System

Abstract

An object of a system according to an embodiment is to perform authentication based on an operation pattern of a user and eliminate the need for a login action.SOLUTION: A system according to an embodiment includes an operation pattern collection unit, an analysis unit, and an authentication unit. The operation pattern collection unit collects an operation pattern of a user. The analysis unit analyzes the operation pattern collected by the operation pattern collection unit. The authentication unit authenticates the user on the basis of the operation pattern analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies require login procedures such as ID / Pass or biometric authentication, leaving room for improvement in terms of usability and security.

[0005] The system according to the embodiment aims to perform authentication based on the user's operation patterns, thereby eliminating the need for login. [Means for solving the problem]

[0006] A system according to an embodiment includes an operation pattern collection unit, an analysis unit, and an authentication unit. The operation pattern collection unit collects user operation patterns. The analysis unit analyzes the operation patterns collected by the operation pattern collection unit. The authentication unit authenticates the user based on the operation patterns analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment performs authentication based on the user's operation patterns, making it possible to eliminate the need for login actions. [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 authentication system according to the embodiment of the present invention uses AI to analyze the operating habits of PCs and smartphones and continuously identify the person who is operating them. This eliminates the need for login procedures such as ID / Pass or biometric authentication, improving usability and security levels.

[0029] An authentication system according to an embodiment includes an operation pattern collection unit, an analysis unit, and an authentication unit. The operation pattern collection unit collects user operation patterns. For example, it collects keyboard typing patterns. The operation pattern collection unit can also collect mouse movement patterns, for example. The operation pattern collection unit can also collect touchscreen operation methods, for example. The analysis unit analyzes the operation patterns collected by the operation pattern collection unit. For example, the analysis unit analyzes the operation patterns using a statistical method. The analysis unit can also analyze the operation patterns using a machine learning algorithm, for example. The analysis unit can also extract features of the operation patterns, for example. The authentication unit authenticates a user based on the operation patterns analyzed by the analysis unit. For example, the authentication unit authenticates a user based on the degree of similarity of the operation patterns. The authentication unit can also authenticate a user based on the similarity of the operation patterns, for example. The authentication unit can also authenticate a user based on the features of the operation patterns, for example. As a result, the authentication system according to the embodiment performs continuous authentication based on the user's operation patterns, eliminating the need for login procedures such as ID / Pass or biometric authentication, thereby improving usability and security levels.

[0030] The operation pattern collection unit can collect environmental sounds and background noises in addition to the user's operation patterns, and can also identify the operation environment. For example, when a user operates a PC or smartphone, the operation pattern collection unit simultaneously collects the surrounding environmental sounds and background noise. For example, it analyzes office noise and household sounds to identify the operation environment. The operation pattern collection unit also analyzes the environmental sounds and background noises to model the operation patterns in a specific operation environment. For example, it takes into account the differences in operation patterns between quiet and noisy environments. The operation pattern collection unit also integrates data on the operation patterns and the environmental sounds to build an authentication model. For example, when a specific environmental sound is detected, it prioritizes authentication of operation patterns appropriate for that environment. This improves authentication accuracy by taking into account the environmental sounds and background noise.

[0031] The operation pattern collection unit can analyze user operation patterns by time period and day of the week, and construct a model that takes into account differences in operations during specific time periods. The operation pattern collection unit, for example, collects and analyzes user operation data by time period and day of the week. For example, differences in operation patterns between weekday mornings and evenings and weekends are considered. The operation pattern collection unit also constructs different authentication models based on operation patterns for each time period and day of the week. For example, weekday operation patterns and weekend operation patterns are modeled separately. The operation pattern collection unit also analyzes differences in operation patterns for each time period and day of the week in real time, improving authentication accuracy. For example, fluctuations in operation patterns during specific time periods are considered. This improves authentication accuracy by taking into account differences in operation patterns for each time period and day of the week.

[0032] The operation pattern collection unit adds the user's gaze tracking data, and can also use gaze movements for authentication. The operation pattern collection unit, for example, collects the user's gaze tracking data and integrates it with the operation pattern. For example, it simultaneously analyzes gaze movements and keyboard typing. The operation pattern collection unit also builds an authentication model for the operation pattern based on the gaze tracking data. For example, it determines whether the gaze movements match a specific operation pattern. The operation pattern collection unit also analyzes the gaze tracking data in real time to improve the authentication accuracy of the operation pattern. For example, it issues a warning if the gaze movements do not match the operation pattern. This improves authentication accuracy by taking gaze movements into consideration.

[0033] The operation pattern collection unit can collect physical operation data, such as the way a device is held and tilted, in addition to the user's operation patterns and use it for authentication. The operation pattern collection unit collects physical operation data, such as the way a device is held and tilted, and integrates it with operation patterns. For example, it analyzes the way a smartphone is held and tilted. The operation pattern collection unit also builds an authentication model for operation patterns based on the physical operation data. For example, it determines whether the way the device is held matches a specific operation pattern. The operation pattern collection unit also analyzes the physical operation data in real time to improve the authentication accuracy of operation patterns. For example, it issues a warning if the way the device is held does not match the operation pattern. This improves authentication accuracy by taking physical operation data into consideration.

[0034] In addition to authentication based on a user's operation patterns, the authentication unit can also consider device usage history and application usage status to perform comprehensive authentication. The authentication unit, for example, collects device usage history and application usage status and integrates them with operation patterns. For example, it analyzes the frequency of use of specific applications. The authentication unit also builds an authentication model for operation patterns based on the usage history and usage status. For example, it determines whether the usage status of a specific application matches the operation pattern. The authentication unit also analyzes the usage history and usage status in real time to improve the authentication accuracy of operation patterns. For example, it issues a warning if the usage status of a specific application does not match the operation pattern. This improves authentication accuracy by taking into account device usage history and application usage status.

[0035] The authentication unit can analyze a user's operation patterns in real time and immediately issue a warning if an abnormal operation is detected. The authentication unit, for example, builds a system that analyzes a user's operation patterns in real time and issues a warning if an abnormal operation is detected. For example, it detects movements that differ from normal operation patterns. The authentication unit also introduces a system that immediately issues a warning if an abnormal operation is detected. For example, it displays an alert when an abnormal operation is detected. The authentication unit also develops a system that analyzes operation patterns in real time and automatically takes measures if an abnormal operation is detected. For example, it restricts access when an abnormal operation is detected. This improves security by detecting abnormal operations in real time and immediately issuing a warning.

[0036] The authentication unit can expand the scope of always-on authentication to include not only PCs and smartphones, but also other devices such as smartwatches and smart speakers. For example, the authentication unit builds a system that expands the scope of always-on authentication to other devices such as smartwatches and smart speakers. For example, it analyzes the operation patterns of smartwatches. The authentication unit also collects operation patterns of other devices and adds them to the scope of always-on authentication. For example, it analyzes the voice operation patterns of smart speakers. The authentication unit also builds an authentication model for always-on authentication based on the operation patterns of other devices. For example, it integrates the operation patterns of smartwatches and smart speakers. This improves the convenience of authentication by expanding the scope of always-on authentication to other devices.

[0037] The authentication unit stores the results of continuous authentication in the cloud and shares authentication information among multiple devices, thereby achieving seamless authentication. For example, the authentication unit stores the results of continuous authentication in the cloud and builds a system that shares authentication information among multiple devices. For example, it synchronizes authentication information between a PC and a smartphone. The authentication unit also achieves seamless authentication among multiple devices based on the authentication information stored in the cloud. For example, it shares authentication information between a smartwatch and a smart speaker. The authentication unit also develops a system that updates the authentication information stored in the cloud in real time and synchronizes it among multiple devices. For example, it immediately updates the authentication information when a new device is added. In this way, seamless authentication is achieved by sharing the authentication information stored in the cloud.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The operation pattern collection unit can collect the device's remaining battery level and charging status in addition to the user's operation patterns and use them for authentication. For example, it analyzes operation patterns when the battery level is low or while charging and compares them with normal operation patterns. The operation pattern collection unit also models differences in operation patterns based on the remaining battery level and charging status to improve authentication accuracy. For example, it determines whether a specific operation pattern appears when the battery level is low. This improves authentication accuracy by taking the remaining battery level and charging status into account.

[0040] The operation pattern collection unit can collect device location information in addition to user operation patterns and use it for authentication. For example, it can analyze operation patterns in specific locations and compare them with normal operation patterns. The operation pattern collection unit also models differences in operation patterns based on location information to improve authentication accuracy. For example, it determines whether operation patterns differ in specific locations, such as an office or home. This improves authentication accuracy by taking location information into account.

[0041] The operation pattern collection unit can collect environmental data such as the device's temperature and humidity in addition to the user's operation patterns and use it for authentication. For example, it analyzes operation patterns when the temperature or humidity is high and compares them with normal operation patterns. The operation pattern collection unit also models differences in operation patterns based on environmental data to improve authentication accuracy. For example, it determines whether specific operation patterns appear when the temperature or humidity is high. This improves authentication accuracy by taking environmental data into consideration.

[0042] The operation pattern collection unit can collect the device's network connection status in addition to the user's operation patterns and use it for authentication. For example, it analyzes operation patterns when connected to Wi-Fi and when connected to mobile data and compares them with normal operation patterns. The operation pattern collection unit also models differences in operation patterns based on the network connection status to improve authentication accuracy. For example, it determines whether operation patterns differ under specific network connection conditions. This improves authentication accuracy by taking the network connection status into consideration.

[0043] The operation pattern collection unit can collect setting data such as the device's screen brightness and volume settings in addition to the user's operation patterns and use it for authentication. For example, it analyzes operation patterns when the screen brightness and volume settings are different and compares them with normal operation patterns. The operation pattern collection unit also models differences in operation patterns based on the setting data to improve authentication accuracy. For example, it determines whether operation patterns differ with specific setting data. This improves authentication accuracy by taking the setting data into consideration.

[0044] The operation pattern collection unit can collect the device usage time and break time in addition to the user's operation patterns and use them for authentication. For example, it analyzes the operation pattern after prolonged use and compares it with the normal operation pattern. The operation pattern collection unit also models differences in operation patterns based on usage time and break time to improve authentication accuracy. For example, it determines whether the operation pattern after prolonged use or break time is different. This improves authentication accuracy by taking usage time and break time into consideration.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The operation pattern collection unit collects user operation patterns, such as how to type on a keyboard, how to move a mouse, and how to operate a touchscreen. Step 2: The analysis unit analyzes the operation patterns collected by the operation pattern collection unit. For example, the analysis unit analyzes the operation patterns using a statistical method or a machine learning algorithm, and extracts characteristics of the operation patterns. Step 3: The authentication unit authenticates the user based on the operation pattern analyzed by the analysis unit, for example, based on the degree of match, similarity, or characteristics of the operation pattern.

[0047] (Example 2) The authentication system according to the embodiment of the present invention uses AI to analyze the operating habits of PCs and smartphones and continuously identify the person who is operating them. This eliminates the need for login procedures such as ID / Pass or biometric authentication, improving usability and security levels.

[0048] An authentication system according to an embodiment includes an operation pattern collection unit, an analysis unit, and an authentication unit. The operation pattern collection unit collects user operation patterns. For example, it collects keyboard typing patterns. The operation pattern collection unit can also collect mouse movement patterns, for example. The operation pattern collection unit can also collect touchscreen operation methods, for example. The analysis unit analyzes the operation patterns collected by the operation pattern collection unit. For example, the analysis unit analyzes the operation patterns using a statistical method. The analysis unit can also analyze the operation patterns using a machine learning algorithm, for example. The analysis unit can also extract features of the operation patterns, for example. The authentication unit authenticates a user based on the operation patterns analyzed by the analysis unit. For example, the authentication unit authenticates a user based on the degree of similarity of the operation patterns. The authentication unit can also authenticate a user based on the similarity of the operation patterns, for example. The authentication unit can also authenticate a user based on the features of the operation patterns, for example. As a result, the authentication system according to the embodiment performs continuous authentication based on the user's operation patterns, eliminating the need for login procedures such as ID / Pass or biometric authentication, thereby improving usability and security levels.

[0049] The operation pattern collection unit can collect environmental sounds and background noises in addition to the user's operation patterns, and can also identify the operation environment. For example, when a user operates a PC or smartphone, the operation pattern collection unit simultaneously collects the surrounding environmental sounds and background noise. For example, it analyzes office noise and household sounds to identify the operation environment. The operation pattern collection unit also analyzes the environmental sounds and background noises to model the operation patterns in a specific operation environment. For example, it takes into account the differences in operation patterns between quiet and noisy environments. The operation pattern collection unit also integrates data on the operation patterns and the environmental sounds to build an authentication model. For example, when a specific environmental sound is detected, it prioritizes authentication of operation patterns appropriate for that environment. This improves authentication accuracy by taking into account the environmental sounds and background noise.

[0050] The operation pattern collection unit can analyze user operation patterns by time period and day of the week, and construct a model that takes into account differences in operations during specific time periods. The operation pattern collection unit, for example, collects and analyzes user operation data by time period and day of the week. For example, differences in operation patterns between weekday mornings and evenings and weekends are considered. The operation pattern collection unit also constructs different authentication models based on operation patterns for each time period and day of the week. For example, weekday operation patterns and weekend operation patterns are modeled separately. The operation pattern collection unit also analyzes differences in operation patterns for each time period and day of the week in real time, improving authentication accuracy. For example, fluctuations in operation patterns during specific time periods are considered. This improves authentication accuracy by taking into account differences in operation patterns for each time period and day of the week.

[0051] The operation pattern collection unit can use the emotion estimation function to analyze the emotional state of the user during operation and consider differences in operation patterns according to changes in emotion. The operation pattern collection unit, for example, analyzes the emotional state of the user during operation in real time and reflects it in the operation pattern. For example, differences in operation patterns in a stressed state or a relaxed state are considered. The operation pattern collection unit also uses the emotion estimation function to collect emotional data during operation by the user and incorporates it into an authentication model. For example, differences in operation patterns according to changes in emotion are modeled. The operation pattern collection unit also analyzes differences in operation patterns based on the emotional state and improves authentication accuracy. For example, differences in operation patterns in a positive emotional state and a negative emotional state are considered. This improves authentication accuracy by considering differences in operation patterns according to changes in emotion.

[0052] The operation pattern collection unit adds the user's gaze tracking data, and can also use gaze movements for authentication. The operation pattern collection unit, for example, collects the user's gaze tracking data and integrates it with the operation pattern. For example, it simultaneously analyzes gaze movements and keyboard typing. The operation pattern collection unit also builds an authentication model for the operation pattern based on the gaze tracking data. For example, it determines whether the gaze movements match a specific operation pattern. The operation pattern collection unit also analyzes the gaze tracking data in real time to improve the authentication accuracy of the operation pattern. For example, it issues a warning if the gaze movements do not match the operation pattern. This improves authentication accuracy by taking gaze movements into consideration.

[0053] The operation pattern collection unit can collect physical operation data, such as the way a device is held and tilted, in addition to the user's operation patterns and use it for authentication. The operation pattern collection unit collects physical operation data, such as the way a device is held and tilted, and integrates it with operation patterns. For example, it analyzes the way a smartphone is held and tilted. The operation pattern collection unit also builds an authentication model for operation patterns based on the physical operation data. For example, it determines whether the way the device is held matches a specific operation pattern. The operation pattern collection unit also analyzes the physical operation data in real time to improve the authentication accuracy of operation patterns. For example, it issues a warning if the way the device is held does not match the operation pattern. This improves authentication accuracy by taking physical operation data into consideration.

[0054] The operation pattern collection unit can use the emotion estimation function to analyze the stress level felt by the user during operation and strengthen authentication when stress is high. The operation pattern collection unit, for example, uses the emotion estimation function to analyze the stress level felt by the user during operation in real time. For example, it strengthens authentication when stress is high. The operation pattern collection unit also analyzes differences in operation patterns based on stress level and incorporates them into an authentication model. For example, it models operation patterns in a high-stress state. The operation pattern collection unit also takes measures to improve authentication accuracy of operation patterns when stress level is high. For example, it requests additional authentication procedures when stress is high. In this way, authentication accuracy is improved by taking stress level into consideration.

[0055] In addition to authentication based on a user's operation patterns, the authentication unit can also consider device usage history and application usage status to perform comprehensive authentication. The authentication unit, for example, collects device usage history and application usage status and integrates them with operation patterns. For example, it analyzes the frequency of use of specific applications. The authentication unit also builds an authentication model for operation patterns based on the usage history and usage status. For example, it determines whether the usage status of a specific application matches the operation pattern. The authentication unit also analyzes the usage history and usage status in real time to improve the authentication accuracy of operation patterns. For example, it issues a warning if the usage status of a specific application does not match the operation pattern. This improves authentication accuracy by taking into account device usage history and application usage status.

[0056] The authentication unit can analyze a user's operation patterns in real time and immediately issue a warning if an abnormal operation is detected. The authentication unit, for example, builds a system that analyzes a user's operation patterns in real time and issues a warning if an abnormal operation is detected. For example, it detects movements that differ from normal operation patterns. The authentication unit also introduces a system that immediately issues a warning if an abnormal operation is detected. For example, it displays an alert when an abnormal operation is detected. The authentication unit also develops a system that analyzes operation patterns in real time and automatically takes measures if an abnormal operation is detected. For example, it restricts access when an abnormal operation is detected. This improves security by detecting abnormal operations in real time and immediately issuing a warning.

[0057] The authentication unit can use the emotion estimation function to monitor the emotional state of the user during operation in real time and adjust the strictness of authentication according to changes in emotion. The authentication unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the user during operation in real time. For example, it analyzes changes in the user's emotion and adjusts the strictness of authentication. The authentication unit also analyzes differences in operation patterns based on the emotional state and incorporates them into an authentication model. For example, it models differences in operation patterns according to changes in emotion. The authentication unit also develops a system that adjusts the strictness of authentication when the emotional state changes. For example, it strengthens authentication when it detects an operation pattern in a negative emotional state. This improves authentication accuracy by adjusting the strictness of authentication according to changes in emotion.

[0058] The authentication unit can expand the scope of always-on authentication to include not only PCs and smartphones, but also other devices such as smartwatches and smart speakers. For example, the authentication unit builds a system that expands the scope of always-on authentication to other devices such as smartwatches and smart speakers. For example, it analyzes the operation patterns of smartwatches. The authentication unit also collects operation patterns of other devices and adds them to the scope of always-on authentication. For example, it analyzes the voice operation patterns of smart speakers. The authentication unit also builds an authentication model for always-on authentication based on the operation patterns of other devices. For example, it integrates the operation patterns of smartwatches and smart speakers. This improves the convenience of authentication by expanding the scope of always-on authentication to other devices.

[0059] The authentication unit stores the results of continuous authentication in the cloud and shares authentication information among multiple devices, thereby achieving seamless authentication. For example, the authentication unit stores the results of continuous authentication in the cloud and builds a system that shares authentication information among multiple devices. For example, it synchronizes authentication information between a PC and a smartphone. The authentication unit also achieves seamless authentication among multiple devices based on the authentication information stored in the cloud. For example, it shares authentication information between a smartwatch and a smart speaker. The authentication unit also develops a system that updates the authentication information stored in the cloud in real time and synchronizes it among multiple devices. For example, it immediately updates the authentication information when a new device is added. In this way, seamless authentication is achieved by sharing the authentication information stored in the cloud.

[0060] The authentication unit can use the emotion estimation function to analyze the sense of security felt by the user during operation and strengthen authentication when the sense of security is low. The authentication unit, for example, uses the emotion estimation function to build a system that analyzes the sense of security felt by the user during operation in real time. For example, it strengthens authentication when the sense of security is low. The authentication unit also analyzes differences in operation patterns based on the sense of security and incorporates them into an authentication model. For example, it models operation patterns when the sense of security is low. The authentication unit also takes measures to improve the authentication accuracy of operation patterns when the sense of security is low. For example, it requests additional authentication procedures when the sense of security is low. This improves security by strengthening authentication when the sense of security is low.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The operation pattern collection unit can collect the device's remaining battery level and charging status in addition to the user's operation patterns and use them for authentication. For example, it analyzes operation patterns when the battery level is low or while charging and compares them with normal operation patterns. The operation pattern collection unit also models differences in operation patterns based on the remaining battery level and charging status to improve authentication accuracy. For example, it determines whether a specific operation pattern appears when the battery level is low. This improves authentication accuracy by taking the remaining battery level and charging status into account.

[0063] The operation pattern collection unit can collect device location information in addition to user operation patterns and use it for authentication. For example, it can analyze operation patterns in specific locations and compare them with normal operation patterns. The operation pattern collection unit also models differences in operation patterns based on location information to improve authentication accuracy. For example, it determines whether operation patterns differ in specific locations, such as an office or home. This improves authentication accuracy by taking location information into account.

[0064] The operation pattern collection unit uses the emotion estimation function to analyze the user's level of concentration during operation and can strengthen authentication when the level of concentration is low. For example, it determines whether a specific operation pattern appears when the level of concentration is low. The operation pattern collection unit also models differences in operation patterns based on the level of concentration to improve authentication accuracy. For example, it models operation patterns when the level of concentration is low. This improves authentication accuracy by taking the level of concentration into consideration.

[0065] The operation pattern collection unit can collect environmental data such as the device's temperature and humidity in addition to the user's operation patterns and use it for authentication. For example, it analyzes operation patterns when the temperature or humidity is high and compares them with normal operation patterns. The operation pattern collection unit also models differences in operation patterns based on environmental data to improve authentication accuracy. For example, it determines whether specific operation patterns appear when the temperature or humidity is high. This improves authentication accuracy by taking environmental data into consideration.

[0066] The operation pattern collection unit uses the emotion estimation function to analyze the excitement level of the user during operation and can strengthen authentication when the excitement level is high. For example, it determines whether a specific operation pattern appears when the excitement level is high. The operation pattern collection unit also models differences in operation patterns based on the excitement level to improve authentication accuracy. For example, it models operation patterns in a state where the excitement level is high. This improves authentication accuracy by taking excitement level into account.

[0067] The operation pattern collection unit can collect the device's network connection status in addition to the user's operation patterns and use it for authentication. For example, it analyzes operation patterns when connected to Wi-Fi and when connected to mobile data and compares them with normal operation patterns. The operation pattern collection unit also models differences in operation patterns based on the network connection status to improve authentication accuracy. For example, it determines whether operation patterns differ under specific network connection conditions. This improves authentication accuracy by taking the network connection status into consideration.

[0068] The operation pattern collection unit uses the emotion estimation function to analyze the user's level of fatigue during operation and can strengthen authentication when the level of fatigue is high. For example, it determines whether a specific operation pattern appears when the level of fatigue is high. The operation pattern collection unit also models differences in operation patterns based on the level of fatigue to improve authentication accuracy. For example, it models operation patterns when the level of fatigue is high. This improves authentication accuracy by taking the level of fatigue into consideration.

[0069] The operation pattern collection unit can collect setting data such as the device's screen brightness and volume settings in addition to the user's operation patterns and use it for authentication. For example, it analyzes operation patterns when the screen brightness and volume settings are different and compares them with normal operation patterns. The operation pattern collection unit also models differences in operation patterns based on the setting data to improve authentication accuracy. For example, it determines whether operation patterns differ with specific setting data. This improves authentication accuracy by taking the setting data into consideration.

[0070] The operation pattern collection unit uses the emotion estimation function to analyze the happiness level of the user during operation and can strengthen authentication if the happiness level is low. For example, it determines whether a specific operation pattern appears when the happiness level is low. The operation pattern collection unit also models differences in operation patterns based on happiness level to improve authentication accuracy. For example, it models operation patterns when the happiness level is low. This improves authentication accuracy by taking happiness level into consideration.

[0071] The operation pattern collection unit can collect the device usage time and break time in addition to the user's operation patterns and use them for authentication. For example, it analyzes the operation pattern after prolonged use and compares it with the normal operation pattern. The operation pattern collection unit also models differences in operation patterns based on usage time and break time to improve authentication accuracy. For example, it determines whether the operation pattern after prolonged use or break time is different. This improves authentication accuracy by taking usage time and break time into consideration.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The operation pattern collection unit collects user operation patterns, such as how to type on a keyboard, how to move a mouse, and how to operate a touchscreen. Step 2: The analysis unit analyzes the operation patterns collected by the operation pattern collection unit. For example, the analysis unit analyzes the operation patterns using a statistical method or a machine learning algorithm, and extracts characteristics of the operation patterns. Step 3: The authentication unit authenticates the user based on the operation pattern analyzed by the analysis unit, for example, based on the degree of match, similarity, or characteristics of the operation pattern.

[0074] 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.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, the 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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. [Explanation of symbols]

[0141] 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. an operation pattern collection unit that collects user operation patterns; an analysis unit that analyzes the operation patterns collected by the operation pattern collection unit; an authentication unit that authenticates a user based on the operation pattern analyzed by the analysis unit. A system characterized by:

2. The operation pattern collection unit In addition to user operation patterns, environmental sounds and background noises are collected to identify the operating environment.

2. The system of claim 1.

3. The operation pattern collection unit Analyze user operation patterns by time of day and day of the week, and build a model that takes into account differences in operations during specific time periods.

2. The system of claim 1.

4. The operation pattern collection unit Analyze the user's emotional state during operation and consider differences in operation patterns according to changes in emotion 2. The system of claim 1.

5. The operation pattern collection unit Adding user eye tracking data to also use eye movements for authentication 2. The system of claim 1.

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