displaying Hokkaido-specific nuisance call information. This allows region-specific nuisance call information to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the operations unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the operations unit can input the user's geographical location information into the generation AI and update the database based on the region-specific nuisance call information displayed by the generation AI.

[0045] The operation unit can analyze a user's social media activity and display relevant nuisance call information when operating the database. For example, when operating the database, the operation unit analyzes a user's social media activity and displays relevant nuisance call information. The operation unit uses a generation AI to analyze a user's social media activity and display appropriate nuisance call information. For example, the operation unit displays nuisance call information recently reported by the user on social media. The operation unit prioritizes displaying specific nuisance call information from the user's social media activity. The operation unit analyzes a user's social media activity and displays the most relevant nuisance call information. In this way, relevant nuisance call information can be displayed by analyzing the user's social media activity. Some or all of the above-mentioned processing in the operation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the operation unit can input a user's social media activity into the generation AI and display nuisance call information based on the results of the analysis by the generation AI.

[0046] The display unit can optimize the criteria for the danger rankings displayed on the incoming call screen based on past report data. For example, the display unit optimizes the criteria for the danger rankings displayed on the incoming call screen based on past report data. The display unit uses the generation AI to analyze the past report data and optimize the criteria for the danger rankings. For example, the display unit causes the generation AI to adjust the criteria for the danger rankings based on the past report data. The display unit analyzes the past report data and sets the most effective criteria for the danger rankings for the generation AI. The display unit refers to the past report data and causes the generation AI to optimize the criteria for the danger rankings. This enables more accurate risk assessment by optimizing the criteria for the danger rankings based on the past report data. Some or all of the above-described processing in the display unit may be performed using, or without, the generation AI. For example, the display unit can input past report data into the generation AI and optimize the criteria for the danger rankings based on the results of the analysis by the generation AI.

[0047] The display unit can adjust the criteria for the danger rankings displayed on the incoming call screen to region-specific criteria, taking into account the user's geographical location information. For example, the display unit adjusts the criteria for the danger rankings displayed on the incoming call screen to region-specific criteria, taking into account the user's geographical location information. The display unit acquires the user's geographical location information using a generation AI and sets appropriate criteria. For example, if the user is in Tokyo, the generation AI sets Tokyo-specific danger ranking criteria. If the user is in Osaka, the generation AI sets Osaka-specific danger ranking criteria. If the user is in Hokkaido, the generation AI sets Hokkaido-specific danger ranking criteria. This allows adjustment to region-specific criteria by taking the user's geographical location information into account. Some or all of the above-described processing on the display unit may be performed using, or without, the generation AI. For example, the display unit can input the user's geographical location information into the generation AI and display the danger rankings based on the region-specific criteria set by the generation AI.

[0048] The display unit can optimize the criteria for the danger rankings displayed on the incoming call screen based on the user's past report data. For example, the display unit optimizes the criteria for the danger rankings displayed on the incoming call screen based on the user's past report data. The display unit uses a generation AI to analyze the user's past report data and optimize the criteria for the danger rankings. For example, the display unit causes the generation AI to adjust the criteria for the danger rankings based on the user's past report data. The display unit analyzes the user's past report data and sets the most effective criteria for the danger rankings. The display unit refers to the user's past report data and causes the generation AI to optimize the criteria for the danger rankings. This enables more accurate risk assessment by optimizing the criteria for the danger rankings based on the user's past report data. Some or all of the above-described processing in the display unit may be performed using, or without, the generation AI. For example, the display unit can input the user's past report data into the generation AI and optimize the criteria for the danger rankings based on the analysis results of the generation AI.

[0049] The display unit can analyze the user's social media activity and adjust the criteria for the danger rankings displayed on the incoming call screen to relevant criteria. For example, the display unit analyzes the user's social media activity and adjusts the criteria for the danger rankings displayed on the incoming call screen to relevant criteria. The display unit uses the generation AI to analyze the user's social media activity and set appropriate criteria. For example, the display unit causes the generation AI to set the criteria for the danger rankings based on nuisance call information recently reported by the user on social media. The display unit causes the generation AI to preferentially display specific nuisance call information from the user's social media activity. The display unit analyzes the user's social media activity and causes the generation AI to display the most relevant nuisance call information. This allows adjustment to relevant criteria by analyzing the user's social media activity. Some or all of the above-mentioned processing in the display unit may be performed using, or without, the generation AI. For example, the display unit can input the user's social media activity into the generation AI and display a danger ranking based on the criteria set by the generation AI.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The response unit can automatically detect the language of the caller of the nuisance call and respond in the appropriate language. For example, if the caller speaks English, the generation AI will select a fixed English phrase to respond. If the caller speaks Chinese, the generation AI will select a fixed Chinese phrase to respond. If the caller speaks Spanish, the generation AI will select a fixed Spanish phrase to respond. This makes it possible to respond appropriately depending on the language of the caller of the nuisance call.

[0052] The collection unit can analyze the voice patterns of the callers of nuisance calls and classify the type of nuisance call based on the specific voice pattern. For example, it can identify sales calls with specific intonation and speaking patterns, identify fraudulent calls that frequently use specific phrases and expressions, and identify survey calls with specific voice characteristics. In this way, by classifying the type of nuisance call based on the voice pattern, it becomes possible to take more accurate measures against nuisance calls.

[0053] The operations department can analyze the geographic location information of the source of nuisance calls and identify patterns of nuisance calls from specific regions. For example, identify nuisance calls that frequently come from specific cities. Identify fraudulent calls that frequently come from specific countries. Identify patterns of sales calls from specific regions. By identifying patterns of nuisance calls based on geographic location information, it becomes possible to take measures against nuisance calls that are specific to each region.

[0054] The display unit can adjust the risk ranking based on the time of day from which the nuisance call originates. For example, the risk level can be set high for nuisance calls that occur frequently late at night, medium for sales calls that occur frequently during the day, and low for survey calls that occur frequently in the evening. This allows for more appropriate risk assessment by adjusting the risk ranking based on the time of day from which the call originates.

[0055] The collection unit analyzes the content of calls made by nuisance callers in real time and can instantly classify the type of nuisance call based on specific keywords. For example, it identifies fraudulent calls that include keywords such as "credit card" or "bank account." It identifies sales calls that include keywords such as "free" or "benefit." It also identifies survey calls that include keywords such as "survey" or "research." This allows for quick response by instantly classifying the type of nuisance call based on real-time analysis of call content.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The response unit automatically responds to nuisance calls. The response unit uses the generation AI to select fixed phrases and respond automatically. For example, the generation AI automatically selects and responds with phrases such as "I am currently away from my desk. Please call back later" or "I am not currently available to answer the phone. Please leave a message." Step 2: The collection unit collects information on nuisance calls based on the responses provided by the response unit. The collection unit collects information on nuisance calls reported by users, analyzes it using the generation AI, and stores it in a database. Step 3: The operations department analyzes the information collected by the collection department and operates a database of nuisance calls. The operations department analyzes the collected information on nuisance calls and stores it in the database. Using the generation AI, the collected information on nuisance calls is analyzed and stored in the database. Step 4: The display unit displays a risk ranking on the incoming call screen based on the database operated by the operations unit. The display unit evaluates the risk of the incoming call number based on past report data and displays "Risk: High," "Risk: Medium," or "Risk: Low" on the incoming call screen.

[0058] (Example 2) The spam and fraud call prevention system of an embodiment of the present invention uses a generation AI to deal with nuisance calls. When a nuisance call is received, the system activates an automatic response function, responding with a fixed phrase. For example, the generation AI automatically selects and responds with phrases such as "I'm away from my desk right now. Please call back later" or "I'm not available at the moment. Please leave a message." This eliminates the need for users to respond directly to nuisance calls. Next, a nuisance call prevention database is operated. This database stores information on previously reported nuisance calls, which the generation AI references to identify and respond to nuisance calls. A system is provided for users to report nuisance calls, and the reported data is analyzed by the generation AI and stored in the database. This allows for more accurate identification and response of nuisance calls. Furthermore, a risk ranking is displayed on the incoming call screen. The generation AI references a nuisance call database and evaluates the risk level of the incoming number. For example, if a call comes from a number with many past reports, it is deemed high risk, and the incoming call screen displays "Risk: High," "Risk: Medium," or "Risk: Low." The evaluation criteria take into account the number of reports and the seriousness of the reports. This allows users to check the risk level of incoming calls at a glance. This service protects users from nuisance and fraudulent calls, allowing them to use the phone with peace of mind. This enables the spam and fraud call prevention system to automatically respond to nuisance calls, collect information, operate a database, and display risk rankings.

[0059] A spam and fraud call prevention system according to an embodiment includes a response unit, a collection unit, an operation unit, and a display unit. The response unit automatically responds to nuisance calls. For example, the response unit uses a generation AI to select a fixed phrase and respond automatically. The generation AI uses a text generation AI (e.g., LLM) to generate an appropriate phrase for nuisance calls. For example, the response unit automatically selects and responds with a phrase such as "I'm away from my desk right now. Please call back later" or "I'm not currently available. Please leave a message." The collection unit collects information about nuisance calls based on the content of the response provided by the response unit. For example, the collection unit collects information about nuisance calls reported by users. The collection unit uses the generation AI to analyze the information about nuisance calls reported by users and store it in a database. The operation unit analyzes the information collected by the collection unit and operates a nuisance call database. For example, the operation unit analyzes the collected information about nuisance calls and stores it in a database. The operations unit uses a generation AI to analyze the collected nuisance call information and store it in a database. The display unit displays a risk ranking on the incoming call screen based on the database operated by the operations unit. For example, the display unit evaluates the risk of incoming numbers based on past report data and displays "Risk: High," "Risk: Medium," or "Risk: Low" on the incoming call screen. This enables the spam and fraud call prevention system of the embodiment to automatically respond to nuisance calls, collect information, operate a database, and display risk rankings.

[0060] The response unit can select a fixed phrase using the generation AI and perform an automatic response. The response unit, for example, uses the generation AI to select a fixed phrase and perform an automatic response. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate an appropriate phrase for a nuisance call. For example, the response unit automatically selects and responds with a phrase such as "I am currently away from my desk. Please call back later" or "I am not currently available. Please leave a message." This enables automatic responses to nuisance calls by having the generation AI select a fixed phrase. Some or all of the above-described processing in the response unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the response unit can input a prompt to the generation AI, such as "Please generate an appropriate phrase for a nuisance call," and perform an automatic response using the phrase generated by the generation AI.

[0061] The collection unit can collect information on nuisance calls reported by users. For example, the collection unit collects information on nuisance calls reported by users. The collection unit uses a generation AI to analyze the information on nuisance calls reported by users and store it in a database. For example, the collection unit has a mechanism whereby users provide information on nuisance calls through a reporting form. The information reported by users includes the content of the call, the call origin number, the duration of the call, etc. The collection unit analyzes this information and uses it to identify nuisance calls. In this way, collecting information on nuisance calls reported by users improves the accuracy of the database. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input information on nuisance calls reported by users into the generation AI and store the results of the analysis by the generation AI in a database.

[0062] The operations department can analyze the collected information on nuisance calls and store it in a database. The operations department, for example, analyzes the collected information on nuisance calls and stores it in a database. The operations department uses a generation AI to analyze the collected information on nuisance calls and store it in a database. For example, the operations department uses text mining technology to analyze the content of nuisance calls. The operations department uses a machine learning algorithm to identify patterns of nuisance calls and store them in a database. In this way, by analyzing the collected information on nuisance calls and storing it in a database, the accuracy of identifying nuisance calls is improved. Some or all of the above-mentioned processing in the operations department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the collected information on nuisance calls into a generation AI and store the results of the generation AI's analysis in a database.

[0063] The display unit can evaluate the risk level of the incoming call number based on past report data and display "Risk Level: High," "Risk Level: Medium," or "Risk Level: Low" on the incoming call screen. For example, the display unit evaluates the risk level of the incoming call number based on past report data and displays "Risk Level: High," "Risk Level: Medium," or "Risk Level: Low" on the incoming call screen. The display unit uses a generation AI to analyze past report data and evaluate the risk level of the incoming call number. For example, if the call is from a phone number that has received many past reports, the display unit determines that the risk level is high and displays "Risk Level: High" on the incoming call screen. The display unit evaluates the risk level taking into account the number of reports, the seriousness of the report content, etc. In this way, by evaluating the risk level of the incoming call number based on past report data and displaying it on the incoming call screen, the user can check the risk level of a nuisance call at a glance. Some or all of the above-mentioned processing in the display unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the display unit can input past report data into the generation AI and display the risk level evaluated by the generation AI on the incoming call screen.

[0064] The response unit can estimate the user's emotions and select a response phrase based on the estimated user emotions. For example, the response unit estimates the user's emotions and selects a response phrase based on the estimated user emotions. The response unit uses a generation AI to estimate the user's emotions. For example, the response unit estimates the user's emotions using voice analysis technology. The response unit can also estimate the user's emotions using facial expression recognition technology. The response unit uses a generation AI to select an appropriate response phrase based on the estimated user emotions. For example, if the user is stressed, the generation AI selects a simple phrase such as "I'm away from my desk right now. Please call back later." If the user is relaxed, the generation AI selects a more detailed phrase such as "I'm currently unavailable. Please leave a message." If the user is in a hurry, the generation AI selects a short phrase such as "I'm currently unavailable." This allows for a more appropriate response by selecting a response phrase based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response unit may be performed using, or without, the generation AI. For example, the response unit may input the user's voice data into the generation AI and select a response phrase based on the emotion estimated by the generation AI.

[0065] The response unit can generate different fixed phrases depending on the type of nuisance call. For example, the response unit generates different fixed phrases depending on the type of nuisance call. The response unit uses a generation AI to identify the type of nuisance call and generate an appropriate phrase. For example, in the case of a sales call, the generation AI generates a phrase such as "We are currently not accepting sales calls." In the case of a fraudulent call, the generation AI generates a warning phrase such as "This call may be fraudulent. Please be careful." In the case of a survey, the generation AI generates a phrase such as "You are currently not able to respond to the survey." This allows for more effective responses by generating different fixed phrases depending on the type of nuisance call. Some or all of the above-described processing in the response unit may be performed using, or without, the generation AI. For example, the response unit can input the type of nuisance call into the generation AI and respond using a phrase generated by the generation AI.

[0066] When selecting a response phrase, the response unit can refer to past response history to select an appropriate phrase. For example, when selecting a response phrase, the response unit refers to past response history to select the optimal phrase. The response unit uses a generation AI to analyze past response history and select an appropriate phrase. For example, if there has been a nuisance call from the same number in the past, the response unit has the generation AI reuse the response phrase used at that time. In the response unit, the generation AI selects the most effective phrase from the past response history. The response unit can also analyze the past response history and have the generation AI suggest a new phrase. In this way, the optimal phrase can be selected by referring to the past response history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the response unit can input past response history into the generation AI and select an appropriate phrase based on the analysis results of the generation AI.

[0067] The response unit can estimate the user's emotion and adjust the timing of the response based on the estimated user's emotion. For example, the response unit estimates the user's emotion and adjusts the timing of the response based on the estimated user's emotion. The response unit estimates the user's emotion using a generation AI. For example, the response unit estimates the user's emotion using voice analysis technology. The response unit can also estimate the user's emotion using facial expression recognition technology. The response unit adjusts the timing of the response of the generation AI based on the estimated user's emotion. For example, if the user is stressed, the generation AI responds immediately. If the user is relaxed, the generation AI responds with a slight delay. If the user is in a hurry, the generation AI responds quickly. This enables a more appropriate response by adjusting the timing of the response based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the response unit may input the user's voice data into the generation AI and adjust the timing of the response based on the emotion estimated by the generation AI.

[0068] The response unit can use region-specific phrases by taking into account the user's geographical location information when selecting a response phrase. For example, the response unit can use region-specific phrases by taking into account the user's geographical location information when selecting a response phrase. The response unit uses a generation AI to obtain the user's geographical location information and select an appropriate phrase. For example, if the user is in Tokyo, the generation AI can use a phrase such as "I'm currently in Tokyo, so please call me back later." If the user is in Osaka, the generation AI can use a phrase such as "I'm currently in Osaka, so please call me back later." If the user is in Hokkaido, the generation AI can use a phrase such as "I'm currently in Hokkaido, so please call me back later." This allows region-specific phrases to be used by taking into account the user's geographical location information. Some or all of the above-described processing in the response unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the response unit can input the user's geographical location information into the generation AI and use region-specific phrases selected by the generation AI.

[0069] The response unit can analyze the user's social media activity and use relevant phrases when selecting a response phrase. For example, the response unit can analyze the user's social media activity and use relevant phrases when selecting a response phrase. The response unit uses a generation AI to analyze the user's social media activity and select appropriate phrases. For example, if the user has recently posted about their travels on social media, the generation AI can use a phrase such as "I'm currently traveling, please call me back later." If the user has recently posted about their work on social media, the generation AI can use a phrase such as "I'm currently at work, please call me back later." If the user has recently posted about their family on social media, the generation AI can use a phrase such as "I'm currently spending time with my family, please call me back later." This allows relevant phrases to be used by analyzing the user's social media activity. Some or all of the above-described processing in the response unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the response unit can input the user's social media activity into the generation AI and use the phrases selected by the generation AI.

[0070] The collection unit can estimate the user's emotions and determine the priority of reports based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of reports based on the estimated user emotions. The collection unit estimates the user's emotions using a generation AI. For example, the collection unit estimates the user's emotions using voice analysis technology. The collection unit can also estimate the user's emotions using facial expression recognition technology. The collection unit causes the generation AI to determine the priority of reports based on the estimated user emotions. For example, if the user is stressed, the generation AI processes the report with priority. If the user is relaxed, the generation AI processes the report with normal priority. If the user is in a hurry, the generation AI processes the report quickly. This enables more appropriate reports by determining the priority of reports based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's voice data into the generation AI and determine the priority of reports based on the emotions estimated by the generation AI.

[0071] The collection unit can analyze the source information of nuisance calls and collect detailed data during collection. For example, the collection unit analyzes the source information of nuisance calls and collects detailed data during collection. The collection unit analyzes the source information of nuisance calls using a generation AI. For example, the collection unit analyzes the phone number of the source of the nuisance call and collects regional information of the source. The collection unit analyzes the business information of the source of the nuisance call and collects detailed data about the business. The collection unit analyzes the call history of the source of the nuisance call and collects past call data. In this way, detailed data can be collected by analyzing the source information of the nuisance call. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the source information of the nuisance call into a generation AI and collect detailed data based on the analysis results of the generation AI.

[0072] The collection unit can prioritize collecting region-specific nuisance call information by taking into account the user's geographical location information when collecting. For example, the collection unit prioritizes collecting region-specific nuisance call information by taking into account the user's geographical location information when collecting. The collection unit uses the generation AI to obtain the user's geographical location information and collect appropriate nuisance call information. For example, if the user is in Tokyo, the collection unit prioritizes collecting Tokyo-specific nuisance call information. If the user is in Osaka, the collection unit prioritizes collecting Osaka-specific nuisance call information. If the user is in Hokkaido, the collection unit prioritizes collecting Hokkaido-specific nuisance call information. This allows region-specific nuisance call information to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI and update the database based on the region-specific nuisance call information collected by the generation AI.

[0073] The collection unit can estimate the user's emotions and adjust the timing of the report based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of the report based on the estimated user emotions. The collection unit estimates the user's emotions using a generation AI. For example, the collection unit estimates the user's emotions using voice analysis technology. The collection unit can also estimate the user's emotions using facial expression recognition technology. The collection unit adjusts the timing of the generation AI's report based on the estimated user emotions. For example, if the user is stressed, the generation AI reports immediately. If the user is relaxed, the generation AI reports with a slight delay. If the user is in a hurry, the generation AI reports quickly. This allows for more appropriate reporting by adjusting the timing of the report based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's voice data into the generation AI and adjust the timing of the report based on the emotion estimated by the generation AI.

[0074] The collection unit can collect nuisance call information by referring to the user's past movement history when collecting the information. For example, the collection unit collects nuisance call information by referring to the user's past movement history when collecting the information. The collection unit uses a generation AI to analyze the user's past movement history and collect appropriate nuisance call information. For example, the collection unit collects nuisance call information from places the user has visited in the past. The collection unit preferentially collects nuisance call information from specific areas from the user's past movement history. The collection unit analyzes the user's past movement history and collects the most relevant nuisance call information. In this way, by referring to the user's past movement history, it is possible to collect highly relevant nuisance call information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past movement history into the generation AI and collect nuisance call information based on the analysis results of the generation AI.

[0075] The collection unit can analyze the user's social media activity and collect relevant nuisance call information at the time of collection. For example, the collection unit can analyze the user's social media activity and collect relevant nuisance call information at the time of collection. The collection unit can use a generation AI to analyze the user's social media activity and collect appropriate nuisance call information. For example, the collection unit can collect nuisance call information recently reported by the user on social media. The collection unit can prioritize the collection of specific nuisance call information from the user's social media activity. The collection unit can analyze the user's social media activity and collect the most relevant nuisance call information. This makes it possible to collect relevant nuisance call information by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's social media activity into the generation AI and collect nuisance call information based on the analysis results of the generation AI.

[0076] The operation unit can estimate the user's emotions and adjust the database update frequency based on the estimated user emotions. The operation unit, for example, estimates the user's emotions and adjusts the database update frequency based on the estimated user emotions. The operation unit estimates the user's emotions using a generation AI. For example, the operation unit estimates the user's emotions using voice analysis technology. The operation unit can also estimate the user's emotions using facial expression recognition technology. The operation unit causes the generation AI to adjust the database update frequency based on the estimated user emotions. For example, if the user is stressed, the generation AI updates the database frequently. If the user is relaxed, the generation AI updates the database at a normal frequency. If the user is in a hurry, the generation AI updates the database quickly. This enables more appropriate database operation by adjusting the database update frequency based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the operation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the operation unit may input the user's voice data into the generation AI and adjust the update frequency of the database based on the emotion estimated by the generation AI.

[0077] The operations department can analyze the collected data and identify patterns of nuisance calls. The operations department, for example, analyzes the collected data and identifies patterns of nuisance calls. The operations department analyzes the collected data using a generation AI. For example, the operations department identifies patterns of nuisance calls that occur frequently during specific time periods. The operations department identifies patterns of nuisance calls that occur frequently in specific areas. The operations department identifies patterns of nuisance calls from specific callers. In this way, by analyzing the collected data, it is possible to identify patterns of nuisance calls. Some or all of the above-mentioned processing in the operations department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the collected data into a generation AI and identify patterns of nuisance calls based on the results of the analysis by the generation AI.

[0078] When operating the database, the operations department can refer to past data to improve the accuracy of identifying new nuisance calls. For example, when operating the database, the operations department can refer to past data to improve the accuracy of identifying new nuisance calls. The operations department analyzes past data using the generation AI. For example, the operations department refers to past call records and report data, and the generation AI improves the accuracy of identifying new nuisance calls. The operations department analyzes past data, and the generation AI improves the algorithm for identifying nuisance calls. Based on the past data, the operations department discovers new patterns that the generation AI will use to improve its accuracy in identifying nuisance calls. In this way, by referring to past data, the accuracy of identifying new nuisance calls can be improved. Some or all of the above-mentioned processing in the operations department may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the operations department can input past data into the generation AI and improve the accuracy of identifying new nuisance calls based on the results of the analysis by the generation AI.

[0079] The operation unit can estimate the user's emotion and adjust the display method of the database based on the estimated user emotion. For example, the operation unit estimates the user's emotion and adjusts the display method of the database based on the estimated user emotion. The operation unit estimates the user's emotion using a generation AI. For example, the operation unit estimates the user's emotion using voice analysis technology. The operation unit can also estimate the user's emotion using facial expression recognition technology. The operation unit causes the generation AI to adjust the display method of the database based on the estimated user emotion. For example, if the user is stressed, the generation AI provides a simple, highly visible display method. If the user is relaxed, the generation AI provides a display method including detailed information. If the user is in a hurry, the generation AI provides a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display method of the database based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the operation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the operation unit may input the user's voice data into the generation AI and adjust the display method of the database based on the emotion estimated by the generation AI.

[0080] When operating the database, the operations unit can prioritize displaying region-specific nuisance call information by taking into account the user's geographical location information. For example, when operating the database, the operations unit prioritizes displaying region-specific nuisance call information by taking into account the user's geographical location information. The operations unit uses the generation AI to obtain the user's geographical location information and display appropriate nuisance call information. For example, if the user is in Tokyo, the operations unit prioritizes displaying Tokyo-specific nuisance call information. If the user is in Osaka, the operations unit prioritizes displaying Osaka-specific nuisance call information. If the user is in Hokkaido, the operations unit prioritizes displaying Hokkaido-specific nuisance call information. This allows region-specific nuisance call information to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the operations unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the operations unit can input the user's geographical location information into the generation AI and update the database based on the region-specific nuisance call information displayed by the generation AI.

[0081] The operation unit can analyze a user's social media activity and display relevant nuisance call information when operating the database. For example, when operating the database, the operation unit analyzes a user's social media activity and displays relevant nuisance call information. The operation unit uses a generation AI to analyze a user's social media activity and display appropriate nuisance call information. For example, the operation unit displays nuisance call information recently reported by the user on social media. The operation unit prioritizes displaying specific nuisance call information from the user's social media activity. The operation unit analyzes a user's social media activity and displays the most relevant nuisance call information. In this way, relevant nuisance call information can be displayed by analyzing the user's social media activity. Some or all of the above-mentioned processing in the operation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the operation unit can input a user's social media activity into the generation AI and display nuisance call information based on the results of the analysis by the generation AI.

[0082] The display unit can estimate the user's emotions and adjust the display method of the danger rankings based on the estimated user emotions. The display unit, for example, estimates the user's emotions and adjusts the display method of the danger rankings based on the estimated user emotions. The display unit estimates the user's emotions using a generation AI. For example, the display unit estimates the user's emotions using voice analysis technology. The display unit can also estimate the user's emotions using facial expression recognition technology. The display unit allows the generation AI to adjust the display method of the danger rankings based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI displays a simple, highly visible danger ranking. If the user is relaxed, the generation AI displays a danger ranking with detailed information. If the user is in a hurry, the generation AI displays a danger ranking that focuses on the main points. This allows for a more appropriate display by adjusting the display method of the danger rankings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, or without, the generation AI. For example, the display unit may input the user's voice data into the generation AI and adjust the display method of the risk ranking based on the emotion estimated by the generation AI.

[0083] The display unit can optimize the criteria for the danger rankings displayed on the incoming call screen based on past report data. For example, the display unit optimizes the criteria for the danger rankings displayed on the incoming call screen based on past report data. The display unit uses the generation AI to analyze the past report data and optimize the criteria for the danger rankings. For example, the display unit causes the generation AI to adjust the criteria for the danger rankings based on the past report data. The display unit analyzes the past report data and sets the most effective criteria for the danger rankings for the generation AI. The display unit refers to the past report data and causes the generation AI to optimize the criteria for the danger rankings. This enables more accurate risk assessment by optimizing the criteria for the danger rankings based on the past report data. Some or all of the above-described processing in the display unit may be performed using, or without, the generation AI. For example, the display unit can input past report data into the generation AI and optimize the criteria for the danger rankings based on the results of the analysis by the generation AI.

[0084] The display unit can adjust the criteria for the danger rankings displayed on the incoming call screen to region-specific criteria, taking into account the user's geographical location information. For example, the display unit adjusts the criteria for the danger rankings displayed on the incoming call screen to region-specific criteria, taking into account the user's geographical location information. The display unit acquires the user's geographical location information using a generation AI and sets appropriate criteria. For example, if the user is in Tokyo, the generation AI sets Tokyo-specific danger ranking criteria. If the user is in Osaka, the generation AI sets Osaka-specific danger ranking criteria. If the user is in Hokkaido, the generation AI sets Hokkaido-specific danger ranking criteria. This allows adjustment to region-specific criteria by taking the user's geographical location information into account. Some or all of the above-described processing on the display unit may be performed using, or without, the generation AI. For example, the display unit can input the user's geographical location information into the generation AI and display the danger rankings based on the region-specific criteria set by the generation AI.

[0085] The display unit can estimate the user's emotions and adjust the timing of displaying the risk rankings based on the estimated user's emotions. The display unit, for example, estimates the user's emotions and adjusts the timing of displaying the risk rankings based on the estimated user's emotions. The display unit estimates the user's emotions using a generation AI. For example, the display unit estimates the user's emotions using voice analysis technology. The display unit can also estimate the user's emotions using facial expression recognition technology. The display unit adjusts the timing of displaying the risk rankings using the generation AI based on the estimated user's emotions. For example, if the user is stressed, the generation AI immediately displays the risk rankings. If the user is relaxed, the generation AI displays the risk rankings with a slight delay. If the user is in a hurry, the generation AI quickly displays the risk rankings. This allows for a more appropriate display by adjusting the timing of displaying the risk rankings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the display unit may input the user's voice data into the generation AI and adjust the timing of displaying the risk ranking based on the emotion estimated by the generation AI.

[0086] The display unit can optimize the criteria for the danger rankings displayed on the incoming call screen based on the user's past report data. For example, the display unit optimizes the criteria for the danger rankings displayed on the incoming call screen based on the user's past report data. The display unit uses a generation AI to analyze the user's past report data and optimize the criteria for the danger rankings. For example, the display unit causes the generation AI to adjust the criteria for the danger rankings based on the user's past report data. The display unit analyzes the user's past report data and sets the most effective criteria for the danger rankings. The display unit refers to the user's past report data and causes the generation AI to optimize the criteria for the danger rankings. This enables more accurate risk assessment by optimizing the criteria for the danger rankings based on the user's past report data. Some or all of the above-described processing in the display unit may be performed using, or without, the generation AI. For example, the display unit can input the user's past report data into the generation AI and optimize the criteria for the danger rankings based on the analysis results of the generation AI.

[0087] The display unit can analyze the user's social media activity and adjust the criteria for the danger rankings displayed on the incoming call screen to relevant criteria. For example, the display unit analyzes the user's social media activity and adjusts the criteria for the danger rankings displayed on the incoming call screen to relevant criteria. The display unit uses the generation AI to analyze the user's social media activity and set appropriate criteria. For example, the display unit causes the generation AI to set the criteria for the danger rankings based on nuisance call information recently reported by the user on social media. The display unit causes the generation AI to preferentially display specific nuisance call information from the user's social media activity. The display unit analyzes the user's social media activity and causes the generation AI to display the most relevant nuisance call information. This allows adjustment to relevant criteria by analyzing the user's social media activity. Some or all of the above-mentioned processing in the display unit may be performed using, or without, the generation AI. For example, the display unit can input the user's social media activity into the generation AI and display a danger ranking based on the criteria set by the generation AI. === Hard Collateral 1-1 === Each of the multiple elements including the response unit, collection unit, operation 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 response unit is realized by the control unit 46A of the smart device 14, and selects a fixed phrase using a generation AI and performs an automatic response. The collection unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes information on nuisance calls reported by users and stores it in a database. The operation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes collected information on nuisance calls and stores it in a database. The display unit is realized, for example, by the control unit 46A of the smart device 14, and displays a risk ranking on the incoming call screen. === Hard Collateral 1-2 === Each of the multiple elements including the response unit, collection unit, operation unit, and display unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the response unit is realized by the control unit 46A of the smart glasses 214, and selects a fixed phrase using a generation AI and performs an automatic response. The collection unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes information on nuisance calls reported by users and stores it in a database. The operation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes collected information on nuisance calls and stores it in a database. The display unit is realized, for example, by the control unit 46A of the smart glasses 214, and displays a risk ranking on the incoming call screen. === Hard Collateral 1-3 === Each of the multiple elements including the response unit, collection unit, operation unit, and display unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the response unit is realized by the control unit 46A of the headset type terminal 314, and selects a fixed phrase using a generation AI and performs an automatic response. The collection unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes information on nuisance calls reported by users and stores it in a database. The operation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes collected information on nuisance calls and stores it in a database. The display unit is realized, for example, by the control unit 46A of the headset type terminal 314, and displays a risk ranking on the incoming call screen. === Hard Collateral 1-4 === Each of the multiple elements including the response unit, collection unit, operation unit, and display unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the response unit is realized by the control unit 46A of the robot 414, and selects a fixed phrase using a generation AI and performs an automatic response. The collection unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes information on nuisance calls reported by users and stores it in a database. The operation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes collected information on nuisance calls and stores it in a database. The display unit is realized, for example, by the control unit 46A of the robot 414, and displays a risk ranking on the incoming call screen.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The response unit can automatically detect the language of the caller of the nuisance call and respond in the appropriate language. For example, if the caller speaks English, the generation AI will select a fixed English phrase to respond. If the caller speaks Chinese, the generation AI will select a fixed Chinese phrase to respond. If the caller speaks Spanish, the generation AI will select a fixed Spanish phrase to respond. This makes it possible to respond appropriately depending on the language of the caller of the nuisance call.

[0090] The collection unit can analyze the voice patterns of the callers of nuisance calls and classify the type of nuisance call based on the specific voice pattern. For example, it can identify sales calls with specific intonation and speaking patterns, identify fraudulent calls that frequently use specific phrases and expressions, and identify survey calls with specific voice characteristics. In this way, by classifying the type of nuisance call based on the voice pattern, it becomes possible to take more accurate measures against nuisance calls.

[0091] The operations department can analyze the geographic location information of the source of nuisance calls and identify patterns of nuisance calls from specific regions. For example, identify nuisance calls that frequently come from specific cities. Identify fraudulent calls that frequently come from specific countries. Identify patterns of sales calls from specific regions. By identifying patterns of nuisance calls based on geographic location information, it becomes possible to take measures against nuisance calls that are specific to each region.

[0092] The display unit can adjust the risk ranking based on the time of day from which the nuisance call originates. For example, the risk level can be set high for nuisance calls that occur frequently late at night, medium for sales calls that occur frequently during the day, and low for survey calls that occur frequently in the evening. This allows for more appropriate risk assessment by adjusting the risk ranking based on the time of day from which the call originates.

[0093] The response unit can estimate the user's emotions and adjust the tone of the response phrase based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI will select a phrase with a calm tone. If the user is relaxed, the generation AI will select a phrase with a friendly tone. If the user is in a hurry, the generation AI will select a phrase with a quick tone. This allows for more appropriate responses by adjusting the tone of the response phrase based on the user's emotions.

[0094] The collection unit can estimate the user's emotions and adjust the level of detail in the report based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI will request detailed report content. If the user is relaxed, the generation AI will request concise report content. If the user is in a hurry, the generation AI will request minimal report content. This allows for more appropriate reporting by adjusting the level of detail in the report content based on the user's emotions.

[0095] The operations department can estimate the user's emotions and adjust the database search results based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI will provide concise search results. If the user is relaxed, the generation AI will provide detailed search results. If the user is in a hurry, the generation AI will provide search results that focus on the main points. In this way, by adjusting the database search results based on the user's emotions, it becomes possible to provide more appropriate information.

[0096] The display unit can estimate the user's emotions and adjust the display format of the danger ranking based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI provides a simple, highly visible display format. If the user is relaxed, the generation AI provides a display format that includes detailed information. If the user is in a hurry, the generation AI provides a display format that focuses on the main points. This allows for a more appropriate display by adjusting the display format of the danger ranking based on the user's emotions.

[0097] The response unit can estimate the user's emotions and adjust the timing of the response based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI will respond immediately. If the user is relaxed, the generation AI will respond with a slight delay. If the user is in a hurry, the generation AI will respond quickly. This allows for more appropriate responses by adjusting the timing of the response based on the user's emotions.

[0098] The collection unit analyzes the content of calls made by nuisance callers in real time and can instantly classify the type of nuisance call based on specific keywords. For example, it identifies fraudulent calls that include keywords such as "credit card" or "bank account." It identifies sales calls that include keywords such as "free" or "benefit." It also identifies survey calls that include keywords such as "survey" or "research." This allows for quick response by instantly classifying the type of nuisance call based on real-time analysis of call content.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The response unit automatically responds to nuisance calls. The response unit uses the generation AI to select fixed phrases and respond automatically. For example, the generation AI automatically selects and responds with phrases such as "I am currently away from my desk. Please call back later" or "I am not currently available to answer the phone. Please leave a message." Step 2: The collection unit collects information on nuisance calls based on the responses provided by the response unit. The collection unit collects information on nuisance calls reported by users, analyzes it using the generation AI, and stores it in a database. Step 3: The operations department analyzes the information collected by the collection department and operates a database of nuisance calls. The operations department analyzes the collected information on nuisance calls and stores it in the database. Using the generation AI, the collected information on nuisance calls is analyzed and stored in the database. Step 4: The display unit displays a risk ranking on the incoming call screen based on the database operated by the operations unit. The display unit evaluates the risk of the incoming call number based on past report data and displays "Risk: High," "Risk: Medium," or "Risk: Low" on the incoming call screen.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] 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.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] 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.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 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 response unit that automatically responds to nuisance calls; a collection unit that collects information about nuisance calls based on the content of the response from the response unit; an operation unit that analyzes the information collected by the collection unit and operates a database of nuisance calls; a display unit that displays a risk ranking on an incoming call screen based on the database operated by the operation unit; A system characterized by:

2. The response unit Select a fixed phrase using AI generation and provide an automatic response 2. The system of claim 1.

3. The collecting unit Collect information about nuisance calls reported by users 2. The system of claim 1.

4. The operation unit Analyze the collected nuisance call information and store it in a database 2. The system of claim 1.

5. The display unit Evaluates the risk level of the incoming call number based on past report data and displays it on the incoming call screen 2. The system of claim 1.

6. The response unit Estimate the user's emotions and select a response phrase based on the estimated user emotions.

2. The system of claim 1.

7. The response unit Generate different catch phrases depending on the type of spam call 2. The system of claim 1.

8. The response unit When selecting a response phrase, refer to past response history to select an appropriate phrase.

2. The system of claim 1.

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