system

Abstract

The system according to this embodiment aims to support elderly people and people with disabilities so that they can walk safely. [Solution] The system according to the embodiment comprises a camera unit, an analysis unit, and a guidance unit. The camera unit acquires video data using a camera mounted on the walking stick. The analysis unit analyzes the video data acquired by the camera unit. The guidance unit provides appropriate guidance to the user based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, sufficient support for the safe walking of the elderly and physically disabled people has not been provided, and there is room for improvement. [[ID=3⑥]]

[0005] The system according to the embodiment aims to support the elderly and physically disabled people to walk safely.

Means for Solving the Problems

[0006] The system according to the embodiment includes a camera unit, an analysis unit, and a guidance unit. The camera unit acquires video data with a camera mounted on a cane. The analysis unit analyzes the video data acquired by the camera unit. The guidance unit provides appropriate guidance to the user based on the data analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can assist elderly people and people with disabilities in walking safely. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The walking stick system according to an embodiment of the present invention is a system that enables elderly and disabled people to walk safely by incorporating a camera into the walking stick and analyzing the video data with a generating AI. The walking stick system uses a camera mounted on the walking stick to acquire video in real time, and the generating AI analyzes this video data. The generating AI understands the user's walking speed and surrounding environment and provides appropriate guidance. For example, it can detect obstacles such as steps and cars and alert the user with voice control. It can also be applied to mountain climbing, providing appropriate route guidance that takes into account slopes and steps. This system enables elderly people to walk around safely and provides an environment where they can go out with peace of mind. For example, the walking stick system uses a camera mounted on the walking stick to acquire video in real time. For example, when a user is walking, the camera takes pictures of the surroundings. This video data is input to the generating AI. Next, the generating AI analyzes the input video data. The generating AI understands the user's walking speed and surrounding environment and detects obstacles such as steps and cars. For example, when a user is walking, the generating AI detects a step and alerts the user with voice. Furthermore, the generating AI provides appropriate guidance to the user. For example, when a user is hiking, the generating AI provides appropriate route guidance that takes into account slopes and steps. This allows the user to hike safely. This system enables elderly people to walk around safely, providing an environment where they can go out with peace of mind. For example, when a user is walking in the city, the generating AI detects obstacles such as steps and cars and warns them with voice prompts, allowing them to walk safely. It can also be applied to hiking, where the generating AI provides appropriate route guidance that takes into account slopes and steps, enabling safe hiking. In this way, the walking stick system can enable elderly people and people with disabilities to walk safely.

[0029] The walking stick system according to this embodiment comprises a camera unit, an analysis unit, and a guidance unit. The camera unit acquires video data using a camera mounted on the walking stick. The camera unit can, for example, acquire video of the user's surroundings in real time using a camera attached to the walking stick. The camera unit can also, for example, acquire a wide-angle lens to acquire a wide-area video. The camera unit can also, for example, use an infrared camera to acquire video even at night. The analysis unit analyzes the video data acquired by the camera unit using a generation AI. The analysis unit can, for example, use a generation AI to understand the user's walking speed and surrounding conditions. The analysis unit can also, for example, use a generation AI to detect obstacles such as steps and cars. The analysis unit can also, for example, use a generation AI to analyze the user's walking pattern. The guidance unit provides appropriate guidance to the user based on the data analyzed by the analysis unit. The guidance unit can, for example, use a generation AI to detect obstacles such as steps and cars and provide voice warnings. The guidance unit can also, for example, use a generation AI to provide appropriate route guidance that takes into account slopes and steps when climbing mountains. Furthermore, the guidance unit can, for example, use a generation AI to guide the user so that they can walk safely. As a result, the cane system according to this embodiment can enable elderly people and people with disabilities to walk safely.

[0030] The camera unit acquires video data using a camera mounted on the cane. For example, the camera unit can acquire real-time video of the user's surroundings using a camera attached to the cane. Specifically, the camera unit uses small cameras mounted on the tip or side of the cane, designed to complement the user's field of vision. By using a wide-angle lens, it is possible to acquire 360-degree video of the user's surroundings, allowing the user to understand their surroundings in detail. Furthermore, the camera unit can use an infrared camera to acquire video even at night or in dark places. Infrared cameras have the ability to detect objects even in darkness, ensuring safety during nighttime walks or outings. In addition, the camera unit has functions to adjust the video resolution and frame rate, providing optimal video quality according to the user's needs and environment. For example, acquiring high-resolution video allows for more accurate detection of small obstacles and steps. The camera unit is also waterproof and dustproof, allowing for stable operation even in rainy or dusty environments. This enables the camera unit to play a crucial role in supporting user safety in various environments.

[0031] The analysis unit uses generative AI to analyze video data acquired by the camera unit. For example, the analysis unit can use generative AI to understand the user's walking speed and surrounding environment. Specifically, the generative AI processes video data in real time and analyzes the user's walking speed and walking pattern. This allows it to determine how fast the user is walking and whether their walking is stable. The generative AI can also detect obstacles such as steps and cars from the video data. For example, using image recognition technology, the generative AI can identify steps in the road and obstacles on the sidewalk and alert the user. Furthermore, the generative AI can analyze the user's walking pattern and detect changes in walking stability and balance. This allows it to issue warnings in advance if the user is likely to fall or if their walking is unstable. Based on this information, the analysis unit provides appropriate instructions to the guidance unit to ensure the user's safety. The analysis unit can also utilize past data and statistical information to monitor the user's walking patterns and environmental changes over the long term. This allows the analysis unit to provide valuable information for evaluating improvements in the user's walking and the effectiveness of rehabilitation.

[0032] The guidance unit provides appropriate guidance to the user based on data analyzed by the analysis unit. For example, the guidance unit can use generative AI to detect obstacles such as steps and cars and provide voice warnings. Specifically, the guidance unit provides real-time voice guidance to the user based on information provided by the analysis unit. For example, if the user approaches a step or obstacle while walking, the guidance unit can issue a voice warning such as, "There is a step ahead. Please be careful." The guidance unit can also use generative AI to provide appropriate route guidance that takes into account slopes and steps when hiking. For example, if there is a steep slope or dangerous area while hiking, the guidance unit can provide specific instructions such as, "Please choose the route on the right." Furthermore, the guidance unit can guide the user to walk safely. For example, if the user seems likely to lose their balance while walking, the guidance unit can provide advice such as, "Please walk slowly." In addition to voice guidance, the guidance unit can also provide notifications using vibration and light. For example, in environments where voice guidance is difficult to hear, the walking stick can vibrate to alert the user. Furthermore, an LED light can be mounted on the tip of the cane to visually attract attention. This allows the guidance unit to provide users with appropriate guidance in various ways, supporting safe walking.

[0033] The camera unit can acquire video data in real time. For example, the camera unit can use a camera attached to a cane to acquire video of the user's surroundings in real time. The camera unit can also use a wide-angle lens to acquire a wide-angle view. Furthermore, the camera unit can use an infrared camera to acquire video even at night. This allows for immediate analysis and guidance by acquiring video data in real time. The specific definition and criteria of real time include, for example, the acceptable range of delay time and the frequency of data updates. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the video data acquired in real time into a generating AI, which can then analyze the video data.

[0034] The analysis unit can understand the user's walking speed and surrounding environment using a generative AI. For example, the analysis unit can analyze the user's walking speed using the generative AI. The analysis unit can also understand the surrounding environment using the generative AI. Furthermore, the analysis unit can detect obstacles such as steps and cars using the generative AI. This allows for appropriate guidance by understanding the user's walking speed and surrounding environment. The specific types and implementation methods of the generative AI include, for example, the algorithms used and the types of training data. Some or all of the above-mentioned processes in the analysis unit are performed using the generative AI. For example, the analysis unit can input the user's walking data into the generative AI, which can then analyze the walking speed and surrounding environment.

[0035] The guidance unit can detect obstacles such as steps and vehicles using generative AI and provide voice warnings. For example, the guidance unit can use generative AI to detect steps and provide voice warnings. The guidance unit can also use generative AI to detect obstacles such as vehicles and provide voice warnings. Furthermore, the guidance unit can use generative AI to detect obstacles such as pedestrians and provide voice warnings. This supports safe walking by detecting obstacles and providing voice warnings. Specific types of obstacles and detection methods include, for example, steps, vehicles, and pedestrians. Some or all of the above processing in the guidance unit is performed using generative AI. For example, the guidance unit can input video data into the generative AI, which can then detect obstacles and provide voice warnings.

[0036] The guidance unit can use a generative AI to provide appropriate route guidance that takes into account slopes and steps during mountain climbing. For example, the guidance unit can use the generative AI to provide route guidance that takes slopes into account during mountain climbing. The guidance unit can also use the generative AI to provide route guidance that takes steps into account. Furthermore, the guidance unit can use the generative AI to provide route guidance that takes into account the weather and terrain during mountain climbing. This supports safe mountain climbing by providing appropriate route guidance during mountain climbing. Specific criteria and methods for appropriate route guidance include, for example, the gradient of the slope, the height of the steps, and the shortest route. Some or all of the above processing in the guidance unit is performed using a generative AI. For example, the guidance unit can input mountain climbing route data into the generative AI, and the generative AI can provide appropriate route guidance.

[0037] The guidance unit can use a generative AI to guide the user so that they can walk safely. For example, the guidance unit can use a generative AI to guide the user so that they can walk safely. The guidance unit can also use a generative AI to guide the user so that they can avoid dangerous areas. Furthermore, the guidance unit can also use a generative AI to guide the user so that they can adjust their walking speed. In this way, by guiding the user so that they can walk safely, an environment is provided in which they can go out with peace of mind. Specific guidance methods and criteria for walking safely include, for example, methods for avoiding dangerous areas and adjustment of walking speed. Some or all of the above processing in the guidance unit is performed using a generative AI. For example, the guidance unit can input the user's walking data into the generative AI, and the generative AI can guide the user so that they can walk safely.

[0038] The camera unit can reflect the user's walking pattern in the acquired video data. For example, the camera unit can adjust the camera's frame rate according to the user's walking speed to acquire images with less blur. The camera unit can also adjust the camera's shutter speed in accordance with the user's walking rhythm to capture moving images clearly. Furthermore, the camera unit can analyze the user's walking pattern and automatically adjust the camera's focus for specific actions (e.g., climbing steps). By reflecting the user's walking pattern, more accurate data can be acquired. The specific definition and analysis method of the walking pattern includes, for example, stride length, walking speed, and walking rhythm. Some or all of the above processing in the camera unit is performed using a generative AI. For example, the camera unit can input the user's walking data into the generative AI, which can then analyze the walking pattern and reflect it in the video data.

[0039] The camera unit can synchronize ambient audio information with the video data it acquires. For example, the camera unit can collect ambient audio using a microphone at the same time as the camera acquires video, and synchronize the video and audio. The camera unit can also analyze ambient audio information and automatically adjust the camera's focus to specific sounds (e.g., car horns). Furthermore, the camera unit can combine video and audio data to provide more effective warnings and guidance to the user. By synchronizing video and audio information, more detailed environmental information can be obtained. Specific types of audio information and methods of acquisition include, for example, ambient sounds, conversations, and warning sounds. Some or all of the above processing in the camera unit is performed using a generative AI. For example, the camera unit can input audio data into the generative AI, which can analyze the audio information and synchronize it with the video data.

[0040] The camera unit can combine the acquired video data with the user's location information. For example, the camera unit can collect GPS data simultaneously with the video acquired by the camera and synchronize the video with the location information. For example, the camera unit can automatically adjust the camera's focus based on the user's location information to capture important landmarks. Furthermore, the camera unit can combine video data and location information to provide more effective guidance to the user. This allows for more accurate guidance by combining video data and location information. Specific methods and accuracy of location information acquisition include, for example, GPS data, beacon data, and Wi-Fi location information. Some or all of the above processing in the camera unit is performed using a generative AI. For example, the camera unit can input location information data into the generative AI, which can analyze the location information and combine it with video data.

[0041] The camera unit can combine the acquired video data with ambient temperature and humidity information. For example, the camera unit can collect ambient environmental information using temperature and humidity sensors simultaneously with the video acquired by the camera, and synchronize the video and environmental information. The camera unit can also analyze ambient temperature and humidity information and automatically adjust the camera settings for specific environmental conditions (e.g., high temperature and high humidity). Furthermore, the camera unit can combine video data and environmental information to provide more effective warnings and guidance to the user. This allows for the acquisition of more detailed environmental information by combining video data and environmental information. The specific methods and accuracy of acquiring temperature and humidity information include, for example, the type of sensor, measurement range, and accuracy. Some or all of the above processing in the camera unit is performed using a generative AI. For example, the camera unit can input temperature and humidity data into the generative AI, which can then analyze the environmental information and combine it with the video data.

[0042] The analysis unit can incorporate the user's past walking data into the data being analyzed. For example, the analysis unit can predict the current walking speed based on the user's past walking speed data and incorporate this into the analysis. The analysis unit can also analyze the user's past walking patterns and adjust the analysis algorithm for specific actions (e.g., climbing steps). Furthermore, the analysis unit can more accurately grasp the current walking situation based on the user's past walking data and incorporate this into the analysis results. This allows for more accurate analysis by incorporating past walking data. Specific types and methods of acquiring past walking data include, for example, walking history and walking pattern records. Some or all of the above-described processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input past walking data into the generation AI, which can then analyze the current walking situation.

[0043] The analysis unit can combine ambient sound information with the data being analyzed. For example, the analysis unit can analyze ambient sound information and adjust the analysis algorithm for specific sounds (e.g., car horns). The analysis unit can also combine video data and audio data to perform a detailed analysis of the environment. Furthermore, the analysis unit can analyze specific environmental conditions (e.g., noise levels) based on audio data and reflect these in the analysis results. This allows for a more detailed environmental analysis by combining audio information. Specific types and acquisition methods of audio information include, for example, ambient sounds, conversational audio, and warning sounds. Some or all of the above-described processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input audio data into the generation AI, which will analyze the audio information and reflect it in the analysis results.

[0044] The analysis unit can combine the data being analyzed with the user's location information. For example, the analysis unit can update the analysis results in real time based on the user's location information to provide optimal guidance. The analysis unit can also provide analysis results for specific landmarks based on location information. Furthermore, the analysis unit can combine location information and analysis data to provide more effective guidance to the user. This allows for more accurate guidance by combining location information. Specific methods for acquiring location information and their accuracy include, for example, GPS data, beacon data, and Wi-Fi location information. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input location information data into the generation AI, which can then analyze the location information and reflect it in the analysis results.

[0045] The analysis unit can combine ambient temperature and humidity information with the data being analyzed. For example, the analysis unit can reflect environmental information collected by temperature and humidity sensors into the analysis data. For example, the analysis unit can adjust the analysis algorithm for specific environmental conditions (e.g., high temperature and high humidity) based on ambient temperature and humidity information. Furthermore, the analysis unit can combine environmental information and analysis data to provide more effective warnings and guidance to the user. This allows for more detailed environmental analysis by combining temperature and humidity information. The specific acquisition method and accuracy of temperature and humidity information include, for example, the type of sensor, measurement range, and accuracy. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input temperature and humidity data into the generation AI, which can analyze the environmental information and reflect it in the analysis results.

[0046] The guidance unit can incorporate the user's past walking data into the guidance it provides. For example, the guidance unit can provide guidance tailored to the user's current walking speed based on the user's past walking speed data. The guidance unit can also analyze the user's past walking patterns and provide appropriate guidance for specific actions (e.g., climbing steps). Furthermore, the guidance unit can provide guidance tailored to the user's current walking situation based on the user's past walking data. By incorporating past walking data, more accurate guidance becomes possible. Specific types of past walking data and methods of acquisition include, for example, walking history and walking pattern records. Some or all of the above processing in the guidance unit is performed using a generation AI. For example, the guidance unit can input past walking data into the generation AI, which can then analyze the current walking situation and reflect it in the guidance.

[0047] The guidance unit can combine ambient audio information with the guidance it provides. For example, the guidance unit can analyze ambient audio information and provide appropriate guidance for specific sounds (e.g., car horns). The guidance unit can also combine video data and audio data to provide more effective guidance to the user. Furthermore, the guidance unit can provide appropriate guidance for specific environmental conditions (e.g., noise levels) based on audio data. By combining audio information, more detailed guidance becomes possible. Specific types of audio information and methods of acquisition include, for example, ambient sounds, conversational audio, and warning sounds. Some or all of the above processing in the guidance unit is performed using a generation AI. For example, the guidance unit can input audio data into the generation AI, which can then analyze the audio information and reflect it in the guidance.

[0048] The guidance unit can combine the guidance it provides with the user's location information. For example, the guidance unit can update the guidance in real time based on the user's location information to provide optimal guidance. For example, the guidance unit can also provide guidance to specific landmarks based on location information. Furthermore, the guidance unit can combine location information and guidance data to provide more effective guidance to the user. This allows for more accurate guidance by combining location information. Specific methods for acquiring location information and their accuracy include, for example, GPS data, beacon data, and Wi-Fi location information. Some or all of the above processing in the guidance unit is performed using a generation AI. For example, the guidance unit can input location information data into the generation AI, which can then analyze the location information and reflect it in the guidance.

[0049] The guidance unit can combine ambient temperature and humidity information with the guidance it provides. For example, the guidance unit can reflect environmental information collected by temperature and humidity sensors into the guidance data. For example, the guidance unit can provide appropriate guidance for specific environmental conditions (e.g., high temperature and high humidity) based on ambient temperature and humidity information. Furthermore, the guidance unit can combine environmental information and guidance data to provide more effective warnings and guidance to the user. This allows for more detailed guidance by combining temperature and humidity information. The specific acquisition method and accuracy of temperature and humidity information include, for example, the type of sensor, measurement range, and accuracy. Some or all of the above processing in the guidance unit is performed using a generation AI. For example, the guidance unit can input temperature and humidity data into the generation AI, which can then analyze the environmental information and reflect it in the guidance.

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

[0051] The cane system can also be equipped with a vibration feedback unit. The vibration feedback unit generates vibrations in the cane in response to obstacles or steps detected by the analysis unit. For example, if the user approaches a step, the vibration feedback unit can generate a small vibration to alert them. Furthermore, if a moving obstacle such as a car is approaching, the vibration feedback unit can generate a strong vibration to warn the user. In addition, the vibration feedback unit can periodically generate light vibrations to provide a sense of security when the user is walking on a safe route. This allows the user to receive safety information not only through sight and hearing, but also through touch.

[0052] The cane system can also be equipped with a temperature control unit. This unit regulates the temperature of the cane's grip, maintaining comfort for the user's hand. For example, in cold environments, the unit warms the grip, protecting the user's hands from the cold. Conversely, in hot environments, the unit cools the grip, preventing sweating. Furthermore, the unit can automatically adjust its temperature according to the user's body temperature and the ambient temperature. This ensures the user can always use the cane comfortably.

[0053] The walking stick system can also be equipped with a location tracking unit. This unit uses GPS or beacons to track the user's location in real time. For example, if the user gets lost, the unit can notify family members or caregivers of their current location. The unit can also issue a warning if the user crosses a designated safety zone. Furthermore, the unit can record the user's movement history for later review. This ensures the user's safety and allows family members and caregivers to monitor them with peace of mind.

[0054] The cane system can also be equipped with a health monitoring unit. This unit measures the user's health data in real time, including heart rate, blood pressure, and body temperature. For example, if the user's heart rate becomes abnormally high, the health monitoring unit can issue a warning and encourage rest. Similarly, if blood pressure fluctuates rapidly, the unit can send a notification to a medical institution. Furthermore, the health monitoring unit can record the user's health data, which can be used for regular health checks. This allows for constant monitoring of the user's health status and appropriate action to be taken.

[0055] The walking stick system can also be equipped with a learning function. This learning function learns the user's walking patterns and activity history, optimizing the system's operation. For example, it can learn frequently used routes and provide guidance tailored to those routes. It can also learn the user's walking speed and rhythm, providing feedback at appropriate times. Furthermore, the learning function can learn the user's preferences and habits, providing individually customized support. This allows for more user-friendly and effective support.

[0056] The cane system can also be equipped with an emergency call unit. The emergency call unit sends an emergency notification to pre-registered contacts when the user presses a button in an emergency. For example, if the user falls, the emergency call unit can notify family members or caregivers of their current location and situation. Furthermore, if the user feels unwell, the emergency call unit can contact a medical institution. The emergency call unit can also be equipped with a function to automatically send an emergency notification if the user loses consciousness. This allows users to go out with peace of mind and enables a quick response in emergencies.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The camera unit acquires video data using a camera mounted on the cane. The camera unit can, for example, acquire video of the user's surroundings in real time using a camera attached to the cane. The camera unit can also acquire wide-angle video using a wide-angle lens, and can use an infrared camera to acquire video even at night. Step 2: The analysis unit uses a generation AI to analyze the video data acquired by the camera unit. The analysis unit can understand the user's walking speed and surrounding environment, and detect obstacles such as steps and cars. The analysis unit can also analyze the user's walking pattern. Step 3: The guidance unit provides appropriate guidance to the user based on the data analyzed by the analysis unit. The guidance unit can use generation AI to detect obstacles such as steps and cars and provide voice warnings. It can also provide appropriate route guidance that takes into account slopes and steps when hiking, guiding the user to walk safely.

[0059] (Example of form 2) The walking stick system according to an embodiment of the present invention is a system that enables elderly and disabled people to walk safely by incorporating a camera into the walking stick and analyzing the video data with a generating AI. The walking stick system uses a camera mounted on the walking stick to acquire video in real time, and the generating AI analyzes this video data. The generating AI understands the user's walking speed and surrounding environment and provides appropriate guidance. For example, it can detect obstacles such as steps and cars and alert the user with voice control. It can also be applied to mountain climbing, providing appropriate route guidance that takes into account slopes and steps. This system enables elderly people to walk around safely and provides an environment where they can go out with peace of mind. For example, the walking stick system uses a camera mounted on the walking stick to acquire video in real time. For example, when a user is walking, the camera takes pictures of the surroundings. This video data is input to the generating AI. Next, the generating AI analyzes the input video data. The generating AI understands the user's walking speed and surrounding environment and detects obstacles such as steps and cars. For example, when a user is walking, the generating AI detects a step and alerts the user with voice. Furthermore, the generating AI provides appropriate guidance to the user. For example, when a user is hiking, the generating AI provides appropriate route guidance that takes into account slopes and steps. This allows the user to hike safely. This system enables elderly people to walk around safely, providing an environment where they can go out with peace of mind. For example, when a user is walking in the city, the generating AI detects obstacles such as steps and cars and warns them with voice prompts, allowing them to walk safely. It can also be applied to hiking, where the generating AI provides appropriate route guidance that takes into account slopes and steps, enabling safe hiking. In this way, the walking stick system can enable elderly people and people with disabilities to walk safely.

[0060] The walking stick system according to this embodiment comprises a camera unit, an analysis unit, and a guidance unit. The camera unit acquires video data using a camera mounted on the walking stick. The camera unit can, for example, acquire video of the user's surroundings in real time using a camera attached to the walking stick. The camera unit can also, for example, acquire a wide-angle lens to acquire a wide-area video. The camera unit can also, for example, use an infrared camera to acquire video even at night. The analysis unit analyzes the video data acquired by the camera unit using a generation AI. The analysis unit can, for example, use a generation AI to understand the user's walking speed and surrounding conditions. The analysis unit can also, for example, use a generation AI to detect obstacles such as steps and cars. The analysis unit can also, for example, use a generation AI to analyze the user's walking pattern. The guidance unit provides appropriate guidance to the user based on the data analyzed by the analysis unit. The guidance unit can, for example, use a generation AI to detect obstacles such as steps and cars and provide voice warnings. The guidance unit can also, for example, use a generation AI to provide appropriate route guidance that takes into account slopes and steps when climbing mountains. Furthermore, the guidance unit can, for example, use a generation AI to guide the user so that they can walk safely. As a result, the cane system according to this embodiment can enable elderly people and people with disabilities to walk safely.

[0061] The camera unit acquires video data using a camera mounted on the cane. For example, the camera unit can acquire real-time video of the user's surroundings using a camera attached to the cane. Specifically, the camera unit uses small cameras mounted on the tip or side of the cane, designed to complement the user's field of vision. By using a wide-angle lens, it is possible to acquire 360-degree video of the user's surroundings, allowing the user to understand their surroundings in detail. Furthermore, the camera unit can use an infrared camera to acquire video even at night or in dark places. Infrared cameras have the ability to detect objects even in darkness, ensuring safety during nighttime walks or outings. In addition, the camera unit has functions to adjust the video resolution and frame rate, providing optimal video quality according to the user's needs and environment. For example, acquiring high-resolution video allows for more accurate detection of small obstacles and steps. The camera unit is also waterproof and dustproof, allowing for stable operation even in rainy or dusty environments. This enables the camera unit to play a crucial role in supporting user safety in various environments.

[0062] The analysis unit uses generative AI to analyze video data acquired by the camera unit. For example, the analysis unit can use generative AI to understand the user's walking speed and surrounding environment. Specifically, the generative AI processes video data in real time and analyzes the user's walking speed and walking pattern. This allows it to determine how fast the user is walking and whether their walking is stable. The generative AI can also detect obstacles such as steps and cars from the video data. For example, using image recognition technology, the generative AI can identify steps in the road and obstacles on the sidewalk and alert the user. Furthermore, the generative AI can analyze the user's walking pattern and detect changes in walking stability and balance. This allows it to issue warnings in advance if the user is likely to fall or if their walking is unstable. Based on this information, the analysis unit provides appropriate instructions to the guidance unit to ensure the user's safety. The analysis unit can also utilize past data and statistical information to monitor the user's walking patterns and environmental changes over the long term. This allows the analysis unit to provide valuable information for evaluating improvements in the user's walking and the effectiveness of rehabilitation.

[0063] The guidance unit provides appropriate guidance to the user based on data analyzed by the analysis unit. For example, the guidance unit can use generative AI to detect obstacles such as steps and cars and provide voice warnings. Specifically, the guidance unit provides real-time voice guidance to the user based on information provided by the analysis unit. For example, if the user approaches a step or obstacle while walking, the guidance unit can issue a voice warning such as, "There is a step ahead. Please be careful." The guidance unit can also use generative AI to provide appropriate route guidance that takes into account slopes and steps when hiking. For example, if there is a steep slope or dangerous area while hiking, the guidance unit can provide specific instructions such as, "Please choose the route on the right." Furthermore, the guidance unit can guide the user to walk safely. For example, if the user seems likely to lose their balance while walking, the guidance unit can provide advice such as, "Please walk slowly." In addition to voice guidance, the guidance unit can also provide notifications using vibration and light. For example, in environments where voice guidance is difficult to hear, the walking stick can vibrate to alert the user. Furthermore, an LED light can be mounted on the tip of the cane to visually attract attention. This allows the guidance unit to provide users with appropriate guidance in various ways, supporting safe walking.

[0064] The camera unit can acquire video data in real time. For example, the camera unit can use a camera attached to a cane to acquire video of the user's surroundings in real time. The camera unit can also use a wide-angle lens to acquire a wide-angle view. Furthermore, the camera unit can use an infrared camera to acquire video even at night. This allows for immediate analysis and guidance by acquiring video data in real time. The specific definition and criteria of real time include, for example, the acceptable range of delay time and the frequency of data updates. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the video data acquired in real time into a generating AI, which can then analyze the video data.

[0065] The analysis unit can understand the user's walking speed and surrounding environment using a generative AI. For example, the analysis unit can analyze the user's walking speed using the generative AI. The analysis unit can also understand the surrounding environment using the generative AI. Furthermore, the analysis unit can detect obstacles such as steps and cars using the generative AI. This allows for appropriate guidance by understanding the user's walking speed and surrounding environment. The specific types and implementation methods of the generative AI include, for example, the algorithms used and the types of training data. Some or all of the above-mentioned processes in the analysis unit are performed using the generative AI. For example, the analysis unit can input the user's walking data into the generative AI, which can then analyze the walking speed and surrounding environment.

[0066] The guidance unit can detect obstacles such as steps and vehicles using generative AI and provide voice warnings. For example, the guidance unit can use generative AI to detect steps and provide voice warnings. The guidance unit can also use generative AI to detect obstacles such as vehicles and provide voice warnings. Furthermore, the guidance unit can use generative AI to detect obstacles such as pedestrians and provide voice warnings. This supports safe walking by detecting obstacles and providing voice warnings. Specific types of obstacles and detection methods include, for example, steps, vehicles, and pedestrians. Some or all of the above processing in the guidance unit is performed using generative AI. For example, the guidance unit can input video data into the generative AI, which can then detect obstacles and provide voice warnings.

[0067] The guidance unit can use a generative AI to provide appropriate route guidance that takes into account slopes and steps during mountain climbing. For example, the guidance unit can use the generative AI to provide route guidance that takes slopes into account during mountain climbing. The guidance unit can also use the generative AI to provide route guidance that takes steps into account. Furthermore, the guidance unit can use the generative AI to provide route guidance that takes into account the weather and terrain during mountain climbing. This supports safe mountain climbing by providing appropriate route guidance during mountain climbing. Specific criteria and methods for appropriate route guidance include, for example, the gradient of the slope, the height of the steps, and the shortest route. Some or all of the above processing in the guidance unit is performed using a generative AI. For example, the guidance unit can input mountain climbing route data into the generative AI, and the generative AI can provide appropriate route guidance.

[0068] The guidance unit can use a generative AI to guide the user so that they can walk safely. For example, the guidance unit can use a generative AI to guide the user so that they can walk safely. The guidance unit can also use a generative AI to guide the user so that they can avoid dangerous areas. Furthermore, the guidance unit can also use a generative AI to guide the user so that they can adjust their walking speed. In this way, by guiding the user so that they can walk safely, an environment is provided in which they can go out with peace of mind. Specific guidance methods and criteria for walking safely include, for example, methods for avoiding dangerous areas and adjustment of walking speed. Some or all of the above processing in the guidance unit is performed using a generative AI. For example, the guidance unit can input the user's walking data into the generative AI, and the generative AI can guide the user so that they can walk safely.

[0069] The camera unit can estimate the user's emotions and adjust the camera's shooting angle based on the estimated emotions. For example, if the user is tense, the camera unit can widen the shooting angle to capture more of the surrounding environment. If the user is relaxed, the camera unit can narrow the shooting angle to focus on a specific object. Furthermore, if the user is in a hurry, the camera unit can dynamically adjust the shooting angle to prioritize capturing important information. By adjusting the camera's shooting angle according to the user's emotions, more appropriate video data can be obtained. User emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit is performed using generative AI. For example, the camera unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the camera's shooting angle.

[0070] The camera unit can reflect the user's walking pattern in the acquired video data. For example, the camera unit can adjust the camera's frame rate according to the user's walking speed to acquire images with less blur. The camera unit can also adjust the camera's shutter speed in accordance with the user's walking rhythm to capture moving images clearly. Furthermore, the camera unit can analyze the user's walking pattern and automatically adjust the camera's focus for specific actions (e.g., climbing steps). By reflecting the user's walking pattern, more accurate data can be acquired. The specific definition and analysis method of the walking pattern includes, for example, stride length, walking speed, and walking rhythm. Some or all of the above processing in the camera unit is performed using a generative AI. For example, the camera unit can input the user's walking data into the generative AI, which can then analyze the walking pattern and reflect it in the video data.

[0071] The camera unit can synchronize ambient audio information with the video data it acquires. For example, the camera unit can collect ambient audio using a microphone at the same time as the camera acquires video, and synchronize the video and audio. The camera unit can also analyze ambient audio information and automatically adjust the camera's focus to specific sounds (e.g., car horns). Furthermore, the camera unit can combine video and audio data to provide more effective warnings and guidance to the user. By synchronizing video and audio information, more detailed environmental information can be obtained. Specific types of audio information and methods of acquisition include, for example, ambient sounds, conversations, and warning sounds. Some or all of the above processing in the camera unit is performed using a generative AI. For example, the camera unit can input audio data into the generative AI, which can analyze the audio information and synchronize it with the video data.

[0072] The camera unit can estimate the user's emotions and adjust the camera's shooting frequency based on the estimated emotions. For example, if the user is nervous, the camera unit can increase the shooting frequency to collect more information. If the user is relaxed, for example, the camera unit can decrease the shooting frequency to collect only the necessary information. Furthermore, if the user is in a hurry, for example, the camera unit can dynamically adjust the shooting frequency to prioritize the collection of important information. This allows for the collection of more appropriate information by adjusting the camera's shooting frequency according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit is performed using generative AI. For example, the camera unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the camera's shooting frequency.

[0073] The camera unit can combine the acquired video data with the user's location information. For example, the camera unit can collect GPS data simultaneously with the video acquired by the camera and synchronize the video with the location information. For example, the camera unit can automatically adjust the camera's focus based on the user's location information to capture important landmarks. Furthermore, the camera unit can combine video data and location information to provide more effective guidance to the user. This allows for more accurate guidance by combining video data and location information. Specific methods and accuracy of location information acquisition include, for example, GPS data, beacon data, and Wi-Fi location information. Some or all of the above processing in the camera unit is performed using a generative AI. For example, the camera unit can input location information data into the generative AI, which can analyze the location information and combine it with video data.

[0074] The camera unit can combine the acquired video data with ambient temperature and humidity information. For example, the camera unit can collect ambient environmental information using temperature and humidity sensors simultaneously with the video acquired by the camera, and synchronize the video and environmental information. The camera unit can also analyze ambient temperature and humidity information and automatically adjust the camera settings for specific environmental conditions (e.g., high temperature and high humidity). Furthermore, the camera unit can combine video data and environmental information to provide more effective warnings and guidance to the user. This allows for the acquisition of more detailed environmental information by combining video data and environmental information. The specific methods and accuracy of acquiring temperature and humidity information include, for example, the type of sensor, measurement range, and accuracy. Some or all of the above processing in the camera unit is performed using a generative AI. For example, the camera unit can input temperature and humidity data into the generative AI, which can then analyze the environmental information and combine it with the video data.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is tense, the analysis unit can quickly operate the analysis algorithm to provide immediate feedback. For example, if the user is relaxed, the analysis unit can operate the analysis algorithm in detail to analyze more information. Furthermore, if the user is in a hurry, the analysis unit can dynamically adjust the analysis algorithm to prioritize the analysis of important information. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the analysis algorithm.

[0076] The analysis unit can incorporate the user's past walking data into the data being analyzed. For example, the analysis unit can predict the current walking speed based on the user's past walking speed data and incorporate this into the analysis. The analysis unit can also analyze the user's past walking patterns and adjust the analysis algorithm for specific actions (e.g., climbing steps). Furthermore, the analysis unit can more accurately grasp the current walking situation based on the user's past walking data and incorporate this into the analysis results. This allows for more accurate analysis by incorporating past walking data. Specific types and methods of acquiring past walking data include, for example, walking history and walking pattern records. Some or all of the above-described processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input past walking data into the generation AI, which can then analyze the current walking situation.

[0077] The analysis unit can combine ambient sound information with the data being analyzed. For example, the analysis unit can analyze ambient sound information and adjust the analysis algorithm for specific sounds (e.g., car horns). The analysis unit can also combine video data and audio data to perform a detailed analysis of the environment. Furthermore, the analysis unit can analyze specific environmental conditions (e.g., noise levels) based on audio data and reflect these in the analysis results. This allows for a more detailed environmental analysis by combining audio information. Specific types and acquisition methods of audio information include, for example, ambient sounds, conversational audio, and warning sounds. Some or all of the above-described processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input audio data into the generation AI, which will analyze the audio information and reflect it in the analysis results.

[0078] 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 tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also 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 gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate information. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit can input user facial expression data into the generative AI, the generative AI can estimate the user's emotions, and the display method of the analysis results can be adjusted.

[0079] The analysis unit can combine the data being analyzed with the user's location information. For example, the analysis unit can update the analysis results in real time based on the user's location information to provide optimal guidance. The analysis unit can also provide analysis results for specific landmarks based on location information. Furthermore, the analysis unit can combine location information and analysis data to provide more effective guidance to the user. This allows for more accurate guidance by combining location information. Specific methods for acquiring location information and their accuracy include, for example, GPS data, beacon data, and Wi-Fi location information. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input location information data into the generation AI, which can then analyze the location information and reflect it in the analysis results.

[0080] The analysis unit can combine ambient temperature and humidity information with the data being analyzed. For example, the analysis unit can reflect environmental information collected by temperature and humidity sensors into the analysis data. For example, the analysis unit can adjust the analysis algorithm for specific environmental conditions (e.g., high temperature and high humidity) based on ambient temperature and humidity information. Furthermore, the analysis unit can combine environmental information and analysis data to provide more effective warnings and guidance to the user. This allows for more detailed environmental analysis by combining temperature and humidity information. The specific acquisition method and accuracy of temperature and humidity information include, for example, the type of sensor, measurement range, and accuracy. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input temperature and humidity data into the generation AI, which can analyze the environmental information and reflect it in the analysis results.

[0081] The guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated emotions. For example, if the user is nervous, the guidance unit can provide simple and highly visible guidance. If the user is relaxed, the guidance unit can also provide guidance that includes detailed information. Furthermore, if the user is in a hurry, the guidance unit can provide guidance that gets straight to the point. By adjusting the way the guidance is presented according to the user's emotions, more appropriate guidance becomes possible. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the guidance unit is performed using generative AI. For example, the guidance unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the way the guidance is presented.

[0082] The guidance unit can incorporate the user's past walking data into the guidance it provides. For example, the guidance unit can provide guidance tailored to the user's current walking speed based on the user's past walking speed data. The guidance unit can also analyze the user's past walking patterns and provide appropriate guidance for specific actions (e.g., climbing steps). Furthermore, the guidance unit can provide guidance tailored to the user's current walking situation based on the user's past walking data. By incorporating past walking data, more accurate guidance becomes possible. Specific types of past walking data and methods of acquisition include, for example, walking history and walking pattern records. Some or all of the above processing in the guidance unit is performed using a generation AI. For example, the guidance unit can input past walking data into the generation AI, which can then analyze the current walking situation and reflect it in the guidance.

[0083] The guidance unit can combine ambient audio information with the guidance it provides. For example, the guidance unit can analyze ambient audio information and provide appropriate guidance for specific sounds (e.g., car horns). The guidance unit can also combine video data and audio data to provide more effective guidance to the user. Furthermore, the guidance unit can provide appropriate guidance for specific environmental conditions (e.g., noise levels) based on audio data. By combining audio information, more detailed guidance becomes possible. Specific types of audio information and methods of acquisition include, for example, ambient sounds, conversational audio, and warning sounds. Some or all of the above processing in the guidance unit is performed using a generation AI. For example, the guidance unit can input audio data into the generation AI, which can then analyze the audio information and reflect it in the guidance.

[0084] The guidance unit can estimate the user's emotions and determine the priority of guidance based on the estimated emotions. For example, if the user is nervous, the guidance unit will prioritize providing important information. If the user is relaxed, the guidance unit may also provide guidance that includes detailed information. Furthermore, if the user is in a hurry, the guidance unit may prioritize providing concise guidance. In this way, by determining the priority of guidance according to the user's emotions, more important information can be provided preferentially. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the guidance unit is performed using generative AI. For example, the guidance unit can input user facial expression data into the generative AI, which can estimate the user's emotions and determine the priority of guidance.

[0085] The guidance unit can combine the guidance it provides with the user's location information. For example, the guidance unit can update the guidance in real time based on the user's location information to provide optimal guidance. For example, the guidance unit can also provide guidance to specific landmarks based on location information. Furthermore, the guidance unit can combine location information and guidance data to provide more effective guidance to the user. This allows for more accurate guidance by combining location information. Specific methods for acquiring location information and their accuracy include, for example, GPS data, beacon data, and Wi-Fi location information. Some or all of the above processing in the guidance unit is performed using a generation AI. For example, the guidance unit can input location information data into the generation AI, which can then analyze the location information and reflect it in the guidance.

[0086] The guidance unit can combine ambient temperature and humidity information with the guidance it provides. For example, the guidance unit can reflect environmental information collected by temperature and humidity sensors into the guidance data. For example, the guidance unit can provide appropriate guidance for specific environmental conditions (e.g., high temperature and high humidity) based on ambient temperature and humidity information. Furthermore, the guidance unit can combine environmental information and guidance data to provide more effective warnings and guidance to the user. This allows for more detailed guidance by combining temperature and humidity information. The specific acquisition method and accuracy of temperature and humidity information include, for example, the type of sensor, measurement range, and accuracy. Some or all of the above processing in the guidance unit is performed using a generation AI. For example, the guidance unit can input temperature and humidity data into the generation AI, which can then analyze the environmental information and reflect it in the guidance.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The cane system can also be equipped with a vibration feedback unit. The vibration feedback unit generates vibrations in the cane in response to obstacles or steps detected by the analysis unit. For example, if the user approaches a step, the vibration feedback unit can generate a small vibration to alert them. Furthermore, if a moving obstacle such as a car is approaching, the vibration feedback unit can generate a strong vibration to warn the user. In addition, the vibration feedback unit can periodically generate light vibrations to provide a sense of security when the user is walking on a safe route. This allows the user to receive safety information not only through sight and hearing, but also through touch.

[0089] The cane system can also be equipped with a temperature control unit. This unit regulates the temperature of the cane's grip, maintaining comfort for the user's hand. For example, in cold environments, the unit warms the grip, protecting the user's hands from the cold. Conversely, in hot environments, the unit cools the grip, preventing sweating. Furthermore, the unit can automatically adjust its temperature according to the user's body temperature and the ambient temperature. This ensures the user can always use the cane comfortably.

[0090] The walking stick system can also be equipped with a location tracking unit. This unit uses GPS or beacons to track the user's location in real time. For example, if the user gets lost, the unit can notify family members or caregivers of their current location. The unit can also issue a warning if the user crosses a designated safety zone. Furthermore, the unit can record the user's movement history for later review. This ensures the user's safety and allows family members and caregivers to monitor them with peace of mind.

[0091] The cane system can also be equipped with a voice recognition unit. The voice recognition unit recognizes the user's voice commands and operates the system. For example, if the user says, "Tell me about steps," the voice recognition unit can send instructions to the analysis unit to provide information about steps. Also, if the user says, "Start route guidance," the voice recognition unit can send instructions to the guidance unit to start appropriate route guidance. Furthermore, the voice recognition unit can estimate the user's emotions from the tone and speed of their voice and provide appropriate feedback. This allows the user to operate the system without using their hands, making it more convenient to use.

[0092] The cane system can also be equipped with a health monitoring unit. This unit measures the user's health data in real time, including heart rate, blood pressure, and body temperature. For example, if the user's heart rate becomes abnormally high, the health monitoring unit can issue a warning and encourage rest. Similarly, if blood pressure fluctuates rapidly, the unit can send a notification to a medical institution. Furthermore, the health monitoring unit can record the user's health data, which can be used for regular health checks. This allows for constant monitoring of the user's health status and appropriate action to be taken.

[0093] The cane system can also be equipped with an emotion estimation unit. This unit estimates the user's emotions from their facial expressions and tone of voice, and adjusts the system's operation accordingly. For example, if the user is feeling anxious, the emotion estimation unit can send instructions to the guidance unit to provide more detailed guidance. Conversely, if the user is relaxed, the emotion estimation unit can send instructions to the guidance unit to provide concise guidance. Furthermore, the emotion estimation unit can adjust the tone and content of the voice feedback according to the user's emotions. This allows for the provision of appropriate support tailored to the user's feelings.

[0094] The walking stick system can also be equipped with a learning function. This learning function learns the user's walking patterns and activity history, optimizing the system's operation. For example, it can learn frequently used routes and provide guidance tailored to those routes. It can also learn the user's walking speed and rhythm, providing feedback at appropriate times. Furthermore, the learning function can learn the user's preferences and habits, providing individually customized support. This allows for more user-friendly and effective support.

[0095] The cane system can also be equipped with an emergency call unit. The emergency call unit sends an emergency notification to pre-registered contacts when the user presses a button in an emergency. For example, if the user falls, the emergency call unit can notify family members or caregivers of their current location and situation. Furthermore, if the user feels unwell, the emergency call unit can contact a medical institution. The emergency call unit can also be equipped with a function to automatically send an emergency notification if the user loses consciousness. This allows users to go out with peace of mind and enables a quick response in emergencies.

[0096] The walking stick system can also be equipped with an entertainment section. This section provides features that allow users to enjoy music or audiobooks while walking. For example, users can play their favorite music to make walking more enjoyable. The entertainment section can also adjust the music tempo to match the user's walking rhythm. Furthermore, the entertainment section can recommend appropriate content based on the user's mood. This allows users to enjoy themselves while walking safely.

[0097] The cane system can also be equipped with a communication unit. This unit provides features that allow users to easily contact family and friends. For example, by pressing a button, the user can initiate a voice call to pre-registered contacts. The communication unit can also convert the user's voice into text and send messages. Furthermore, the communication unit can estimate the user's emotions and prompt them to contact others at the appropriate time. This allows users to stay connected with family and friends at all times without feeling isolated.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The camera unit acquires video data using a camera mounted on the cane. The camera unit can, for example, acquire video of the user's surroundings in real time using a camera attached to the cane. The camera unit can also acquire wide-angle video using a wide-angle lens, and can use an infrared camera to acquire video even at night. Step 2: The analysis unit uses a generation AI to analyze the video data acquired by the camera unit. The analysis unit can understand the user's walking speed and surrounding environment, and detect obstacles such as steps and cars. The analysis unit can also analyze the user's walking pattern. Step 3: The guidance unit provides appropriate guidance to the user based on the data analyzed by the analysis unit. The guidance unit can use generation AI to detect obstacles such as steps and cars and provide voice warnings. It can also provide appropriate route guidance that takes into account slopes and steps when hiking, guiding the user to walk safely.

[0100] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0103] Each of the multiple elements described above, including the camera unit, analysis unit, and guidance unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the smart device 14 and acquires video of the user's surroundings in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the video data using generated AI to understand the user's walking speed and surrounding conditions. The guidance unit is implemented by the control unit 46A of the smart device 14 and provides voice warnings based on the analyzed data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 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.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the camera unit, analysis unit, and guidance unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the smart glasses 214, which acquires video of the user's surroundings in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the video data using generated AI to understand the user's walking speed and surrounding conditions. The guidance unit is implemented by the control unit 46A of the smart glasses 214, which provides voice warnings based on the analyzed data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the camera unit, analysis unit, and guidance unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the headset terminal 314, which acquires video of the user's surroundings in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the video data using generated AI to understand the user's walking speed and surrounding conditions. The guidance unit is implemented by the control unit 46A of the headset terminal 314, which provides voice warnings based on the analyzed data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the camera unit, analysis unit, and guidance unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the robot 414 and acquires video of the user's surroundings in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the video data using generated AI to understand the user's walking speed and surrounding conditions. The guidance unit is implemented by the control unit 46A of the robot 414 and provides voice warnings based on the analyzed data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) The camera unit acquires video data using a camera mounted on the cane, An analysis unit analyzes the video data acquired by the camera unit, The system includes a guidance unit that provides appropriate guidance to the user based on the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned camera unit is Acquire video data in real time The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The generated AI understands the user's walking speed and surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned guide section is The AI ​​generates signals to detect obstacles such as steps and cars, and provides voice warnings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide section is The AI ​​generates routes that take into account slopes and uneven terrain, providing appropriate route guidance during mountain climbing. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned guide section is The generated AI guides users to ensure they can walk safely. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting angle based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned camera unit is The acquired video data will reflect the user's walking pattern. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned camera unit is Synchronize the acquired video data with ambient audio information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned camera unit is The acquired video data is combined with the user's location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned camera unit is The acquired video data is combined with ambient temperature and humidity information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The data to be analyzed will include the user's past walking data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Combine surrounding audio information with the data to be analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Combine the user's location information with the data to be analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Combine ambient temperature and humidity information with the data to be analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide section is The system estimates the user's emotions and adjusts the way guidance is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is The guidance provided will incorporate the user's past walking data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is The guidance provided will be combined with ambient audio information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide section is The system estimates the user's emotions and determines the priority of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide section is The guidance provided will be combined with the user's location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is The guidance provided will be combined with ambient temperature and humidity information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The camera unit acquires video data using a camera mounted on the cane, An analysis unit analyzes the video data acquired by the camera unit, The system includes a guidance unit that provides appropriate guidance to the user based on the data analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned camera unit is Acquire video data in real time The system according to feature 1.

3. The aforementioned analysis unit, The AI ​​generates data to understand the user's walking speed and surrounding environment. The system according to feature 1.

4. The aforementioned guide section is The AI ​​generates signals to detect obstacles such as steps and cars, and provides voice warnings. The system according to feature 1.

5. The aforementioned guide section is The AI ​​generates routes that take into account slopes and uneven terrain during mountain climbing, providing appropriate route guidance. The system according to feature 1.

6. The aforementioned guide section is The AI ​​generates directions to guide users so they can walk safely. The system according to feature 1.

7. The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting angle based on those emotions. The system according to feature 1.

8. The aforementioned camera unit is The acquired video data will reflect the user's walking pattern. The system according to feature 1.

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