System

Abstract

An object of the system according to the embodiment is to translate and display the content of a sign or a signboard in the field of view in real time.SOLUTION: A system includes a camera, an analysis unit, a translation unit, and a display unit. The camera captures an image of a sign or a signboard in the field of view. The analysis part analyzes image data photographed by a camera to extract a text. The translation unit translates the text extracted by the analysis unit. The display unit displays the text translated by the translation unit on the display of the smart glasses.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 technology has had the problem of making it difficult to translate and display the contents of signs and billboards within the field of view in real time.

[0005] The system of the embodiment aims to translate and display the content of signs and billboards within the field of view in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, an analysis unit, a translation unit, and a display unit. The camera captures images of signs and billboards within its field of view. The analysis unit analyzes the image data captured by the camera to extract text. The translation unit translates the text extracted by the analysis unit. The display unit displays the text translated by the translation unit on the display of the smart glasses. [Effects of the Invention]

[0007] The system according to the embodiment can translate and display the content of signs and billboards within its field of view in real time. [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 smart glasses system according to an embodiment of the present invention automatically detects signs and billboards within its field of view, and uses a generation AI to translate the text and display it on the smart glasses display. This allows the smart glasses system to translate the content of signs and billboards within its field of view in real time and provide it to the user.

[0029] A smart glasses system according to an embodiment includes a camera, an analysis unit, a translation unit, and a display unit. The camera captures signs and billboards within its field of view. For example, the camera has high resolution and can clearly capture signs and billboards even from a distance. The camera can also automatically detect road signs and store signs while walking around town. The analysis unit analyzes image data captured by the camera to extract text. For example, the analysis unit uses an image recognition algorithm to recognize and extract text within an image. The analysis unit can also use text extraction technology to accurately extract text such as "STOP" written on a road sign or "Restaurant" written on a store sign. The translation unit translates the text extracted by the analysis unit. For example, the translation unit uses a generation AI to translate the extracted text into a specified language. The generation AI translates the text in real time based on the language set by the user. For example, the translation unit translates an English sign that says "STOP" into Japanese, which means "Stop." The display unit displays the text translated by the translation unit on the smart glasses display. For example, the display unit displays the translation result in a small size at the edge of the field of view so as not to obstruct the center of the field of view. The display unit also appropriately positions the translation result within the user's field of view, allowing the user to comfortably confirm the information. This allows the smart glasses system according to the embodiment to translate the contents of signs and billboards within the field of view in real time and display them on the smart glasses display. For example, the contents of signs and billboards seen in a foreign city can be understood in one's own language. Furthermore, even in an environment where different languages ​​are used, it becomes possible to quickly obtain information and take appropriate action.

[0030] The camera can link the location information of signs and billboards with map data in real time and automatically determine the user's current location. For example, the camera links the location information of signs and billboards with GPS data to determine the user's current location in real time. For example, it determines which intersection the user is at based on the location of a road sign. The camera also compares the location information of signs and billboards with map data and automatically tracks the user's movement route. For example, it displays the route the user is walking on a map. The camera also uses the location information of signs and billboards to record the history of places the user has visited. For example, it saves the location information of famous places visited at tourist spots. This makes it possible to automatically determine the user's current location.

[0031] The camera can be equipped with an algorithm that analyzes the shape and color of signs and billboards and preferentially detects signs with a specific pattern. For example, the camera can analyze the shape and color of signs and billboards and preferentially detect a specific pattern (e.g., a red octagonal STOP sign). The camera can also analyze the color of signs and billboards and preferentially detect a specific color (e.g., a blue information sign). The camera can also analyze the shape of signs and billboards and preferentially detect a specific shape (e.g., a triangular warning sign). This makes it possible to preferentially detect signs with a specific pattern.

[0032] The camera can upload information about signs and billboards to a cloud platform and share it with other users. For example, the camera uploads information about signs and billboards to the cloud and builds a platform for sharing with other users. For example, sign information for tourist spots can be shared. The camera also stores information about signs and billboards in the cloud and makes it accessible to other users. For example, traffic information can be shared in real time. The camera also uploads information about signs and billboards to the cloud and builds a platform where other users can comment and rate it. For example, store sign information can be shared. This allows information to be shared with other users.

[0033] The camera can notify the user of information about signs and billboards by voice. For example, the camera adds a function to notify the user of information about signs and billboards by voice. For example, it can announce by voice, "There is a STOP sign ahead." The camera also adds a function to notify the user of information about signs and billboards by voice. For example, it can announce by voice, "There is a restaurant sign on the right." The camera also adds a function to notify the user of information about signs and billboards by voice. For example, it can announce by voice, "There is a parking sign on the left." This makes it possible to notify the user of information by voice.

[0034] The analysis unit can also analyze background information of signs and billboards when analyzing image data, and complement the meaning of the text. For example, the analysis unit can analyze background information of signs and billboards when analyzing image data, and complement the meaning of the text. For example, the analysis unit can analyze the types of surrounding buildings to complement the meaning of signs. Furthermore, the analysis unit can analyze background information of signs and billboards when analyzing image data, and complement the meaning of the text. For example, the analysis unit can analyze the characteristics of the scenery to complement the content of the sign. Furthermore, the analysis unit can analyze background information of signs and billboards when analyzing image data, and complement the meaning of the text. For example, the analysis unit can analyze surrounding traffic conditions to complement the meaning of signs. This makes it possible to complement the meaning of the text.

[0035] The analysis unit can improve the image data analysis algorithm to enable high-accuracy text extraction even in low light or bad weather. The analysis unit, for example, improves the image data analysis algorithm to enable high-accuracy text extraction even in low light. For example, accurately analyzes signs at night. The analysis unit also improves the image data analysis algorithm to enable high-accuracy text extraction even in bad weather. For example, accurately analyzes signs in the rain. The analysis unit also improves the image data analysis algorithm to enable high-accuracy text extraction even in low light or bad weather. For example, accurately analyzes signs even in foggy conditions. This allows high-accuracy text extraction even in low light or bad weather.

[0036] The analysis unit can link with other devices and share the analysis results of the image data among multiple devices. The analysis unit, for example, links with a smartphone and shares the analysis results of the image data among multiple devices. For example, the text of a sign analyzed by smart glasses is displayed on the smartphone. The analysis unit can also link with a tablet and share the analysis results of the image data among multiple devices. For example, the text of a sign analyzed by smart glasses is displayed on the tablet. The analysis unit can also link with other devices and share the analysis results of the image data among multiple devices. For example, the text of a sign analyzed by smart glasses is displayed on a computer. This allows the analysis results to be shared among multiple devices.

[0037] The translation unit can implement an algorithm that takes into account the context of signs and billboards during translation and generates more natural translation results. For example, the translation unit implements an algorithm that takes into account the context of signs and billboards during translation and generates more natural translation results. For example, it naturally translates "STOP" as "Stop." The translation unit also implements an algorithm that takes into account the context of signs and billboards during translation and generates more natural translation results. For example, it naturally translates "restaurant" as "restaurant." The translation unit also implements an algorithm that takes into account the context of signs and billboards during translation and generates more natural translation results. For example, it naturally translates "parking lot" as "parking." This allows for more natural translation results to be generated.

[0038] The translation department can collect user feedback on the translation results and build a system that continuously improves translation accuracy. The translation department, for example, collects user feedback on the translation results and builds a system that continuously improves translation accuracy. For example, a function is added that allows users to rate and comment on the translation results. The translation department also collects user feedback on the translation results and builds a system that continuously improves translation accuracy. For example, a function is added that allows users to correct the translation results. The translation department also collects user feedback on the translation results and builds a system that continuously improves translation accuracy. For example, a function is added that allows users to rate the translation results as "good" or "bad". This makes it possible to continuously improve translation accuracy.

[0039] The display unit can optimize the display position of the translation result based on the user's gaze tracking data to improve visibility. The display unit, for example, optimizes the display position of the translation result based on the user's gaze tracking data to improve visibility. For example, the translation result is displayed in the direction the user is looking. The display unit also optimizes the display position of the translation result based on the user's gaze tracking data to improve visibility. For example, the translation result is displayed near a sign that the user is looking at. The display unit also optimizes the display position of the translation result based on the user's gaze tracking data to improve visibility. For example, the translation result is displayed on top of a sign that the user is looking at. This can improve visibility.

[0040] The display unit can add a function that allows the user to customize the display font and color of the translation result according to their preferences. The display unit, for example, adds a function that allows the user to customize the display font and color of the translation result according to their preferences. For example, it provides settings that allow the user to change the font size and color. The display unit also adds a function that allows the user to customize the display font and color of the translation result according to their preferences. For example, it provides settings that allow the user to change the background color and text color. The display unit also adds a function that allows the user to customize the display font and color of the translation result according to their preferences. For example, it provides settings that allow the user to change the font style and thickness. This allows the user to customize the display font and color.

[0041] The display unit can use augmented reality (AR) technology to display the translation result superimposed on a sign or billboard. For example, the display unit uses augmented reality (AR) technology to display the translation result superimposed on an actual sign or billboard. For example, the translation result of "STOP" is displayed superimposed on an actual sign. The display unit also uses augmented reality (AR) technology to display the translation result superimposed on an actual sign or billboard. For example, the translation result of "restaurant" is displayed superimposed on an actual signboard. The display unit also uses augmented reality (AR) technology to display the translation result superimposed on an actual sign or billboard. For example, the translation result of "parking lot" is displayed superimposed on an actual signboard. This allows the translation result to be displayed superimposed on an actual sign or billboard.

[0042] The display unit can add a function that allows the translation results to be shared with other users in real time and the information to be checked collaboratively. The display unit, for example, adds a function that allows the translation results to be shared with other users in real time and the information to be checked collaboratively. For example, the translation results can be uploaded to the cloud so that other users can access them. The display unit also adds a function that allows the translation results to be shared with other users in real time and the information to be checked collaboratively. For example, the translation results can be posted to a social networking site so that other users can comment and rate them. The display unit also adds a function that allows the translation results to be shared with other users in real time and the information to be checked collaboratively. For example, a dedicated app for sharing translation results can be developed. This allows the translation results to be shared with other users in real time.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The smart glasses system can also monitor the user's health condition and provide information according to the health condition. For example, if the user's heart rate is high, it can provide information to help them relax. If the user's step count is high, it can provide information encouraging them to take a break. Furthermore, if the user's sleep time is short, it can provide health advice. This allows the system to provide optimal information according to the user's health condition.

[0045] The smart glasses system can also be equipped with a function to provide information according to the user's preferences. For example, it can prioritize the display of information in areas that interest the user. It can also provide information about places that the user frequently visits. It can also display related information based on keywords set by the user. This allows the system to provide optimal information according to the user's preferences.

[0046] The smart glasses system can also analyze the user's behavioral history and provide information based on the user's behavioral patterns. For example, it can provide information about the user's favorite routes, stores the user frequently visits, and places the user has visited in the past. This allows the system to provide optimal information based on the user's behavioral patterns.

[0047] The smart glasses system can also link with the user's schedule and provide information based on the schedule. For example, it can provide route guidance based on the user's schedule. It can also provide location information related to the user's schedule. It can also display reminders based on the user's schedule. This allows it to provide optimal information based on the user's schedule.

[0048] The smart glasses system can also have a function to provide information in response to a user's voice commands. For example, if a user says, "Tell me about nearby restaurants," information about nearby restaurants will be displayed. If a user says, "Tell me about the next intersection," information about the next intersection will be displayed. Furthermore, if a user says, "Tell me the weather forecast," the weather forecast for the current location can be displayed. This allows the system to provide optimal information in response to the user's voice commands.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The camera captures signs and billboards within its field of view. For example, the camera has high resolution and can clearly capture signs and billboards that are far away. The camera can also automatically detect road signs and store signs as you walk around town. Step 2: The analysis unit analyzes the image data captured by the camera to extract text. For example, the analysis unit uses an image recognition algorithm to recognize and extract text within the image. The analysis unit can also use text extraction technology to accurately extract text such as "STOP" written on a road sign or "Restaurant" written on a store sign. Step 3: The translation unit translates the text extracted by the analysis unit. For example, the translation unit uses a generation AI to translate the extracted text into the specified language. The generation AI translates the text in real time based on the language set by the user. For example, it translates an English sign that says "STOP" into the Japanese sign that says "Stop." Step 4: The display unit displays the text translated by the translation unit on the smart glasses display. For example, the display unit may display the translation result in a small size at the edge of the user's field of view so as not to obstruct the central field of view. The display unit may also position the translation result appropriately within the user's field of view so that the user can comfortably view the information.

[0051] (Example 2) The smart glasses system according to an embodiment of the present invention automatically detects signs and billboards within its field of view, and uses a generation AI to translate the text and display it on the smart glasses display. This allows the smart glasses system to translate the content of signs and billboards within its field of view in real time and provide it to the user.

[0052] A smart glasses system according to an embodiment includes a camera, an analysis unit, a translation unit, and a display unit. The camera captures signs and billboards within its field of view. For example, the camera has high resolution and can clearly capture signs and billboards even from a distance. The camera can also automatically detect road signs and store signs while walking around town. The analysis unit analyzes image data captured by the camera to extract text. For example, the analysis unit uses an image recognition algorithm to recognize and extract text within an image. The analysis unit can also use text extraction technology to accurately extract text such as "STOP" written on a road sign or "Restaurant" written on a store sign. The translation unit translates the text extracted by the analysis unit. For example, the translation unit uses a generation AI to translate the extracted text into a specified language. The generation AI translates the text in real time based on the language set by the user. For example, the translation unit translates an English sign that says "STOP" into Japanese, which means "Stop." The display unit displays the text translated by the translation unit on the smart glasses display. For example, the display unit displays the translation result in a small size at the edge of the field of view so as not to obstruct the center of the field of view. The display unit also appropriately positions the translation result within the user's field of view, allowing the user to comfortably confirm the information. This allows the smart glasses system according to the embodiment to translate the contents of signs and billboards within the field of view in real time and display them on the smart glasses display. For example, the contents of signs and billboards seen in a foreign city can be understood in one's own language. Furthermore, even in an environment where different languages ​​are used, it becomes possible to quickly obtain information and take appropriate action.

[0053] The camera can link the location information of signs and billboards with map data in real time and automatically determine the user's current location. For example, the camera links the location information of signs and billboards with GPS data to determine the user's current location in real time. For example, it determines which intersection the user is at based on the location of a road sign. The camera also compares the location information of signs and billboards with map data and automatically tracks the user's movement route. For example, it displays the route the user is walking on a map. The camera also uses the location information of signs and billboards to record the history of places the user has visited. For example, it saves the location information of famous places visited at tourist spots. This makes it possible to automatically determine the user's current location.

[0054] The camera can be equipped with an algorithm that analyzes the shape and color of signs and billboards and preferentially detects signs with a specific pattern. For example, the camera can analyze the shape and color of signs and billboards and preferentially detect a specific pattern (e.g., a red octagonal STOP sign). The camera can also analyze the color of signs and billboards and preferentially detect a specific color (e.g., a blue information sign). The camera can also analyze the shape of signs and billboards and preferentially detect a specific shape (e.g., a triangular warning sign). This makes it possible to preferentially detect signs with a specific pattern.

[0055] The camera can upload information about signs and billboards to a cloud platform and share it with other users. For example, the camera uploads information about signs and billboards to the cloud and builds a platform for sharing with other users. For example, sign information for tourist spots can be shared. The camera also stores information about signs and billboards in the cloud and makes it accessible to other users. For example, traffic information can be shared in real time. The camera also uploads information about signs and billboards to the cloud and builds a platform where other users can comment and rate it. For example, store sign information can be shared. This allows information to be shared with other users.

[0056] The camera can notify the user of information about signs and billboards by voice. For example, the camera adds a function to notify the user of information about signs and billboards by voice. For example, it can announce by voice, "There is a STOP sign ahead." The camera also adds a function to notify the user of information about signs and billboards by voice. For example, it can announce by voice, "There is a restaurant sign on the right." The camera also adds a function to notify the user of information about signs and billboards by voice. For example, it can announce by voice, "There is a parking sign on the left." This makes it possible to notify the user of information by voice.

[0057] The analysis unit can also analyze background information of signs and billboards when analyzing image data, and complement the meaning of the text. For example, the analysis unit can analyze background information of signs and billboards when analyzing image data, and complement the meaning of the text. For example, the analysis unit can analyze the types of surrounding buildings to complement the meaning of signs. Furthermore, the analysis unit can analyze background information of signs and billboards when analyzing image data, and complement the meaning of the text. For example, the analysis unit can analyze the characteristics of the scenery to complement the content of the sign. Furthermore, the analysis unit can analyze background information of signs and billboards when analyzing image data, and complement the meaning of the text. For example, the analysis unit can analyze surrounding traffic conditions to complement the meaning of signs. This makes it possible to complement the meaning of the text.

[0058] The analysis unit can improve the image data analysis algorithm to enable high-accuracy text extraction even in low light or bad weather. The analysis unit, for example, improves the image data analysis algorithm to enable high-accuracy text extraction even in low light. For example, accurately analyzes signs at night. The analysis unit also improves the image data analysis algorithm to enable high-accuracy text extraction even in bad weather. For example, accurately analyzes signs in the rain. The analysis unit also improves the image data analysis algorithm to enable high-accuracy text extraction even in low light or bad weather. For example, accurately analyzes signs even in foggy conditions. This allows high-accuracy text extraction even in low light or bad weather.

[0059] The analysis unit can link with other devices and share the analysis results of the image data among multiple devices. The analysis unit, for example, links with a smartphone and shares the analysis results of the image data among multiple devices. For example, the text of a sign analyzed by smart glasses is displayed on the smartphone. The analysis unit can also link with a tablet and share the analysis results of the image data among multiple devices. For example, the text of a sign analyzed by smart glasses is displayed on the tablet. The analysis unit can also link with other devices and share the analysis results of the image data among multiple devices. For example, the text of a sign analyzed by smart glasses is displayed on a computer. This allows the analysis results to be shared among multiple devices.

[0060] The translation unit can implement an algorithm that takes into account the context of signs and billboards during translation and generates more natural translation results. For example, the translation unit implements an algorithm that takes into account the context of signs and billboards during translation and generates more natural translation results. For example, it naturally translates "STOP" as "Stop." The translation unit also implements an algorithm that takes into account the context of signs and billboards during translation and generates more natural translation results. For example, it naturally translates "restaurant" as "restaurant." The translation unit also implements an algorithm that takes into account the context of signs and billboards during translation and generates more natural translation results. For example, it naturally translates "parking lot" as "parking." This allows for more natural translation results to be generated.

[0061] The translation department can collect user feedback on the translation results and build a system that continuously improves translation accuracy. The translation department, for example, collects user feedback on the translation results and builds a system that continuously improves translation accuracy. For example, a function is added that allows users to rate and comment on the translation results. The translation department also collects user feedback on the translation results and builds a system that continuously improves translation accuracy. For example, a function is added that allows users to correct the translation results. The translation department also collects user feedback on the translation results and builds a system that continuously improves translation accuracy. For example, a function is added that allows users to rate the translation results as "good" or "bad". This makes it possible to continuously improve translation accuracy.

[0062] The display unit can optimize the display position of the translation result based on the user's gaze tracking data to improve visibility. The display unit, for example, optimizes the display position of the translation result based on the user's gaze tracking data to improve visibility. For example, the translation result is displayed in the direction the user is looking. The display unit also optimizes the display position of the translation result based on the user's gaze tracking data to improve visibility. For example, the translation result is displayed near a sign that the user is looking at. The display unit also optimizes the display position of the translation result based on the user's gaze tracking data to improve visibility. For example, the translation result is displayed on top of a sign that the user is looking at. This can improve visibility.

[0063] The display unit can add a function that allows the user to customize the display font and color of the translation result according to their preferences. The display unit, for example, adds a function that allows the user to customize the display font and color of the translation result according to their preferences. For example, it provides settings that allow the user to change the font size and color. The display unit also adds a function that allows the user to customize the display font and color of the translation result according to their preferences. For example, it provides settings that allow the user to change the background color and text color. The display unit also adds a function that allows the user to customize the display font and color of the translation result according to their preferences. For example, it provides settings that allow the user to change the font style and thickness. This allows the user to customize the display font and color.

[0064] The display unit can use augmented reality (AR) technology to display the translation result superimposed on a sign or billboard. For example, the display unit uses augmented reality (AR) technology to display the translation result superimposed on an actual sign or billboard. For example, the translation result of "STOP" is displayed superimposed on an actual sign. The display unit also uses augmented reality (AR) technology to display the translation result superimposed on an actual sign or billboard. For example, the translation result of "restaurant" is displayed superimposed on an actual signboard. The display unit also uses augmented reality (AR) technology to display the translation result superimposed on an actual sign or billboard. For example, the translation result of "parking lot" is displayed superimposed on an actual signboard. This allows the translation result to be displayed superimposed on an actual sign or billboard.

[0065] The display unit can add a function that allows the translation results to be shared with other users in real time and the information to be checked collaboratively. The display unit, for example, adds a function that allows the translation results to be shared with other users in real time and the information to be checked collaboratively. For example, the translation results can be uploaded to the cloud so that other users can access them. The display unit also adds a function that allows the translation results to be shared with other users in real time and the information to be checked collaboratively. For example, the translation results can be posted to a social networking site so that other users can comment and rate them. The display unit also adds a function that allows the translation results to be shared with other users in real time and the information to be checked collaboratively. For example, a dedicated app for sharing translation results can be developed. This allows the translation results to be shared with other users in real time.

[0066] The display unit can use the emotion estimation function to preferentially adopt a display method that allows the user to relax. The display unit, for example, uses the emotion estimation function to preferentially adopt a display method that allows the user to relax. For example, when the user shows a relaxed facial expression, the display method is preferentially adopted. The display unit also uses the emotion estimation function to preferentially adopt a display method that allows the user to relax. For example, when the user shows a facial expression that does not indicate stress, the display method is preferentially adopted. The display unit also uses the emotion estimation function to preferentially adopt a display method that allows the user to relax. For example, when the user shows a calm facial expression, the display method is preferentially adopted. This makes it possible to adopt a display method that allows the user to relax.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The smart glasses system can further estimate the user's emotions and adjust the display content based on the estimated emotions. For example, if the user is excited, the display unit can display information in a concise format. If the user is tired, the display unit can display information in a larger font for improved visibility. If the user is relaxed, the display unit can scroll the information slowly. This allows the system to provide an optimal display method according to the user's emotions.

[0069] The smart glasses system can further include a function for estimating the user's emotions and adjusting the tone and content of the voice notification based on the estimated emotions. For example, if the user is nervous, the voice notification can be provided in a calm tone. If the user is relaxed, the voice notification can be provided in a bright tone. Furthermore, if the user is tired, the voice notification can be provided in a concise and easy-to-understand manner. This makes it possible to provide optimal voice notifications according to the user's emotions.

[0070] The smart glasses system can also be equipped with a function to estimate the user's emotions and adjust the timing of displaying the translation results based on the estimated emotions. For example, if the user is in a hurry, the translation results can be displayed immediately. If the user is relaxed, the translation results can be displayed slowly. Furthermore, if the user is concentrating, the translation results can be displayed at the edge of the user's field of vision so as not to obstruct the center of the field of vision. This makes it possible to provide the optimal display timing according to the user's emotions.

[0071] The smart glasses system can also be equipped with a function to estimate the user's emotions and adjust the priority of the displayed content based on the estimated emotions. For example, if the user is nervous, important information can be displayed first. If the user is relaxed, detailed information can be displayed first. Furthermore, if the user is tired, simple information can be displayed first. This allows the system to provide optimal information according to the user's emotions.

[0072] The smart glasses system can also be equipped with a function to estimate the user's emotions and adjust the color and font of the displayed content based on the estimated emotions. For example, if the user is relaxed, the color of the displayed content can be changed to a softer color. If the user is nervous, the font of the displayed content can be made larger to improve visibility. Furthermore, if the user is tired, the background color of the displayed content can be made darker to reduce eye strain. This makes it possible to provide an optimal display method according to the user's emotions.

[0073] The smart glasses system can also monitor the user's health condition and provide information according to the health condition. For example, if the user's heart rate is high, it can provide information to help them relax. If the user's step count is high, it can provide information encouraging them to take a break. Furthermore, if the user's sleep time is short, it can provide health advice. This allows the system to provide optimal information according to the user's health condition.

[0074] The smart glasses system can also be equipped with a function to provide information according to the user's preferences. For example, it can prioritize the display of information in areas that interest the user. It can also provide information about places that the user frequently visits. It can also display related information based on keywords set by the user. This allows the system to provide optimal information according to the user's preferences.

[0075] The smart glasses system can also analyze the user's behavioral history and provide information based on the user's behavioral patterns. For example, it can provide information about the user's favorite routes, stores the user frequently visits, and places the user has visited in the past. This allows the system to provide optimal information based on the user's behavioral patterns.

[0076] The smart glasses system can also link with the user's schedule and provide information based on the schedule. For example, it can provide route guidance based on the user's schedule. It can also provide location information related to the user's schedule. It can also display reminders based on the user's schedule. This allows it to provide optimal information based on the user's schedule.

[0077] The smart glasses system can also have a function to provide information in response to a user's voice commands. For example, if a user says, "Tell me about nearby restaurants," information about nearby restaurants will be displayed. If a user says, "Tell me about the next intersection," information about the next intersection will be displayed. Furthermore, if a user says, "Tell me the weather forecast," the weather forecast for the current location can be displayed. This allows the system to provide optimal information in response to the user's voice commands.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The camera captures signs and billboards within its field of view. For example, the camera has high resolution and can clearly capture signs and billboards that are far away. The camera can also automatically detect road signs and store signs as you walk around town. Step 2: The analysis unit analyzes the image data captured by the camera to extract text. For example, the analysis unit uses an image recognition algorithm to recognize and extract text within the image. The analysis unit can also use text extraction technology to accurately extract text such as "STOP" written on a road sign or "Restaurant" written on a store sign. Step 3: The translation unit translates the text extracted by the analysis unit. For example, the translation unit uses a generation AI to translate the extracted text into the specified language. The generation AI translates the text in real time based on the language set by the user. For example, it translates an English sign that says "STOP" into the Japanese sign that says "Stop." Step 4: The display unit displays the text translated by the translation unit on the smart glasses display. For example, the display unit may display the translation result in a small size at the edge of the user's field of view so as not to obstruct the central field of view. The display unit may also position the translation result appropriately within the user's field of view so that the user can comfortably view the information.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 camera that photographs signs and billboards within its field of view, an analysis unit that analyzes the image data captured by the camera and extracts text; a translation unit that translates the text extracted by the analysis unit; a display unit that displays the text translated by the translation unit on a display of the smart glasses. A system characterized by:

2. The camera is The location information of the signs and billboards is linked to map data in real time to automatically identify the user's current location.

2. The system of claim 1.

3. The camera is An algorithm is introduced that analyzes the shape and color of the signs and billboards and preferentially detects signs with specific patterns.

2. The system of claim 1.

4. The camera is Uploading the sign or information about the sign to a cloud platform and sharing it with other users 2. The system of claim 1.

5. The camera is The information on the sign or billboard is notified to the user by voice.

2. The system of claim 1.

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