system

Abstract

The system according to the embodiment aims to enable a visually impaired person to grasp the environment of their current location in a timely manner. [Solution] A system according to an embodiment includes an image acquisition unit, a position acquisition unit, an analysis unit, and a feedback unit. The image acquisition unit acquires image information. The position acquisition unit acquires position information. The analysis unit analyzes the information acquired by the image acquisition unit and the position acquisition unit. The feedback unit provides audio feedback of the information analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for visually impaired people to grasp the environment of their current location in a timely manner.

[0005] The system according to the embodiment aims to enable a visually impaired person to grasp the environment of their current location in a timely manner. [Means for solving the problem]

[0006] A system according to an embodiment includes an image acquisition unit, a position acquisition unit, an analysis unit, and a feedback unit. The image acquisition unit acquires image information. The position acquisition unit acquires position information. The analysis unit analyzes the information acquired by the image acquisition unit and the position acquisition unit. The feedback unit provides audio feedback of the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a visually impaired person to grasp the environment of their current location in a timely manner. [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) An assistance system for visually impaired persons according to an embodiment of the present invention uses a generation AI to analyze space based on image information acquired by a wearable camera, accurate location information, and highly accurate map information. The system then utilizes 5G communications to provide low-latency, real-time information to the user in natural language. This system enables visually impaired persons to gain timely understanding of their current environment without the intervention of sighted persons. First, a visually impaired person wearing a wearable camera captures image information of their surroundings while walking. This image information is sent to a generation AI, which analyzes the images and recognizes the surrounding environment. For example, it identifies obstacles on the sidewalk, the status of traffic lights, and the locations of surrounding buildings. Next, the accurate location information and highly accurate map information are combined to generate detailed environmental information about the current location. Location information is acquired using GPS or other location information technologies, and highly accurate map information is acquired from a service that provides detailed map data. This allows visually impaired persons to accurately grasp their current location. Furthermore, 5G communications are utilized to provide information with low latency and excellent real-time performance. This allows visually impaired persons to instantly receive information about their surrounding environment and make timely decisions. For example, they can know in real time when the traffic light has turned green or if there is an obstacle ahead. Finally, the information analyzed by the generative AI is fed back in natural language. Visually impaired people can receive information about their surrounding environment through audio guidance, allowing them to act independently without the intervention of sighted people. For example, specific information such as "There is a crosswalk ahead. The traffic light is green. There is a building on the right" is provided by voice. This system enables visually impaired people to understand the environment of their current location in a timely manner, enabling safe and independent movement. In this way, the support system for visually impaired people can provide visually impaired people with a timely understanding of the environment of their current location.

[0029] An assistance system for visually impaired persons according to an embodiment includes an image acquisition unit, a position acquisition unit, an analysis unit, and a feedback unit. The image acquisition unit acquires image information using a wearable camera worn by the visually impaired person. For example, the image acquisition unit can acquire image information of the surroundings in real time while the visually impaired person is walking. The image acquisition unit can acquire, for example, still images and videos in high resolution. The image acquisition unit can also convert the image information into a format that is easy to analyze using a generative AI. The position acquisition unit acquires accurate position information using GPS or other position information technology. For example, the position acquisition unit can determine the current location of the visually impaired person in real time. The position acquisition unit acquires position information by receiving, for example, a GPS signal. The position acquisition unit can also supplement the position information using technologies such as Wi-Fi and Bluetooth. The analysis unit analyzes the information acquired by the image acquisition unit and the position acquisition unit. The analysis unit uses a generative AI to integrate the image information and position information and recognize the surrounding environment. For example, the analysis unit can identify obstacles on the sidewalk. The analysis unit can also identify the status of traffic lights. Furthermore, the analysis unit can also identify the locations of surrounding buildings. The feedback unit provides audio feedback of the information analyzed by the analysis unit. The feedback unit generates information in natural language using a generative AI and provides it to the visually impaired person. For example, the feedback unit provides specific information by audio, such as, "There is a crosswalk ahead. The traffic light is green. There is a building on the right." This allows the visually impaired person to instantly receive information about their surrounding environment. This allows the assistance system for visually impaired persons according to the embodiment to provide a timely understanding of the environment of their current location. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the feedback unit may provide information feedback using a generative AI model that receives information analyzed by the analysis unit and outputs audio feedback.

[0030] The analysis unit can identify obstacles on the sidewalk. The analysis unit, for example, uses a generation AI to analyze image information and identify obstacles on the sidewalk. For example, the analysis unit can identify the position of sidewalk steps and obstacles from the image information. The analysis unit can also identify the size and shape of the obstacle using the generation AI. For example, the analysis unit can identify the height and width of the obstacle from the image information. The analysis unit can also identify the type of obstacle using the generation AI. For example, the analysis unit can identify whether the obstacle is a wheelchair ramp or a pedestrian step from the image information. This allows visually impaired people to walk safely by identifying obstacles on the sidewalk. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input image information to the generation AI and have the generation AI identify the obstacle.

[0031] The analysis unit can identify the status of a traffic light. The analysis unit, for example, uses a generation AI to analyze image information and identify the status of the traffic light. For example, the analysis unit can identify the color of the traffic light from the image information. The analysis unit can also identify the flashing status of the traffic light using the generation AI. For example, the analysis unit can identify whether the traffic light is red, green, or flashing from the image information. The analysis unit can also identify the location of the traffic light using the generation AI. For example, the analysis unit can identify the location of the traffic light at an intersection from the image information. This allows visually impaired people to cross the road safely by identifying the status of the traffic light. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input image information to the generation AI and have the generation AI identify the status of the traffic light.

[0032] The analysis unit can identify the positions of surrounding buildings. The analysis unit, for example, uses a generation AI to analyze image information and identify the positions of surrounding buildings. For example, the analysis unit can identify the GPS coordinates of the building from the image information. The analysis unit can also identify the appearance of the building using the generation AI. For example, the analysis unit can identify the shape and color of the building from the image information. The analysis unit can also identify the height of the building using the generation AI. For example, the analysis unit can identify the number of floors of the building from the image information. This makes it easier for visually impaired people to understand their current location by identifying the positions of surrounding buildings. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input image information to the generation AI and cause the generation AI to identify the positions of the buildings.

[0033] The feedback unit can provide information through audio guidance. For example, the feedback unit generates information analyzed using a generation AI in natural language and provides it to the visually impaired person through the audio guidance. For example, the feedback unit provides specific information by voice, such as, "There is a crosswalk ahead. The traffic light is green. There is a building on the right." The feedback unit can also use the generation AI to provide information about the visually impaired person's current location in real time. For example, the feedback unit provides information by voice, such as, "Your current location is ____. The next intersection is 50 meters away." The feedback unit can also use the generation AI to provide information based on the visually impaired person's behavior. For example, the feedback unit provides warning information by voice, such as, "There is an obstacle ahead. Be careful." This allows the visually impaired person to receive information about their surrounding environment through the audio guidance. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can feedback information using a generation AI model that receives information analyzed by the analysis unit and outputs audio feedback.

[0034] The location acquisition unit can acquire location information using GPS or Wi-Fi. Location information includes, but is not limited to, GPS coordinates and Wi-Fi access point location information. The location acquisition unit acquires location information by receiving, for example, a GPS signal. For example, the location acquisition unit can grasp the current location of a visually impaired person in real time. The location acquisition unit can also supplement the location information using Wi-Fi access point location information. For example, the location acquisition unit can analyze the strength of a Wi-Fi signal to identify the location of a visually impaired person. The location acquisition unit can also acquire location information by using a Bluetooth signal. For example, the location acquisition unit can receive a Bluetooth beacon signal to identify the location information. This allows a visually impaired person to obtain accurate location information. Some or all of the above-described processing in the location acquisition unit may be performed using, or without, a generation AI. For example, the location acquisition unit can input a GPS signal to the generation AI and cause the generation AI to identify the location information.

[0035] The image acquisition unit can simultaneously acquire surrounding audio information when acquiring image information and use it for analysis. The image acquisition unit, for example, uses a generation AI to acquire surrounding audio information simultaneously with the image information and use it for analysis. For example, the image acquisition unit can acquire surrounding audio information simultaneously with the image information and identify the position of an obstacle on the sidewalk. The image acquisition unit can also acquire surrounding audio information simultaneously with the image information and identify the status of a traffic light. For example, the image acquisition unit can acquire surrounding audio information simultaneously with the image information and identify the positions of surrounding buildings. By using the surrounding audio information for analysis, the accuracy of environmental recognition is improved. Some or all of the above-mentioned processing in the image acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the image acquisition unit can input surrounding audio data to a generation AI and have the generation AI analyze the audio information.

[0036] When acquiring image information, the image acquisition unit can select an appropriate image acquisition method by taking into account light intensity or weather conditions. The image acquisition unit, for example, uses a generation AI to select the optimal image acquisition method by taking into account light intensity and weather conditions. For example, the image acquisition unit can acquire images by adjusting exposure in bright places by taking into account light intensity. The image acquisition unit can also acquire images using a waterproof camera in rainy weather by taking into account weather conditions. For example, the image acquisition unit can acquire images using an infrared camera at night by taking into account light intensity and weather conditions. This allows the optimal image to be acquired depending on light intensity and weather conditions. Some or all of the above-described processing in the image acquisition unit may be performed using, or without, the generation AI. For example, the image acquisition unit can input data on light intensity and weather conditions into the generation AI and have the generation AI select the optimal image acquisition method.

[0037] When acquiring image information, the image acquisition unit can adjust the acquisition frequency taking into account the user's walking speed. The image acquisition unit adjusts the image information acquisition frequency taking into account the user's walking speed, for example, using a generation AI. For example, the image acquisition unit can increase the image information acquisition frequency when the user is walking fast. The image acquisition unit can also decrease the image information acquisition frequency when the user is walking slowly. For example, the image acquisition unit can pause image information acquisition when the user is standing still. This allows image information to be acquired at an optimal frequency depending on the user's walking speed. Some or all of the above-described processing in the image acquisition unit may be performed using, or without, the generation AI. For example, the image acquisition unit can input the user's walking speed data to the generation AI and cause the generation AI to adjust the image information acquisition frequency.

[0038] When acquiring image information, the image acquisition unit can refer to the user's past movement history and prioritize acquisition of important points. The image acquisition unit, for example, uses a generation AI to refer to the user's past movement history and prioritize acquisition of important points. For example, the image acquisition unit can prioritize acquisition of image information of places that the user has frequently visited in the past. The image acquisition unit can also prioritize acquisition of image information of places that the user has avoided in the past. For example, the image acquisition unit can prioritize acquisition of image information of important points from the user's past movement history. This allows image information of important points to be prioritized based on the user's past movement history. Some or all of the above-described processing in the image acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the image acquisition unit can input the user's past movement history data to the generation AI and cause the generation AI to acquire image information of important points.

[0039] When acquiring location information, the location acquisition unit can select an appropriate location information technology by taking into account the surrounding wireless signal strength. The location acquisition unit, for example, uses a generation AI to select the optimal location information technology by taking into account the surrounding wireless signal strength. For example, the location acquisition unit can acquire location information using GPS in a location with a strong signal. The location acquisition unit can also acquire location information using Wi-Fi in a location with a weak signal. For example, the location acquisition unit can acquire location information using Bluetooth in a location with an unstable signal. This allows the optimal location information technology to be selected depending on the wireless signal strength. Some or all of the above-described processing in the location acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the location acquisition unit can input wireless signal strength data to the generation AI and cause the generation AI to select the optimal location information technology.

[0040] When acquiring location information, the location acquisition unit can analyze the user's movement pattern and select an appropriate acquisition method. The location acquisition unit, for example, uses a generation AI to analyze the user's movement pattern and select an optimal location information acquisition method. For example, the location acquisition unit can acquire highly accurate location information in places where the user moves frequently. The location acquisition unit can also acquire less accurate location information in places where the user moves less. For example, the location acquisition unit can acquire location information in real time in places where the user moves during specific time periods. This allows optimal location information to be acquired based on the user's movement pattern. Some or all of the above-described processing in the location acquisition unit may be performed using, or without, the generation AI. For example, the location acquisition unit can input the user's movement pattern data into the generation AI and cause the generation AI to select an optimal location information acquisition method.

[0041] When acquiring location information, the location acquisition unit can adjust the acquisition frequency based on the remaining battery level of the user's device. The location acquisition unit, for example, uses a generation AI to adjust the acquisition frequency of location information taking into account the remaining battery level of the user's device. For example, the location acquisition unit can reduce the acquisition frequency of location information when the remaining battery level of the user's device is low. Furthermore, the location acquisition unit can increase the acquisition frequency of location information when the remaining battery level of the user's device is high. For example, the location acquisition unit can appropriately adjust the acquisition frequency of location information when the remaining battery level of the user's device is medium. This allows location information to be acquired at an optimal frequency depending on the remaining battery level of the device. Some or all of the above-described processing in the location acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the location acquisition unit can input remaining battery level data of the device to the generation AI and cause the generation AI to adjust the acquisition frequency of location information.

[0042] When acquiring location information, the location acquisition unit can refer to the user's past location information and prioritize acquisition of important points. The location acquisition unit, for example, uses a generation AI to refer to the user's past location information and prioritize acquisition of important points. For example, the location acquisition unit can prioritize acquisition of location information of places the user has frequently visited in the past. The location acquisition unit can also prioritize acquisition of location information of places the user has avoided in the past. For example, the location acquisition unit can prioritize acquisition of location information of important points from the user's past location information. This allows location information of important points to be prioritized based on the user's past location information. Some or all of the above-described processing in the location acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the location acquisition unit can input the user's past location information data to the generation AI and cause the generation AI to acquire location information of important points.

[0043] During analysis, the analysis unit can add surrounding audio information to the analysis to improve the accuracy of environmental recognition. The analysis unit, for example, uses a generation AI to add surrounding audio information to the analysis to improve the accuracy of environmental recognition. For example, the analysis unit can analyze surrounding audio information to identify the position of an obstacle on a sidewalk. The analysis unit can also analyze surrounding audio information to identify the status of a traffic light. For example, the analysis unit can analyze surrounding audio information to identify the positions of surrounding buildings. In this way, adding surrounding audio information to the analysis improves the accuracy of environmental recognition. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input surrounding audio data to the generation AI and cause the generation AI to analyze the audio information.

[0044] During analysis, the analysis unit can improve the accuracy of the current analysis by referring to past analysis data. The analysis unit, for example, uses a generation AI to refer to past analysis data and improve the accuracy of the current analysis. For example, the analysis unit can refer to past analysis data to identify the position of an obstacle on the sidewalk. The analysis unit can also refer to past analysis data to identify the status of a traffic light. For example, the analysis unit can refer to past analysis data to identify the positions of surrounding buildings. In this way, the current analysis accuracy is improved by referring to the past analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past analysis data into the generation AI and have the generation AI improve the accuracy of the current analysis.

[0045] During analysis, the analysis unit can add ambient temperature or humidity information to the analysis to improve the accuracy of environmental recognition. The analysis unit, for example, uses a generation AI to add ambient temperature and humidity information to the analysis to improve the accuracy of environmental recognition. For example, the analysis unit can analyze ambient temperature information to identify the location of an obstacle on a sidewalk. The analysis unit can also analyze ambient humidity information to identify the status of a traffic light. For example, the analysis unit can analyze ambient temperature and humidity information to identify the locations of surrounding buildings. By adding ambient temperature and humidity information to the analysis, the accuracy of environmental recognition is improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input ambient temperature and humidity data to the generation AI and cause the generation AI to improve the accuracy of environmental recognition.

[0046] During analysis, the analysis unit can refer to the user's past movement history and prioritize analysis of important points. The analysis unit, for example, uses a generation AI to refer to the user's past movement history and prioritize analysis of important points. For example, the analysis unit can prioritize analysis of places the user has frequently visited in the past. The analysis unit can also prioritize analysis of places the user has avoided in the past. For example, the analysis unit can prioritize analysis of important points from the user's past movement history. This allows prioritized analysis of important points based on the user's past movement history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past movement history data into the generation AI and cause the generation AI to analyze important points.

[0047] When providing feedback, the feedback unit can select an optimal feedback method by referring to the user's past feedback history. The feedback unit, for example, uses a generation AI to refer to the user's past feedback history and select an optimal feedback method. For example, the feedback unit can refer to the user's past feedback history and select a preferred feedback method. The feedback unit can also refer to the user's past feedback history and select an avoided feedback method. For example, the feedback unit can refer to the user's past feedback history and select an optimal feedback method. This allows the optimal feedback method to be selected based on the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's past feedback history data into the generation AI and cause the generation AI to select an optimal feedback method.

[0048] The feedback unit can customize the feedback content based on the user's current activity status when providing feedback. The feedback unit, for example, uses a generation AI to customize the feedback content by taking the user's current activity status into consideration. For example, the feedback unit can provide brief feedback when the user is walking. The feedback unit can also provide detailed feedback when the user is resting. For example, the feedback unit can provide encouraging feedback when the user is exercising. This makes it possible to provide optimal feedback content according to the user's current activity status. Some or all of the above-described processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input the user's activity status data into the generation AI and cause the generation AI to customize the feedback content.

[0049] The feedback unit can adjust the audio output of the feedback taking into account the volume setting of the user's device when providing feedback. The feedback unit can adjust the audio output of the feedback taking into account the volume setting of the user's device, for example, using a generation AI. For example, the feedback unit can increase the audio output when the volume setting of the user's device is low. The feedback unit can also decrease the audio output when the volume setting of the user's device is high. For example, the feedback unit can appropriately adjust the audio output when the volume setting of the user's device is medium. This makes it possible to provide feedback with optimal audio output according to the volume setting of the device. Some or all of the above-described processing in the feedback unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input device volume setting data to the generation AI and cause the generation AI to adjust the audio output.

[0050] When providing feedback, the feedback unit can refer to the user's past movement history and prioritize feedback of important points. The feedback unit, for example, uses a generation AI to refer to the user's past movement history and prioritize feedback of important points. For example, the feedback unit can prioritize feedback of information about places the user has frequently visited in the past. The feedback unit can also prioritize feedback of information about places the user has avoided in the past. For example, the feedback unit can prioritize feedback of information about important points from the user's past movement history. This allows priority feedback of information about important points based on the user's past movement history. Some or all of the above-described processing in the feedback unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's past movement history data into the generation AI and cause the generation AI to perform feedback of important points.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The image acquisition unit can refer to the user's past movement history and preferentially acquire image information of important points. For example, it can preferentially acquire image information of places the user has frequently visited in the past. It can also preferentially acquire image information of places the user has avoided in the past. Furthermore, it can preferentially acquire image information of important points from the user's past movement history. This allows image information of important points to be preferentially acquired based on the user's past movement history.

[0053] When acquiring image information, the image acquisition unit can select the optimal image acquisition method taking into account the ambient temperature and humidity. For example, if the temperature is high, the image acquisition unit can acquire images using a heat-resistant camera. If the humidity is high, the image acquisition unit can also acquire images using a moisture-proof camera. Furthermore, the optimal image acquisition method can be selected based on the temperature and humidity data. This allows the optimal image to be acquired according to the environmental conditions.

[0054] The location acquisition unit can adjust the frequency of acquiring location information taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the acquisition unit can reduce the frequency of acquiring location information. When the remaining battery level is high, the location acquisition unit can increase the frequency of acquiring location information. Furthermore, when the remaining battery level is medium, the location acquisition unit can appropriately adjust the frequency of acquiring location information. This allows location information to be acquired at an optimal frequency depending on the remaining battery level of the device.

[0055] The image acquisition unit can adjust the frequency of acquiring image information taking into account the user's walking speed. For example, if the user is walking fast, the image acquisition unit can increase the frequency of acquiring image information. Also, if the user is walking slowly, the image acquisition unit can decrease the frequency of acquiring image information. Furthermore, if the user is standing still, the image acquisition unit can temporarily suspend the acquisition of image information. This allows image information to be acquired at an optimal frequency according to the user's walking speed.

[0056] The analysis unit can improve the accuracy of environmental recognition by adding surrounding audio information to the analysis. For example, the analysis unit can identify the location of an obstacle on the sidewalk by analyzing the surrounding audio information. The analysis unit can also identify the status of a traffic light by analyzing the surrounding audio information. Furthermore, the analysis unit can identify the locations of surrounding buildings by analyzing the surrounding audio information. In this way, the accuracy of environmental recognition is improved by adding the surrounding audio information to the analysis.

[0057] The feedback unit can select an optimal feedback method by referring to the user's past feedback history. For example, it can select a preferred feedback method by referring to the user's past feedback history. It can also select a feedback method to be avoided by referring to the user's past feedback history. It can also select an optimal feedback method by referring to the user's past feedback history. In this way, it is possible to select an optimal feedback method based on the user's past feedback history.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The image acquisition unit acquires image information using a wearable camera worn by the visually impaired person. For example, the image acquisition unit can acquire image information of the surroundings in real time while the visually impaired person is walking. The image acquisition unit can, for example, acquire still images and videos in high resolution. The image acquisition unit can also use generative AI to convert the image information into a format that is easy to analyze. Step 2: The location acquisition unit acquires accurate location information using GPS or other location information technology. For example, the location acquisition unit can grasp the current location of the visually impaired person in real time. For example, the location acquisition unit receives GPS signals to acquire location information. The location acquisition unit can also supplement the location information using technologies such as Wi-Fi and Bluetooth. Step 3: The analysis unit analyzes the information acquired by the image acquisition unit and the location acquisition unit. The analysis unit uses generative AI to integrate the image information and location information and recognize the surrounding environment. For example, the analysis unit can identify obstacles on the sidewalk. The analysis unit can also identify the status of traffic lights. Furthermore, the analysis unit can identify the locations of surrounding buildings. Step 4: The feedback unit provides the information analyzed by the analysis unit via voice. The feedback unit uses generative AI to generate information in natural language and provides it to the visually impaired person. For example, the feedback unit may provide specific information via voice, such as, "There is a crosswalk ahead. The traffic light is green. There is a building on the right." This allows the visually impaired person to instantly receive information about their surroundings.

[0060] (Example 2) An assistance system for visually impaired persons according to an embodiment of the present invention uses a generation AI to analyze space based on image information acquired by a wearable camera, accurate location information, and highly accurate map information. The system then utilizes 5G communications to provide low-latency, real-time information to the user in natural language. This system enables visually impaired persons to gain timely understanding of their current environment without the intervention of sighted persons. First, a visually impaired person wearing a wearable camera captures image information of their surroundings while walking. This image information is sent to a generation AI, which analyzes the images and recognizes the surrounding environment. For example, it identifies obstacles on the sidewalk, the status of traffic lights, and the locations of surrounding buildings. Next, the accurate location information and highly accurate map information are combined to generate detailed environmental information about the current location. Location information is acquired using GPS or other location information technologies, and highly accurate map information is acquired from a service that provides detailed map data. This allows visually impaired persons to accurately grasp their current location. Furthermore, 5G communications are utilized to provide information with low latency and excellent real-time performance. This allows visually impaired persons to instantly receive information about their surrounding environment and make timely decisions. For example, they can know in real time when the traffic light has turned green or if there is an obstacle ahead. Finally, the information analyzed by the generative AI is fed back in natural language. Visually impaired people can receive information about their surrounding environment through audio guidance, allowing them to act independently without the intervention of sighted people. For example, specific information such as "There is a crosswalk ahead. The traffic light is green. There is a building on the right" is provided by voice. This system enables visually impaired people to understand the environment of their current location in a timely manner, enabling safe and independent movement. In this way, the support system for visually impaired people can provide visually impaired people with a timely understanding of the environment of their current location.

[0061] An assistance system for visually impaired persons according to an embodiment includes an image acquisition unit, a position acquisition unit, an analysis unit, and a feedback unit. The image acquisition unit acquires image information using a wearable camera worn by the visually impaired person. For example, the image acquisition unit can acquire image information of the surroundings in real time while the visually impaired person is walking. The image acquisition unit can acquire, for example, still images and videos in high resolution. The image acquisition unit can also convert the image information into a format that is easy to analyze using a generative AI. The position acquisition unit acquires accurate position information using GPS or other position information technology. For example, the position acquisition unit can determine the current location of the visually impaired person in real time. The position acquisition unit acquires position information by receiving, for example, a GPS signal. The position acquisition unit can also supplement the position information using technologies such as Wi-Fi and Bluetooth. The analysis unit analyzes the information acquired by the image acquisition unit and the position acquisition unit. The analysis unit uses a generative AI to integrate the image information and position information and recognize the surrounding environment. For example, the analysis unit can identify obstacles on the sidewalk. The analysis unit can also identify the status of traffic lights. Furthermore, the analysis unit can also identify the locations of surrounding buildings. The feedback unit provides audio feedback of the information analyzed by the analysis unit. The feedback unit generates information in natural language using a generative AI and provides it to the visually impaired person. For example, the feedback unit provides specific information by audio, such as, "There is a crosswalk ahead. The traffic light is green. There is a building on the right." This allows the visually impaired person to instantly receive information about their surrounding environment. This allows the assistance system for visually impaired persons according to the embodiment to provide a timely understanding of the environment of their current location. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the feedback unit may provide information feedback using a generative AI model that receives information analyzed by the analysis unit and outputs audio feedback.

[0062] The analysis unit can identify obstacles on the sidewalk. The analysis unit, for example, uses a generation AI to analyze image information and identify obstacles on the sidewalk. For example, the analysis unit can identify the position of sidewalk steps and obstacles from the image information. The analysis unit can also identify the size and shape of the obstacle using the generation AI. For example, the analysis unit can identify the height and width of the obstacle from the image information. The analysis unit can also identify the type of obstacle using the generation AI. For example, the analysis unit can identify whether the obstacle is a wheelchair ramp or a pedestrian step from the image information. This allows visually impaired people to walk safely by identifying obstacles on the sidewalk. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input image information to the generation AI and have the generation AI identify the obstacle.

[0063] The analysis unit can identify the status of a traffic light. The analysis unit, for example, uses a generation AI to analyze image information and identify the status of the traffic light. For example, the analysis unit can identify the color of the traffic light from the image information. The analysis unit can also identify the flashing status of the traffic light using the generation AI. For example, the analysis unit can identify whether the traffic light is red, green, or flashing from the image information. The analysis unit can also identify the location of the traffic light using the generation AI. For example, the analysis unit can identify the location of the traffic light at an intersection from the image information. This allows visually impaired people to cross the road safely by identifying the status of the traffic light. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input image information to the generation AI and have the generation AI identify the status of the traffic light.

[0064] The analysis unit can identify the positions of surrounding buildings. The analysis unit, for example, uses a generation AI to analyze image information and identify the positions of surrounding buildings. For example, the analysis unit can identify the GPS coordinates of the building from the image information. The analysis unit can also identify the appearance of the building using the generation AI. For example, the analysis unit can identify the shape and color of the building from the image information. The analysis unit can also identify the height of the building using the generation AI. For example, the analysis unit can identify the number of floors of the building from the image information. This makes it easier for visually impaired people to understand their current location by identifying the positions of surrounding buildings. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input image information to the generation AI and cause the generation AI to identify the positions of the buildings.

[0065] The feedback unit can provide information through audio guidance. For example, the feedback unit generates information analyzed using a generation AI in natural language and provides it to the visually impaired person through the audio guidance. For example, the feedback unit provides specific information by voice, such as, "There is a crosswalk ahead. The traffic light is green. There is a building on the right." The feedback unit can also use the generation AI to provide information about the visually impaired person's current location in real time. For example, the feedback unit provides information by voice, such as, "Your current location is ____. The next intersection is 50 meters away." The feedback unit can also use the generation AI to provide information based on the visually impaired person's behavior. For example, the feedback unit provides warning information by voice, such as, "There is an obstacle ahead. Be careful." This allows the visually impaired person to receive information about their surrounding environment through the audio guidance. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can feedback information using a generation AI model that receives information analyzed by the analysis unit and outputs audio feedback.

[0066] The location acquisition unit can acquire location information using GPS or Wi-Fi. Location information includes, but is not limited to, GPS coordinates and Wi-Fi access point location information. The location acquisition unit acquires location information by receiving, for example, a GPS signal. For example, the location acquisition unit can grasp the current location of a visually impaired person in real time. The location acquisition unit can also supplement the location information using Wi-Fi access point location information. For example, the location acquisition unit can analyze the strength of a Wi-Fi signal to identify the location of a visually impaired person. The location acquisition unit can also acquire location information by using a Bluetooth signal. For example, the location acquisition unit can receive a Bluetooth beacon signal to identify the location information. This allows a visually impaired person to obtain accurate location information. Some or all of the above-described processing in the location acquisition unit may be performed using, or without, a generation AI. For example, the location acquisition unit can input a GPS signal to the generation AI and cause the generation AI to identify the location information.

[0067] The image acquisition unit can estimate the user's emotions and adjust the timing of image information acquisition based on the estimated user emotions. The image acquisition unit, for example, uses a generation AI to estimate the user's emotions and adjust the timing of image information acquisition. For example, when the user is nervous, the image acquisition unit can increase the frequency of image information acquisition and provide detailed environmental information. Furthermore, when the user is relaxed, the image acquisition unit can reduce the frequency of image information acquisition and provide the minimum necessary information. For example, when the user is in a hurry, the image acquisition unit can speed up the timing of image information acquisition and provide information in real time. This allows image information to be acquired at the optimal timing depending on the user's emotions. Some or all of the above-described processing in the image acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the image acquisition unit can input user emotion data to the generation AI and cause the generation AI to adjust the timing of image information acquisition.

[0068] The image acquisition unit can simultaneously acquire surrounding audio information when acquiring image information and use it for analysis. The image acquisition unit, for example, uses a generation AI to acquire surrounding audio information simultaneously with the image information and use it for analysis. For example, the image acquisition unit can acquire surrounding audio information simultaneously with the image information and identify the position of an obstacle on the sidewalk. The image acquisition unit can also acquire surrounding audio information simultaneously with the image information and identify the status of a traffic light. For example, the image acquisition unit can acquire surrounding audio information simultaneously with the image information and identify the positions of surrounding buildings. By using the surrounding audio information for analysis, the accuracy of environmental recognition is improved. Some or all of the above-mentioned processing in the image acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the image acquisition unit can input surrounding audio data to a generation AI and have the generation AI analyze the audio information.

[0069] When acquiring image information, the image acquisition unit can select an appropriate image acquisition method by taking into account light intensity or weather conditions. The image acquisition unit, for example, uses a generation AI to select the optimal image acquisition method by taking into account light intensity and weather conditions. For example, the image acquisition unit can acquire images by adjusting exposure in bright places by taking into account light intensity. The image acquisition unit can also acquire images using a waterproof camera in rainy weather by taking into account weather conditions. For example, the image acquisition unit can acquire images using an infrared camera at night by taking into account light intensity and weather conditions. This allows the optimal image to be acquired depending on light intensity and weather conditions. Some or all of the above-described processing in the image acquisition unit may be performed using, or without, the generation AI. For example, the image acquisition unit can input data on light intensity and weather conditions into the generation AI and have the generation AI select the optimal image acquisition method.

[0070] The image acquisition unit can estimate the user's emotions and determine the priority of image information to be acquired based on the estimated user's emotions. The image acquisition unit can estimate the user's emotions using, for example, a generation AI and determine the priority of image information to be acquired. For example, if the user is nervous, the image acquisition unit can prioritize acquiring image information of obstacles on the sidewalk. Also, if the user is relaxed, the image acquisition unit can prioritize acquiring image information of the positions of surrounding buildings. For example, if the user is in a hurry, the image acquisition unit can prioritize acquiring image information of the status of traffic lights. This allows important image information to be prioritized according to the user's emotions. Some or all of the above-described processing in the image acquisition unit can be performed using, or without, the generation AI. For example, the image acquisition unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of image information.

[0071] When acquiring image information, the image acquisition unit can adjust the acquisition frequency taking into account the user's walking speed. The image acquisition unit adjusts the image information acquisition frequency taking into account the user's walking speed, for example, using a generation AI. For example, the image acquisition unit can increase the image information acquisition frequency when the user is walking fast. The image acquisition unit can also decrease the image information acquisition frequency when the user is walking slowly. For example, the image acquisition unit can pause image information acquisition when the user is standing still. This allows image information to be acquired at an optimal frequency depending on the user's walking speed. Some or all of the above-described processing in the image acquisition unit may be performed using, or without, the generation AI. For example, the image acquisition unit can input the user's walking speed data to the generation AI and cause the generation AI to adjust the image information acquisition frequency.

[0072] When acquiring image information, the image acquisition unit can refer to the user's past movement history and prioritize acquisition of important points. The image acquisition unit, for example, uses a generation AI to refer to the user's past movement history and prioritize acquisition of important points. For example, the image acquisition unit can prioritize acquisition of image information of places that the user has frequently visited in the past. The image acquisition unit can also prioritize acquisition of image information of places that the user has avoided in the past. For example, the image acquisition unit can prioritize acquisition of image information of important points from the user's past movement history. This allows image information of important points to be prioritized based on the user's past movement history. Some or all of the above-described processing in the image acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the image acquisition unit can input the user's past movement history data to the generation AI and cause the generation AI to acquire image information of important points.

[0073] The location acquisition unit can estimate the user's emotions and adjust the timing of acquiring location information based on the estimated user emotions. The location acquisition unit can estimate the user's emotions using, for example, a generation AI and adjust the timing of acquiring location information. For example, the location acquisition unit can increase the frequency of acquiring location information when the user is nervous. The location acquisition unit can also decrease the frequency of acquiring location information when the user is relaxed. For example, the location acquisition unit can speed up the timing of acquiring location information when the user is in a hurry. This allows location information to be acquired at the optimal timing depending on the user's emotions. Some or all of the above-described processing in the location acquisition unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the location acquisition unit can input user emotion data to the generation AI and cause the generation AI to adjust the timing of acquiring location information.

[0074] When acquiring location information, the location acquisition unit can select an appropriate location information technology by taking into account the surrounding wireless signal strength. The location acquisition unit, for example, uses a generation AI to select the optimal location information technology by taking into account the surrounding wireless signal strength. For example, the location acquisition unit can acquire location information using GPS in a location with a strong signal. The location acquisition unit can also acquire location information using Wi-Fi in a location with a weak signal. For example, the location acquisition unit can acquire location information using Bluetooth in a location with an unstable signal. This allows the optimal location information technology to be selected depending on the wireless signal strength. Some or all of the above-described processing in the location acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the location acquisition unit can input wireless signal strength data to the generation AI and cause the generation AI to select the optimal location information technology.

[0075] When acquiring location information, the location acquisition unit can analyze the user's movement pattern and select an appropriate acquisition method. The location acquisition unit, for example, uses a generation AI to analyze the user's movement pattern and select an optimal location information acquisition method. For example, the location acquisition unit can acquire highly accurate location information in places where the user moves frequently. The location acquisition unit can also acquire less accurate location information in places where the user moves less. For example, the location acquisition unit can acquire location information in real time in places where the user moves during specific time periods. This allows optimal location information to be acquired based on the user's movement pattern. Some or all of the above-described processing in the location acquisition unit may be performed using, or without, the generation AI. For example, the location acquisition unit can input the user's movement pattern data into the generation AI and cause the generation AI to select an optimal location information acquisition method.

[0076] The location acquisition unit can estimate the user's emotions and determine the priority of location information to be acquired based on the estimated user's emotions. The location acquisition unit can estimate the user's emotions using, for example, a generation AI and determine the priority of location information to be acquired. For example, when the user is nervous, the location acquisition unit can prioritize acquiring important location information. The location acquisition unit can also prioritize acquiring detailed location information when the user is relaxed. For example, when the user is in a hurry, the location acquisition unit can prioritize acquiring location information that can be acquired quickly. This allows important location information to be acquired preferentially depending on the user's emotions. Some or all of the above-described processing in the location acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the location acquisition unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of location information.

[0077] When acquiring location information, the location acquisition unit can adjust the acquisition frequency based on the remaining battery level of the user's device. The location acquisition unit, for example, uses a generation AI to adjust the acquisition frequency of location information taking into account the remaining battery level of the user's device. For example, the location acquisition unit can reduce the acquisition frequency of location information when the remaining battery level of the user's device is low. Furthermore, the location acquisition unit can increase the acquisition frequency of location information when the remaining battery level of the user's device is high. For example, the location acquisition unit can appropriately adjust the acquisition frequency of location information when the remaining battery level of the user's device is medium. This allows location information to be acquired at an optimal frequency depending on the remaining battery level of the device. Some or all of the above-described processing in the location acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the location acquisition unit can input remaining battery level data of the device to the generation AI and cause the generation AI to adjust the acquisition frequency of location information.

[0078] When acquiring location information, the location acquisition unit can refer to the user's past location information and prioritize acquisition of important points. The location acquisition unit, for example, uses a generation AI to refer to the user's past location information and prioritize acquisition of important points. For example, the location acquisition unit can prioritize acquisition of location information of places the user has frequently visited in the past. The location acquisition unit can also prioritize acquisition of location information of places the user has avoided in the past. For example, the location acquisition unit can prioritize acquisition of location information of important points from the user's past location information. This allows location information of important points to be prioritized based on the user's past location information. Some or all of the above-described processing in the location acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the location acquisition unit can input the user's past location information data to the generation AI and cause the generation AI to acquire location information of important points.

[0079] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, a generation AI and determine the analysis priorities. For example, if the user is nervous, the analysis unit can prioritize analyzing obstacles on the sidewalk. Also, if the user is relaxed, the analysis unit can prioritize analyzing the locations of surrounding buildings. For example, if the user is in a hurry, the analysis unit can prioritize analyzing the status of traffic lights. This allows important information to be analyzed preferentially depending on the user's emotions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0080] During analysis, the analysis unit can add surrounding audio information to the analysis to improve the accuracy of environmental recognition. The analysis unit, for example, uses a generation AI to add surrounding audio information to the analysis to improve the accuracy of environmental recognition. For example, the analysis unit can analyze surrounding audio information to identify the position of an obstacle on a sidewalk. The analysis unit can also analyze surrounding audio information to identify the status of a traffic light. For example, the analysis unit can analyze surrounding audio information to identify the positions of surrounding buildings. In this way, adding surrounding audio information to the analysis improves the accuracy of environmental recognition. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input surrounding audio data to the generation AI and cause the generation AI to analyze the audio information.

[0081] During analysis, the analysis unit can improve the accuracy of the current analysis by referring to past analysis data. The analysis unit, for example, uses a generation AI to refer to past analysis data and improve the accuracy of the current analysis. For example, the analysis unit can refer to past analysis data to identify the position of an obstacle on the sidewalk. The analysis unit can also refer to past analysis data to identify the status of a traffic light. For example, the analysis unit can refer to past analysis data to identify the positions of surrounding buildings. In this way, the current analysis accuracy is improved by referring to the past analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past analysis data into the generation AI and have the generation AI improve the accuracy of the current analysis.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, uses a generation AI to estimate the user's emotions and adjust the display method of the analysis results. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the analysis results to be provided in an optimal display method depending on the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0083] During analysis, the analysis unit can add ambient temperature or humidity information to the analysis to improve the accuracy of environmental recognition. The analysis unit, for example, uses a generation AI to add ambient temperature and humidity information to the analysis to improve the accuracy of environmental recognition. For example, the analysis unit can analyze ambient temperature information to identify the location of an obstacle on a sidewalk. The analysis unit can also analyze ambient humidity information to identify the status of a traffic light. For example, the analysis unit can analyze ambient temperature and humidity information to identify the locations of surrounding buildings. By adding ambient temperature and humidity information to the analysis, the accuracy of environmental recognition is improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input ambient temperature and humidity data to the generation AI and cause the generation AI to improve the accuracy of environmental recognition.

[0084] During analysis, the analysis unit can refer to the user's past movement history and prioritize analysis of important points. The analysis unit, for example, uses a generation AI to refer to the user's past movement history and prioritize analysis of important points. For example, the analysis unit can prioritize analysis of places the user has frequently visited in the past. The analysis unit can also prioritize analysis of places the user has avoided in the past. For example, the analysis unit can prioritize analysis of important points from the user's past movement history. This allows prioritized analysis of important points based on the user's past movement history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past movement history data into the generation AI and cause the generation AI to analyze important points.

[0085] The feedback unit can estimate the user's emotions and adjust the feedback expression method based on the estimated user's emotions. The feedback unit can estimate the user's emotions using, for example, a generation AI and adjust the feedback expression method. For example, if the user is nervous, the feedback unit can provide feedback in a calm voice. Also, if the user is relaxed, the feedback unit can provide feedback in a cheerful voice. For example, if the user is in a hurry, the feedback unit can provide quick and concise feedback. This makes it possible to provide feedback in an optimal expression method depending on the user's emotions. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback expression method.

[0086] When providing feedback, the feedback unit can select an optimal feedback method by referring to the user's past feedback history. The feedback unit, for example, uses a generation AI to refer to the user's past feedback history and select an optimal feedback method. For example, the feedback unit can refer to the user's past feedback history and select a preferred feedback method. The feedback unit can also refer to the user's past feedback history and select an avoided feedback method. For example, the feedback unit can refer to the user's past feedback history and select an optimal feedback method. This allows the optimal feedback method to be selected based on the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's past feedback history data into the generation AI and cause the generation AI to select an optimal feedback method.

[0087] The feedback unit can customize the feedback content based on the user's current activity status when providing feedback. The feedback unit, for example, uses a generation AI to customize the feedback content by taking the user's current activity status into consideration. For example, the feedback unit can provide brief feedback when the user is walking. The feedback unit can also provide detailed feedback when the user is resting. For example, the feedback unit can provide encouraging feedback when the user is exercising. This makes it possible to provide optimal feedback content according to the user's current activity status. Some or all of the above-described processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input the user's activity status data into the generation AI and cause the generation AI to customize the feedback content.

[0088] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The feedback unit can estimate the user's emotions using, for example, a generation AI and determine the priority of feedback. For example, if the user is nervous, the feedback unit can prioritize feedback of important information. The feedback unit can also prioritize feedback of detailed information if the user is relaxed. For example, if the user is in a hurry, the feedback unit can prioritize feedback of information that can be fed back quickly. This allows important information to be prioritized according to the user's emotions. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input user's emotion data into the generation AI and have the generation AI determine the priority of feedback.

[0089] The feedback unit can adjust the audio output of the feedback taking into account the volume setting of the user's device when providing feedback. The feedback unit can adjust the audio output of the feedback taking into account the volume setting of the user's device, for example, using a generation AI. For example, the feedback unit can increase the audio output when the volume setting of the user's device is low. The feedback unit can also decrease the audio output when the volume setting of the user's device is high. For example, the feedback unit can appropriately adjust the audio output when the volume setting of the user's device is medium. This makes it possible to provide feedback with optimal audio output according to the volume setting of the device. Some or all of the above-described processing in the feedback unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input device volume setting data to the generation AI and cause the generation AI to adjust the audio output.

[0090] When providing feedback, the feedback unit can refer to the user's past movement history and prioritize feedback of important points. The feedback unit, for example, uses a generation AI to refer to the user's past movement history and prioritize feedback of important points. For example, the feedback unit can prioritize feedback of information about places the user has frequently visited in the past. The feedback unit can also prioritize feedback of information about places the user has avoided in the past. For example, the feedback unit can prioritize feedback of information about important points from the user's past movement history. This allows priority feedback of information about important points based on the user's past movement history. Some or all of the above-described processing in the feedback unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's past movement history data into the generation AI and cause the generation AI to perform feedback of important points. === Hard Collateral 1-1 === Each of the multiple elements, including the image acquisition unit, position acquisition unit, analysis unit, and feedback unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the image acquisition unit acquires image information using the camera 42 of the smart device 14. The position acquisition unit receives GPS signals via the communication I / F 26 of the data processing device 12 and acquires accurate position information. The analysis unit integrates the image information and position information using the specific processing unit 290 of the data processing device 12 to recognize the surrounding environment. The feedback unit provides audio feedback of the information analyzed using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the image acquisition unit, position acquisition unit, analysis unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the image acquisition unit acquires image information using the camera 42 of the smart glasses 214. The position acquisition unit receives GPS signals via the communication I / F 26 of the data processing device 12 and acquires accurate position information. The analysis unit integrates the image information and the position information using the specific processing unit 290 of the data processing device 12 to recognize the surrounding environment. The feedback unit provides audio feedback of the analyzed information using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the image acquisition unit, position acquisition unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the image acquisition unit acquires image information using the camera 42 of the headset type terminal 314. The position acquisition unit receives GPS signals via the communication I / F 26 of the data processing device 12 and acquires accurate position information. The analysis unit integrates the image information and position information using the specific processing unit 290 of the data processing device 12 to recognize the surrounding environment. The feedback unit provides audio feedback of the information analyzed using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the image acquisition unit, position acquisition unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the image acquisition unit acquires image information using the camera 42 of the robot 414. The position acquisition unit receives GPS signals via the communication I / F 26 of the data processing device 12 and acquires accurate position information. The analysis unit integrates the image information and position information using the specific processing unit 290 of the data processing device 12 to recognize the surrounding environment. The feedback unit provides audio feedback of the information analyzed using the control unit 46A of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide concise, to-the-point information. If the user is relaxed, the analysis unit can also provide detailed information. Furthermore, if the user is in a hurry, the analysis unit can prioritize providing information that can be quickly understood. This makes it possible to provide optimal information according to the user's emotions.

[0093] The image acquisition unit can refer to the user's past movement history and preferentially acquire image information of important points. For example, it can preferentially acquire image information of places the user has frequently visited in the past. It can also preferentially acquire image information of places the user has avoided in the past. Furthermore, it can preferentially acquire image information of important points from the user's past movement history. This allows image information of important points to be preferentially acquired based on the user's past movement history.

[0094] The feedback unit can estimate the user's emotion and adjust the tone of the feedback based on the estimated emotion. For example, if the user is nervous, the feedback unit can provide information in a calm tone. If the user is relaxed, the feedback unit can provide information in a bright tone. Furthermore, if the user is in a hurry, the feedback unit can provide information in a quick and concise tone. This allows feedback to be provided in an optimal tone depending on the user's emotion.

[0095] When acquiring image information, the image acquisition unit can select the optimal image acquisition method taking into account the ambient temperature and humidity. For example, if the temperature is high, the image acquisition unit can acquire images using a heat-resistant camera. If the humidity is high, the image acquisition unit can also acquire images using a moisture-proof camera. Furthermore, the optimal image acquisition method can be selected based on the temperature and humidity data. This allows the optimal image to be acquired according to the environmental conditions.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This makes it possible to provide analysis results in the optimal display method depending on the user's emotions.

[0097] The location acquisition unit can adjust the frequency of acquiring location information taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the location acquisition unit can reduce the frequency of acquiring location information. When the remaining battery level is high, the location acquisition unit can increase the frequency of acquiring location information. Furthermore, when the remaining battery level is medium, the location acquisition unit can appropriately adjust the frequency of acquiring location information. This allows location information to be acquired at an optimal frequency depending on the remaining battery level of the device.

[0098] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is nervous, important information can be given priority as feedback. Also, if the user is relaxed, detailed information can be given priority as feedback. Furthermore, if the user is in a hurry, information that can be given quickly can be given priority as feedback. In this way, important information can be given priority as feedback according to the user's emotions.

[0099] The image acquisition unit can adjust the frequency of acquiring image information taking into account the user's walking speed. For example, if the user is walking fast, the image acquisition unit can increase the frequency of acquiring image information. Also, if the user is walking slowly, the image acquisition unit can decrease the frequency of acquiring image information. Furthermore, if the user is standing still, the image acquisition unit can temporarily suspend the acquisition of image information. This allows image information to be acquired at an optimal frequency according to the user's walking speed.

[0100] The analysis unit can improve the accuracy of environmental recognition by adding surrounding audio information to the analysis. For example, the analysis unit can identify the location of an obstacle on the sidewalk by analyzing the surrounding audio information. The analysis unit can also identify the status of a traffic light by analyzing the surrounding audio information. Furthermore, the analysis unit can identify the locations of surrounding buildings by analyzing the surrounding audio information. In this way, the accuracy of environmental recognition is improved by adding the surrounding audio information to the analysis.

[0101] The feedback unit can select an optimal feedback method by referring to the user's past feedback history. For example, it can select a preferred feedback method by referring to the user's past feedback history. It can also select a feedback method to be avoided by referring to the user's past feedback history. It can also select an optimal feedback method by referring to the user's past feedback history. In this way, it is possible to select an optimal feedback method based on the user's past feedback history.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The image acquisition unit acquires image information using a wearable camera worn by the visually impaired person. For example, the image acquisition unit can acquire image information of the surroundings in real time while the visually impaired person is walking. The image acquisition unit can, for example, acquire still images and videos in high resolution. The image acquisition unit can also use generative AI to convert the image information into a format that is easy to analyze. Step 2: The location acquisition unit acquires accurate location information using GPS or other location information technology. For example, the location acquisition unit can grasp the current location of the visually impaired person in real time. For example, the location acquisition unit receives GPS signals to acquire location information. The location acquisition unit can also supplement the location information using technologies such as Wi-Fi and Bluetooth. Step 3: The analysis unit analyzes the information acquired by the image acquisition unit and the location acquisition unit. The analysis unit uses generative AI to integrate the image information and location information and recognize the surrounding environment. For example, the analysis unit can identify obstacles on the sidewalk. The analysis unit can also identify the status of traffic lights. Furthermore, the analysis unit can identify the locations of surrounding buildings. Step 4: The feedback unit provides the information analyzed by the analysis unit via voice. The feedback unit uses generative AI to generate information in natural language and provides it to the visually impaired person. For example, the feedback unit may provide specific information via voice, such as, "There is a crosswalk ahead. The traffic light is green. There is a building on the right." This allows the visually impaired person to instantly receive information about their surroundings.

[0104] 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.

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

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[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 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.

[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 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.

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

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 image acquisition unit that acquires image information; a location acquisition unit that acquires location information; an analysis unit that analyzes the information acquired by the image acquisition unit and the position acquisition unit; a feedback unit that provides audio feedback of the information analyzed by the analysis unit; Equipped with A system characterized by:

2. The analysis unit Identify obstacles on the sidewalk 2. The system of claim 1.

3. The analysis unit Identify traffic light status 2. The system of claim 1.

4. The analysis unit Locate surrounding buildings 2. The system of claim 1.

5. The feedback unit Providing information through audio guides 2. The system of claim 1.

6. The position acquisition unit Obtain location information using GPS or Wi-Fi 2. The system of claim 1.

7. The image acquisition unit The system estimates the user's emotions and adjusts the timing of acquiring image information based on the estimated user emotions.

2. The system of claim 1.

8. The image acquisition unit When acquiring image information, surrounding audio information is also acquired and used for analysis.

2. The system of claim 1.

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