system

Abstract

We provide the system. [Solution] A terminal means equipped with a sensor for acquiring environmental information, A server means for receiving and analyzing environmental information transmitted from the terminal means, A means for providing information to the user through voice and vibration based on the analysis results, A means of receiving user feedback and updating accumulated data, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to their characteristics, visually impaired people and other disabled people are often restricted in their independent living and social participation. Therefore, they experience many difficulties in daily life movement, item management, and communication with others. With only conventional voice guidance and physical assistance, it is difficult to fully meet the needs of each individual, and there is a problem that the opportunities for disabled people to act autonomously like healthy people are limited. The purpose of this invention is to effectively solve the life problems faced by such disabled people and promote social participation.

Means for Solving the Problems

[0005] This invention provides a terminal means equipped with a sensor that collects environmental information, and performs analysis of the environmental data by transmitting the data acquired by the terminal means to a server means. The analyzed results are provided to the user in real time through voice and vibration. Furthermore, by continuously collecting user feedback and updating the accumulated data, the accuracy of the entire system and the user experience are continuously improved. In addition, by including functions that suggest the optimal placement of items and functions that provide predictive information based on the user's behavior history, the system is configured to further support the independent living of people with disabilities.

[0006] "Environmental information" refers to data about the physical space in which the user is located, including information such as the room layout, the arrangement of objects, and acoustic patterns.

[0007] A "sensor" is a device that detects physical environmental information and converts it into digital data; examples include cameras and acoustic sensors.

[0008] A "terminal device" is an electronic device that incorporates sensors and has the function of collecting environmental information and transmitting it as data to a server.

[0009] A "server device" is a computer device that receives data transmitted from a terminal device, performs analysis, and generates results.

[0010] "Information via voice and vibration" refers to a method of providing information using voice messages and vibration patterns to convey the analyzed results to the user.

[0011] "Feedback" refers to user-provided information such as operation history and evaluations, which is important data used for improving and adjusting the system.

[0012] "Optimal placement suggestions" is a function that provides guidelines for efficiently arranging items based on the user's past behavior and usage trends.

[0013] "Behavioral history" refers to records of actions and movements a user has taken in the past, and is data used to predict future behavior. [Brief explanation of the drawing]

[0014] [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 the data processing device and 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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. 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).

[0021] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] The 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.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides a system that enables people with disabilities, particularly those with visual impairments, to live more independent lives. The system's main components are a terminal equipped with sensors and a server that performs data analysis.

[0036] Terminal part

[0037] The device is equipped with multiple sensors to acquire information about the surrounding environment in real time. This includes various environmental sensors such as ultrasound and LiDAR to acquire information about the room layout and the placement of objects. The collected data is initially processed, converted into a specific packet format, and sent to the server.

[0038] Server portion

[0039] The server maintains a database of environmental information in the cloud and performs data analysis based on this database. By analyzing the received environmental data using AI algorithms, it generates the information the user needs and constructs customized feedback. The generated feedback is sent to the terminal as commands to control speech synthesis and vibration motors.

[0040] Providing feedback to users

[0041] The user carries a device and receives information transmitted from the server through it. For example, when the user enters a room, the device provides voice guidance about the surrounding layout. This might include messages such as, "There is a table in the center of the room. Please be careful not to step over it." Furthermore, if an object is difficult to maneuver, the device can communicate the appropriate steps to the user through vibration patterns.

[0042] In actual use cases, the system also includes a function that allows users to input voice commands using a terminal. For example, if a user issues the voice command "Tell me the location of the table," the terminal sends a request to the server, and the server responds with the table's location information via voice as a result of its analysis. The feedback obtained during this process is sent to the server and used for future analysis and suggestions.

[0043] This system enables visually impaired individuals to overcome geographical and physical barriers and live their daily lives more smoothly. Furthermore, the program can be expanded to provide similar support to people with other disabilities.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The device scans the surrounding environment and acquires sensor data. The sensors include ultrasonic sensors, LiDAR, and microphones, and this data is used to generate a 3D map of the environment.

[0047] Step 2:

[0048] The terminal converts the initial data into packet format and sends it to the server. At this time, a timestamp is added to the data so that it can be used as time-series data for later analysis.

[0049] Step 3:

[0050] The server receives environmental data and stores it in a database. Simultaneously, an AI algorithm analyzes the data and extracts the environmental information requested by the user.

[0051] Step 4:

[0052] The server generates commands to provide voice guidance or vibration feedback based on the analysis results and sends them to the terminal.

[0053] Step 5:

[0054] The terminal receives commands from the server and transmits guidance messages via speech synthesis and vibration signals to the user.

[0055] Step 6:

[0056] The system acts based on the information received by the user and inputs voice commands into the terminal as needed.

[0057] Step 7:

[0058] The terminal analyzes the user's voice commands and sends them to the server. It also receives user feedback in real time and sends it to the server.

[0059] Step 8:

[0060] The server analyzes the feedback and updates the database. This feedback is then incorporated into the AI ​​algorithm as training data, helping to improve the accuracy of future analyses.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] There is a need to provide support to users with visual or mobility limitations so that they can move safely and comfortably in their daily lives and understand their surroundings. However, conventional technologies have limitations in acquiring environmental information and providing feedback to users, and have been unable to adequately meet the individual needs of users.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes a device means equipped with multiple environmental sensors for acquiring spatial information of the surroundings; an information processing device means for receiving spatial data transmitted from the device means and analyzing it using a machine learning algorithm; a means for controlling a speech synthesis engine and a vibration motor based on the analysis results to provide information to the user; and a means for receiving commands and feedback from the user and updating a history database. This enables users with visual or movement limitations to move safely and understand their surrounding physical situation through real-time, customized feedback.

[0066] "Device means" refers to a module equipped with multiple environmental sensors for acquiring surrounding spatial information and an initial filtering function for processing the data obtained from these sensors.

[0067] "Information processing device means" refers to a system that receives spatial data transmitted from a device means and performs data analysis using machine learning algorithms.

[0068] An "environmental sensor" refers to a measuring element that uses ultrasound, LIDAR, or other technologies to acquire information about surrounding objects and space in real time.

[0069] A "machine learning algorithm" refers to a computer program that analyzes received data and performs pattern recognition or data classification.

[0070] A "speech synthesis engine" refers to software equipped with processing functions to convert text data into speech and provide it to the user as auditory information.

[0071] A "vibration motor" refers to a device that provides tactile feedback to the user by generating physical vibrations.

[0072] A "history database" refers to an information recording system that stores user feedback and command history for use in future analysis and user profile construction.

[0073] This invention is a support system designed to enable visually impaired individuals and users with limited mobility to live their daily lives more independently. The system primarily consists of a terminal equipped with multiple environmental sensors and a server for data analysis.

[0074] terminal

[0075] The device is equipped with environmental sensors such as ultrasonic sensors and LiDAR sensors. This allows the device to acquire spatial information about its surroundings in real time. The data obtained from the sensors is filtered to remove noise, converted into a specific packet format, and sent to the server.

[0076] server

[0077] The server resides in the cloud and receives spatial data transmitted from terminals. This data is analyzed using machine learning algorithms to extract information useful to the user. This analysis applies generative AI models that perform data classification and pattern recognition. Specific examples include guidance on the user's movement within a room and location information of obstacles.

[0078] Feedback system

[0079] Based on the analysis results, the server generates voice feedback using a speech synthesis engine. It can also provide tactile feedback by controlling a vibration motor. This allows users to obtain safety information about their destination without relying on their vision.

[0080] User interaction

[0081] Users can input voice commands into the terminal. For example, if they give the command "Tell me where the table is," the server will analyze it and return specific location information via voice. User feedback and usage history are constantly recorded and stored in the server's history database, which improves the system's accuracy and responsiveness.

[0082] Prompt statements as concrete examples

[0083] "Please implement an audio guidance system that tells you what furniture is in the room and how it's arranged when you enter a new room."

[0084] This system allows users with visual or mobility limitations to more easily overcome physical obstacles in their surroundings, enabling them to live a safe and independent daily life.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The device uses multiple environmental sensors to acquire spatial information about its surroundings. These sensors utilize ultrasound and LiDAR to obtain information about the position and distance of objects. This data is collected in real time and filtered to remove noise. The input is raw data from the sensors, while the output is de-noised and formatted data. In practice, the device accurately measures the positions of walls and furniture in a room within a few seconds.

[0088] Step 2:

[0089] The terminal converts filtered data into a specific packet format and sends it to the server. The input is filtered environmental data, and the output is data packets corresponding to the server. In practice, after the conversion process, the data is quickly sent to a server in the cloud via wireless communication.

[0090] Step 3:

[0091] The server receives data packets sent from the terminal and performs analysis using a generative AI model. The input is data packets from the terminal, and the output is the analysis result based on environmental information. In this analysis, machine learning algorithms are used to extract important features and patterns. Specifically, the server reconstructs the room layout from the received data and identifies important obstacle information.

[0092] Step 4:

[0093] The server generates voice feedback using a speech synthesis engine based on the analysis results and creates control signals for the vibration motor. The input is the analysis results, and the output is voice information and control commands for the vibration pattern. In specific operation, the server generates messages such as "There is a table in the center of the room. Do not approach it."

[0094] Step 5:

[0095] The terminal provides feedback information received from the server to the user. It uses a speech synthesis engine to provide voice guidance and controls a vibration motor to deliver tactile alerts. Input is commands from the server, and output is actual voice and vibration feedback to the user. For example, it might provide a voice announcement such as "The door ahead is closed" and indicate its direction with vibration.

[0096] Step 6:

[0097] Users can operate the terminal and input voice commands. New requests are sent to the server, and the quality and content of the feedback are reviewed. The input is the user's voice command, and the output is a request signal to the server. For example, if the user commands, "Tell me the information for the next room," the system will acquire and analyze further information.

[0098] Step 7:

[0099] The server stores user feedback and command history in a history database. This data is then used for future analysis, improving system responsiveness. The input is user feedback information, and the output is the updated history database. Specifically, the server analyzes the user's action history and updates the model for predicting future actions.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] It is generally difficult for visually impaired individuals to search for products and move smoothly within physical stores. Their shopping experience is often limited due to their difficulty in understanding store layouts and product placement. There is a need to address this challenge and enable visually impaired individuals to move safely and effectively within stores and find products.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes means for providing end users with voice guidance on the location of items based on the installation environment, means for providing voice instructions to assist end users in moving around within the store, and means for predicting the location of items and generating instructions using a generation algorithm. This makes it easier for visually impaired people to understand their location within a physical store and enables them to search for products safely and efficiently.

[0105] "Environmental data" refers to information collected by measurement unit means to acquire physical information about the surrounding environment.

[0106] A "measurement unit means" is a device equipped with sensors for acquiring environmental data, mainly using graphic sensors or laser rangefinders.

[0107] The "processing unit means" refers to a server or computer system that analyzes environmental data received from the measurement unit means and generates information to be provided to the user.

[0108] "End users" refer to the final users of a system, and especially individuals who require assistance, such as those with visual impairments.

[0109] "Voice guidance" refers to voice instructions generated based on analyzed information, informing end users of the location of items and the route they should take.

[0110] "Voice instructions" refer to voice guidance provided to assist end users in navigating within a store.

[0111] A "generative algorithm" is a computational method and processing step used to predict the location of an object based on environmental data and generate instructions regarding that location.

[0112] This invention provides a system for visually impaired individuals to safely navigate a physical store and effectively search for products. The system is comprised of a measurement unit, a processing unit, and means for providing information to the end user.

[0113] The measurement unit is mounted on smart glasses or a mobile device and acquires environmental data using a LiDAR sensor and a camera. This data includes information such as the location and layout of items within a store.

[0114] The server receives environmental data transmitted from the measurement unit on a cloud platform. This process utilizes cloud services such as AWS® Lambda and Google® Cloud AI, and an AI model using TENSORFLOW® analyzes the environmental data. As a result of the analysis, information necessary for the end user is generated.

[0115] Based on the analysis results, the server generates instructions to provide voice guidance to the user. These voice instructions are transmitted to the end user using speech synthesis software such as Google Text-to-Speech. The generation algorithm also predicts the user's current location and the route to the destination item, providing real-time guidance.

[0116] End users can request specific information from the server using voice commands. For example, if a voice input says, "I want to go to the latest novels section," that information is sent to the server, and appropriate navigation is initiated. The feedback collected during this process is stored on the server and used to improve the accuracy of navigation in the future.

[0117] As a concrete example, if an end-user inputs a prompt message via voice, such as "Please guide me to the candy aisle," the system will provide voice guidance for the shortest route to the desired aisle and also issue warnings about obstacles along the way. In this way, it provides support to help visually impaired individuals move safely and effectively within the store and find the products they are looking for.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The device acquires ambient environmental data using a LiDAR sensor and camera. This data input consists of the physical structure and location of items within the store, and the output is environmental information in digital format. This information can be acquired in real time, recording the store's layout and the placement of items.

[0121] Step 2:

[0122] The terminal performs initial processing on the acquired environmental data and converts it into a specific packet format. This process compresses the data size and reshapes it into a format suitable for transmission. The output of this packet format is data ready to be sent to the server.

[0123] Step 3:

[0124] The server receives packet-formatted environmental data sent from the terminal. The received data is passed as input to an AI algorithm for analysis. The server analyzes the data using a generative AI model based on TensorFlow and generates instructions about the store layout and the location of items. The output is voice guidance information to be provided to the user.

[0125] Step 4:

[0126] The server converts the generated voice guidance information into audio data using speech synthesis software. The input for this conversion is the voice guidance information, and the output is audio in a format audible to the end user. Speech synthesis is performed using tools such as Google Text-to-Speech.

[0127] Step 5:

[0128] Users navigate the store based on voice guidance. They can also input voice commands into the terminal as needed. An example of input is a prompt such as "Take me to the beverage section." The server's response is a specific instruction to move to the destination.

[0129] Step 6:

[0130] The server receives feedback from users and updates its database based on this feedback. The input consists of data about the user's behavior history and navigation accuracy, while the output is an improved data model used to generate future instructions.

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] This invention is a system that acquires and analyzes environmental information to enable visually impaired and other disabled individuals to live independent lives. This system is characterized by its ability to recognize each user's emotions in real time and provide effective information tailored to those emotions.

[0133] Terminal part

[0134] The device is equipped with various sensors to acquire environmental information. These include a LiDAR sensor to capture the room layout and a microphone to acquire acoustic information. It also features a camera and voice analysis system as an emotion engine to recognize the user's voice tone and facial expressions. The device transmits the acquired data to a server in real time.

[0135] Server portion

[0136] The server receives environmental and emotional data transmitted from the terminal and analyzes both sets of data in an integrated manner. Using an AI algorithm, it determines the user's current emotional state and generates individually optimized voice guidance and vibration feedback. The analysis results are used to select information delivery templates based on the user's emotional state. This template selection changes depending on whether the user is calm or agitated.

[0137] Information provision and feedback

[0138] Users receive environment-related information and emotionally responsive feedback from the device. Speech synthesis is used to provide user-friendly guidance. For example, if a user is experiencing stress, the device will provide guidance in a calm tone, offering gentle encouragement and support.

[0139] By incorporating an emotion engine, the system can address the emotional needs of users that cannot be met by conventional voice guidance systems. This system is expected to significantly improve the quality of life for people with disabilities, as they will receive not only physical but also emotional support.

[0140] Ultimately, user feedback is collected on the server via the device and used for future analysis. It is also incorporated into the entire system as training data, improving the accuracy of user-specific customization.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The device acquires environmental information and user emotion data. Environmental information includes distance measurement data from a LiDAR sensor, while emotion data is collected from the voice analysis sensor for the user's voice tone and from the camera for facial expression data.

[0144] Step 2:

[0145] The device converts the acquired environmental and emotional data into packets and sends them to the server. During this process, the data type and date / time information are transmitted in an identifiable format.

[0146] Step 3:

[0147] The server receives data from the terminal and begins analyzing environmental and emotional data. An AI algorithm generates a 3D map of the environment, while the emotion engine simultaneously analyzes the user's emotional state.

[0148] Step 4:

[0149] Based on the analysis results, the server generates voice guidance and vibration feedback that corresponds to the user's emotional state. For example, if a stressed state is detected, it selects a calm voice guidance that promotes stabilization.

[0150] Step 5:

[0151] The server generates guidance data and sends it to the terminal. The guidance templates are customized according to the user's emotional state.

[0152] Step 6:

[0153] The terminal receives guidance data from the server and provides information to the user using speech synthesis and vibration signals. The voice is played back at a speed and tone that is easy for the user to hear.

[0154] Step 7:

[0155] Users provide feedback, which is then sent to the server via their devices. This feedback is provided via voice or button input and is used to improve the accuracy of the analysis.

[0156] Step 8:

[0157] The server analyzes the feedback and updates the system's database. This update improves the accuracy and customization of future guidance.

[0158] (Example 2)

[0159] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0160] For people with disabilities, such as those with visual impairments, to live independently, they need to properly understand environmental information and receive appropriate emotional support. However, while conventional assistive devices can provide physical support, they lack the ability to provide appropriate feedback tailored to the user's emotional state.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes an information acquisition device means equipped with sensors for acquiring environmental information and emotional data; an information provision means for providing the user with audio and tactile information based on the analysis results; and a data storage device means for receiving feedback from the user and updating the learning data. This makes it possible to provide optimal support in real time according to the user's emotional state and to meet the user's emotional needs.

[0163] An "information acquisition device means" is a device that uses sensors to acquire environmental information and emotional data from a user.

[0164] A "data processing device" is a device that receives environmental information and emotional data acquired by an information acquisition device and analyzes it.

[0165] An "information provision means" is a device that provides users with necessary information through sound or touch based on the analyzed results.

[0166] A "data storage device" is a device that receives feedback from users, stores it as learning data, and updates it.

[0167] "Environmental information" refers to information that indicates the physical state and conditions around the user, and is data acquired through sensors.

[0168] "Emotional data" refers to data that analyzes the tone of a user's voice and facial expressions to indicate their emotional state.

[0169] A "server" is a central system that receives and analyzes data from multiple devices and provides feedback as needed.

[0170] This invention relates to a support system that uses environmental information and emotional state analysis to enable visually impaired and other disabled individuals to live independent lives.

[0171] The device is equipped with various sensors and plays a role in collecting environmental information and emotional data. Specifically, a LiDAR sensor captures the room layout and arrangement, and a microphone acquires ambient acoustic information. In addition, a camera and voice analysis system monitor the user's facial expressions and voice tone to collect emotional data. This data is transmitted to a server in real time.

[0172] The server integrates received environmental information and emotional data and performs analysis using an AI algorithm. Based on the analysis, the server determines the user's emotional state and generates voice guidance and vibration feedback optimized for that state. For example, if the user is feeling stressed, the server generates voice feedback in a calm tone and sends it to the device.

[0173] Users receive audio and haptic feedback from the device to understand information related to their environment. This feedback is provided using speech synthesis technology, ensuring it is presented in a user-friendly format. For example, if the user is detected as being under high stress, a message such as "You're doing well. Let's take a deep breath" might be provided.

[0174] Furthermore, the server continuously collects user feedback from terminals and uses it as training data. This makes it possible to improve the overall feedback accuracy of the system using a generative AI model.

[0175] An example of a prompt would be, "If the AI ​​determines that the user is currently experiencing high stress, what kind of voice feedback should be provided?" This serves as a specific guideline for the AI ​​to provide the user with the most appropriate guidance.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The device is responsible for collecting environmental information and emotional data. It uses a LiDAR sensor to acquire room layout data and a microphone to collect acoustic data. It also captures the user's facial expressions with a camera and analyzes their voice tone with a voice analysis system. The data from these sensors becomes input and is sent to the server as output from the device in a structured format.

[0179] Step 2:

[0180] The server receives environmental information and emotional data transmitted from the terminal and stores it in a database. Using the received data as input, an AI algorithm performs an integrated analysis. This data analysis evaluates the emotional state based on the user's facial expressions and tone of voice. Based on this analysis, the user's emotional state (e.g., relaxed, stressed) is output.

[0181] Step 3:

[0182] The server uses the results of analysis by an AI algorithm to generate voice guidance and vibration feedback optimized for the user's current emotional state. In this process, the analysis results are treated as input, and based on this, the optimal feedback content is selected from a template, and feedback data is constructed as output.

[0183] Step 4:

[0184] The terminal receives feedback data sent from the server and uses speech synthesis to provide voice guidance to the user. Specifically, the voice guidance system synthesizes a message and plays it through the speaker. It also activates a vibration motor as needed to provide haptic feedback. As a result, the user receives both the generated voice and haptic feedback.

[0185] Step 5:

[0186] The user responds to the provided feedback, and this feedback is collected by the device. The input here is the user's response data, which is output to the server via the device and stored as accumulated data. This data is used with a generative AI model to improve the accuracy of future feedback.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] In autonomous vehicles, it is crucial that passengers feel safe and secure during their journey. However, current technology does not adequately provide systems that adjust their behavior in response to passenger emotions. Therefore, there is a need to alleviate passenger anxiety and provide a comfortable travel experience.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes means for analyzing environmental information collected from a computing device and adjusting the operating style within the vehicle based on the passenger's emotional state, means for determining the passenger's emotions in real time and providing a sense of security, and means for collecting feedback from users and improving the entire system. This enables passengers to use autonomous vehicles with peace of mind and enjoy a comfortable travel experience.

[0192] A "detector" is a sensor device used to acquire environmental information and transmit this information to a computing device.

[0193] A "computation device" is an electronic device that acquires environmental information from detectors and transmits it to a computing server.

[0194] A "computation server" is a server that performs analysis based on information transmitted from a computing device and has the function of determining the emotional state of a user.

[0195] "Adjusting the operation plan" is the process of changing how a mobile device or system operates based on the user's emotional state.

[0196] "Feedback" refers to the opinions and reactions that users provide after using a system, and is information that is used to update and improve the system.

[0197] The system realizing this invention first uses LIDAR sensors, microphones, and cameras to acquire all environmental information in real time using a computing device. These devices can collect physical information, acoustic information, and facial expression information from the surroundings. The information transmitted from the computing device is transferred to a computing server. The server uses advanced AI algorithms to analyze the emotional state of the passengers. As specific AI algorithms, TensorFlow and Keras are used for emotion analysis, and Google Cloud Speech-to-Text is used for speech analysis.

[0198] The server adjusts the operation plan based on the analysis results. If the passenger is experiencing stress, it issues instructions to change the vehicle's driving style. If reassurance is needed, it uses a speech synthesis system to provide voice announcements such as "The vehicle is being driven safely." Speech synthesis technologies such as Amazon Polly can be applied to the backend. Feedback from the passenger is fed back to the computing server and used for system learning and improvement.

[0199] For example, if a passenger feels uneasy on a sharp curve, the server will instruct the vehicle to adjust its speed in real time and provide a smooth voice announcement saying, "We are driving safely in this section." It can also set prompts such as, "Use a generative AI model to create an optimal driving plan based on the passenger's emotional state."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The device uses a LiDAR sensor, microphone, and camera to acquire physical, acoustic, and facial information about its surroundings in order to collect environmental information. In this step, data from each sensor is acquired in real time and prepared for processing. The input is environmental data from the sensors, and the output is initial data from the various sensors.

[0203] Step 2:

[0204] The terminal compresses the acquired environmental data and transmits it to the computing server via wireless communication. This step converts the data into a format suitable for data transfer, streamlining processing on the server side. The input is sensor information, and the output is the data to be transmitted to the server.

[0205] Step 3:

[0206] The server analyzes the received environmental data using an AI algorithm. This analysis includes a process that uses TensorFlow and Keras to determine the emotional state. The input is environmental data from the terminal, and the output is the analyzed emotional state of the passenger.

[0207] Step 4:

[0208] The server sends instructions to the vehicle to adjust its operation plan based on the analysis results. For example, if the server determines that the passenger is anxious, it will issue an instruction to adjust the vehicle's speed. The input is the result of the emotion analysis, and the output is a control instruction to the vehicle.

[0209] Step 5:

[0210] The terminal uses a speech synthesis system to provide voice guidance to users based on instructions from the server. Here, technologies such as Amazon Polly are used to generate voice messages in real time, providing passengers with a sense of security. Input is voice instructions from the server, and output is the generated voice guidance.

[0211] Step 6:

[0212] Users send feedback on their travel experiences to the server via their device, which is then used for data analysis in subsequent visits. This feedback information is also used as training data for the generated AI model, contributing to improvements in the services provided. The input is user feedback information, and the output is training data stored on the server.

[0213] 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.

[0214] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0220] 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.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0222] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] 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.

[0224] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0226] The 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.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0229] This invention provides a system that enables people with disabilities, particularly those with visual impairments, to live more independent lives. The system's main components are a terminal equipped with sensors and a server that performs data analysis.

[0230] Terminal part

[0231] The device is equipped with multiple sensors to acquire information about the surrounding environment in real time. This includes various environmental sensors such as ultrasound and LiDAR to acquire information about the room layout and the placement of objects. The collected data is initially processed, converted into a specific packet format, and sent to the server.

[0232] Server portion

[0233] The server maintains a database of environmental information in the cloud and performs data analysis based on this database. By analyzing the received environmental data using AI algorithms, it generates the information the user needs and constructs customized feedback. The generated feedback is sent to the terminal as commands to control speech synthesis and vibration motors.

[0234] Providing feedback to users

[0235] The user carries a device and receives information transmitted from the server through it. For example, when the user enters a room, the device provides voice guidance about the surrounding layout. This might include messages such as, "There is a table in the center of the room. Please be careful not to step over it." Furthermore, if an object is difficult to maneuver, the device can communicate the appropriate steps to the user through vibration patterns.

[0236] In actual use cases, the system also includes a function that allows users to input voice commands using a terminal. For example, if a user issues the voice command "Tell me the location of the table," the terminal sends a request to the server, and the server responds with the table's location information via voice as a result of its analysis. The feedback obtained during this process is sent to the server and used for future analysis and suggestions.

[0237] This system enables visually impaired individuals to overcome geographical and physical barriers and live their daily lives more smoothly. Furthermore, the program can be expanded to provide similar support to people with other disabilities.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The device scans the surrounding environment and acquires sensor data. The sensors include ultrasonic sensors, LiDAR, and microphones, and this data is used to generate a 3D map of the environment.

[0241] Step 2:

[0242] The terminal converts the initial data into packet format and sends it to the server. At this time, a timestamp is added to the data so that it can be used as time-series data for later analysis.

[0243] Step 3:

[0244] The server receives environmental data and stores it in a database. Simultaneously, an AI algorithm analyzes the data and extracts the environmental information requested by the user.

[0245] Step 4:

[0246] The server generates commands to provide voice guidance or vibration feedback based on the analysis results and sends them to the terminal.

[0247] Step 5:

[0248] The terminal receives commands from the server and transmits guidance messages via speech synthesis and vibration signals to the user.

[0249] Step 6:

[0250] The system acts based on the information received by the user and inputs voice commands into the terminal as needed.

[0251] Step 7:

[0252] The terminal analyzes the user's voice commands and sends them to the server. It also receives user feedback in real time and sends it to the server.

[0253] Step 8:

[0254] The server analyzes the feedback and updates the database. This feedback is then incorporated into the AI ​​algorithm as training data, helping to improve the accuracy of future analyses.

[0255] (Example 1)

[0256] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0257] There is a need to provide support to users with visual or mobility limitations so that they can move safely and comfortably in their daily lives and understand their surroundings. However, conventional technologies have limitations in acquiring environmental information and providing feedback to users, and have been unable to adequately meet the individual needs of users.

[0258] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0259] In this invention, the server includes a device means equipped with multiple environmental sensors for acquiring spatial information of the surroundings; an information processing device means for receiving spatial data transmitted from the device means and analyzing it using a machine learning algorithm; a means for controlling a speech synthesis engine and a vibration motor based on the analysis results to provide information to the user; and a means for receiving commands and feedback from the user and updating a history database. This enables users with visual or movement limitations to move safely and understand their surrounding physical situation through real-time, customized feedback.

[0260] "Device means" refers to a module equipped with multiple environmental sensors for acquiring surrounding spatial information and an initial filtering function for processing the data obtained from these sensors.

[0261] "Information processing device means" refers to a system that receives spatial data transmitted from a device means and performs data analysis using machine learning algorithms.

[0262] An "environmental sensor" refers to a measuring element that uses ultrasound, LIDAR, or other technologies to acquire information about surrounding objects and space in real time.

[0263] A "machine learning algorithm" refers to a computer program that analyzes received data and performs pattern recognition or data classification.

[0264] A "speech synthesis engine" refers to software equipped with processing functions to convert text data into speech and provide it to the user as auditory information.

[0265] A "vibration motor" refers to a device that provides tactile feedback to the user by generating physical vibrations.

[0266] A "history database" refers to an information recording system that stores user feedback and command history for use in future analysis and user profile construction.

[0267] This invention is a support system designed to enable visually impaired individuals and users with limited mobility to live their daily lives more independently. The system primarily consists of a terminal equipped with multiple environmental sensors and a server for data analysis.

[0268] terminal

[0269] The device is equipped with environmental sensors such as ultrasonic sensors and LiDAR sensors. This allows the device to acquire spatial information about its surroundings in real time. The data obtained from the sensors is filtered to remove noise, converted into a specific packet format, and sent to the server.

[0270] server

[0271] The server resides in the cloud and receives spatial data transmitted from terminals. This data is analyzed using machine learning algorithms to extract information useful to the user. This analysis applies generative AI models that perform data classification and pattern recognition. Specific examples include guidance on the user's movement within a room and location information of obstacles.

[0272] Feedback system

[0273] Based on the analysis results, the server generates voice feedback using a speech synthesis engine. It can also provide tactile feedback by controlling a vibration motor. This allows users to obtain safety information about their destination without relying on their vision.

[0274] User interaction

[0275] Users can input voice commands into the terminal. For example, if they give the command "Tell me where the table is," the server will analyze it and return specific location information via voice. User feedback and usage history are constantly recorded and stored in the server's history database, which improves the system's accuracy and responsiveness.

[0276] Prompt statements as concrete examples

[0277] "Please implement an audio guidance system that tells you what furniture is in the room and how it's arranged when you enter a new room."

[0278] This system allows users with visual or mobility limitations to more easily overcome physical obstacles in their surroundings, enabling them to live a safe and independent daily life.

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] The terminal uses multiple environmental sensors to obtain the surrounding space information. The sensors use ultrasonic waves or LIDAR to obtain the position and distance information of objects. This data is collected in real time and filtered to remove noise. The input is the raw data from the sensors, and the output is the data that has been denoised and formatted. As a specific operation, the terminal accurately measures the positions of the walls and furniture in the room within a few seconds.

[0282] Step 2:

[0283] <0000�96>The terminal converts the filtered data into a specific packet format and transmits it to the server. The input is the filtered environmental data, and the output is the data packet corresponding to the server. In a specific operation, after the conversion process, the data is quickly transmitted to the server on the cloud via wireless communication.

[0284] Step 3:

[0285] The server receives the data packet sent from the terminal and performs analysis using a generated AI model. The input is the data packet from the terminal, and the output is the analysis result based on the environmental information. In this analysis, important features and patterns are extracted using machine learning algorithms. As a specific operation, the server reconstructs the layout of the room from the received data and identifies important obstacle information.

[0286] Step 4:

[0287] The server uses a speech synthesis engine to generate voice feedback based on the analysis result and creates a control signal for the vibration motor. The input is the analysis result, and the output is the voice information and the control command for the vibration pattern. As a specific operation, the server generates a message such as "There is a table in the center of the room. Do not approach it."

[0288] Step 5:

[0289] The terminal provides feedback information received from the server to the user. It uses a speech synthesis engine to provide voice guidance and controls a vibration motor to deliver tactile alerts. Input is commands from the server, and output is actual voice and vibration feedback to the user. For example, it might provide a voice announcement such as "The door ahead is closed" and indicate its direction with vibration.

[0290] Step 6:

[0291] Users can operate the terminal and input voice commands. New requests are sent to the server, and the quality and content of the feedback are reviewed. The input is the user's voice command, and the output is a request signal to the server. For example, if the user commands, "Tell me the information for the next room," the system will acquire and analyze further information.

[0292] Step 7:

[0293] The server stores user feedback and command history in a history database. This data is then used for future analysis, improving system responsiveness. The input is user feedback information, and the output is the updated history database. Specifically, the server analyzes the user's action history and updates the model for predicting future actions.

[0294] (Application Example 1)

[0295] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0296] It is generally difficult for visually impaired individuals to search for products and move smoothly within physical stores. Their shopping experience is often limited due to their difficulty in understanding store layouts and product placement. There is a need to address this challenge and enable visually impaired individuals to move safely and effectively within stores and find products.

[0297] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0298] In this invention, the server includes means for providing end users with voice guidance on the location of items based on the installation environment, means for providing voice instructions to assist end users in moving around within the store, and means for predicting the location of items and generating instructions using a generation algorithm. This makes it easier for visually impaired people to understand their location within a physical store and enables them to search for products safely and efficiently.

[0299] "Environmental data" refers to information collected by measurement unit means to acquire physical information about the surrounding environment.

[0300] A "measurement unit means" is a device equipped with sensors for acquiring environmental data, mainly using graphic sensors or laser rangefinders.

[0301] The "processing unit means" refers to a server or computer system that analyzes environmental data received from the measurement unit means and generates information to be provided to the user.

[0302] "End users" refer to the final users of a system, and especially individuals who require assistance, such as those with visual impairments.

[0303] "Voice guidance" refers to voice instructions generated based on analyzed information, informing end users of the location of items and the route they should take.

[0304] "Voice instructions" refer to voice guidance provided to assist end users in navigating within a store.

[0305] A "generative algorithm" is a computational method and processing step used to predict the location of an object based on environmental data and generate instructions regarding that location.

[0306] The present invention provides a system for visually impaired people to move safely in a physical store and effectively search for products. The system is mainly composed of a measurement unit means, a processing unit means, and a means for providing information to the end user.

[0307] The measurement unit means is installed on smart glasses or mobile terminals, and uses a LIDAR sensor or a camera to acquire environmental data. This data is information including the positions and layouts of articles in the store.

[0308] The server receives the environmental data transmitted from the measurement unit means on a cloud platform. For this process, cloud services such as AWS Lambda or Google Cloud AI are used, and an AI model using TensorFlow analyzes the environmental data. As a result of the analysis, information necessary for the end user is generated.

[0309] Based on the analysis results, the server generates an instruction for providing voice guidance to the user. The voice instruction is transmitted to the end user using voice synthesis software such as Google Text-to-Speech. Also, the generation algorithm predicts the route from the user's current position to the destination article and provides guidance in real time.

[0310] The end user can request specific information from the server by voice commands. For example, when a voice input such as "I want to go to the latest novel section" is made, that information is transmitted to the server and appropriate navigation is started. The feedback collected in this process is accumulated in the server and utilized for improving future navigation accuracy.

[0311] As a concrete example, if an end-user inputs a prompt message via voice, such as "Please guide me to the candy aisle," the system will provide voice guidance for the shortest route to the desired aisle and also issue warnings about obstacles along the way. In this way, it provides support to help visually impaired individuals move safely and effectively within the store and find the products they are looking for.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The device acquires ambient environmental data using a LiDAR sensor and camera. This data input consists of the physical structure and location of items within the store, and the output is environmental information in digital format. This information can be acquired in real time, recording the store's layout and the placement of items.

[0315] Step 2:

[0316] The terminal performs initial processing on the acquired environmental data and converts it into a specific packet format. This process compresses the data size and reshapes it into a format suitable for transmission. The output of this packet format is data ready to be sent to the server.

[0317] Step 3:

[0318] The server receives packet-formatted environmental data sent from the terminal. The received data is passed as input to an AI algorithm for analysis. The server analyzes the data using a generative AI model based on TensorFlow and generates instructions about the store layout and the location of items. The output is voice guidance information to be provided to the user.

[0319] Step 4:

[0320] The server converts the generated voice guidance information into audio data using speech synthesis software. The input for this conversion is the voice guidance information, and the output is audio in a format audible to the end user. Speech synthesis is performed using tools such as Google Text-to-Speech.

[0321] Step 5:

[0322] Users navigate the store based on voice guidance. They can also input voice commands into the terminal as needed. An example of input is a prompt such as "Take me to the beverage section." The server's response is a specific instruction to move to the destination.

[0323] Step 6:

[0324] The server receives feedback from users and updates its database based on this feedback. The input consists of data about the user's behavior history and navigation accuracy, while the output is an improved data model used to generate future instructions.

[0325] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0326] This invention is a system that acquires and analyzes environmental information to enable visually impaired and other disabled individuals to live independent lives. This system is characterized by its ability to recognize each user's emotions in real time and provide effective information tailored to those emotions.

[0327] Terminal part

[0328] The device is equipped with various sensors to acquire environmental information. These include a LiDAR sensor to capture the room layout and a microphone to acquire acoustic information. It also features a camera and voice analysis system as an emotion engine to recognize the user's voice tone and facial expressions. The device transmits the acquired data to a server in real time.

[0329] Server portion

[0330] The server receives environmental and emotional data transmitted from the terminal and analyzes both sets of data in an integrated manner. Using an AI algorithm, it determines the user's current emotional state and generates individually optimized voice guidance and vibration feedback. The analysis results are used to select information delivery templates based on the user's emotional state. This template selection changes depending on whether the user is calm or agitated.

[0331] Information provision and feedback

[0332] Users receive environment-related information and emotionally responsive feedback from the device. Speech synthesis is used to provide user-friendly guidance. For example, if a user is experiencing stress, the device will provide guidance in a calm tone, offering gentle encouragement and support.

[0333] By incorporating an emotion engine, the system can address the emotional needs of users that cannot be met by conventional voice guidance systems. This system is expected to significantly improve the quality of life for people with disabilities, as they will receive not only physical but also emotional support.

[0334] Ultimately, user feedback is collected on the server via the device and used for future analysis. It is also incorporated into the entire system as training data, improving the accuracy of user-specific customization.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] The device acquires environmental information and user emotion data. Environmental information includes distance measurement data from a LiDAR sensor, while emotion data is collected from the voice analysis sensor for the user's voice tone and from the camera for facial expression data.

[0338] Step 2:

[0339] The device converts the acquired environmental and emotional data into packets and sends them to the server. During this process, the data type and date / time information are transmitted in an identifiable format.

[0340] Step 3:

[0341] The server receives data from the terminal and begins analyzing environmental and emotional data. An AI algorithm generates a 3D map of the environment, while the emotion engine simultaneously analyzes the user's emotional state.

[0342] Step 4:

[0343] Based on the analysis results, the server generates voice guidance and vibration feedback that corresponds to the user's emotional state. For example, if a stressed state is detected, it selects a calm voice guidance that promotes stabilization.

[0344] Step 5:

[0345] The server generates guidance data and sends it to the terminal. The guidance templates are customized according to the user's emotional state.

[0346] Step 6:

[0347] The terminal receives guidance data from the server and provides information to the user using speech synthesis and vibration signals. The voice is played back at a speed and tone that is easy for the user to hear.

[0348] Step 7:

[0349] Users provide feedback, which is then sent to the server via their devices. This feedback is provided via voice or button input and is used to improve the accuracy of the analysis.

[0350] Step 8:

[0351] The server analyzes the feedback and updates the system's database. This update improves the accuracy and customization of future guidance.

[0352] (Example 2)

[0353] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0354] For people with disabilities, such as those with visual impairments, to live independently, they need to properly understand environmental information and receive appropriate emotional support. However, while conventional assistive devices can provide physical support, they lack the ability to provide appropriate feedback tailored to the user's emotional state.

[0355] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0356] In this invention, the server includes an information acquisition device means equipped with sensors for acquiring environmental information and emotional data; an information provision means for providing the user with audio and tactile information based on the analysis results; and a data storage device means for receiving feedback from the user and updating the learning data. This makes it possible to provide optimal support in real time according to the user's emotional state and to meet the user's emotional needs.

[0357] An "information acquisition device means" is a device that uses sensors to acquire environmental information and emotional data from a user.

[0358] A "data processing device" is a device that receives environmental information and emotional data acquired by an information acquisition device and analyzes it.

[0359] An "information provision means" is a device that provides users with necessary information through sound or touch based on the analyzed results.

[0360] A "data storage device" is a device that receives feedback from users, stores it as learning data, and updates it.

[0361] "Environmental information" refers to information that indicates the physical state and conditions around the user, and is data acquired through sensors.

[0362] "Emotional data" refers to data that analyzes the tone of a user's voice and facial expressions to indicate their emotional state.

[0363] A "server" is a central system that receives and analyzes data from multiple devices and provides feedback as needed.

[0364] This invention relates to a support system that uses environmental information and emotional state analysis to enable visually impaired and other disabled individuals to live independent lives.

[0365] The device is equipped with various sensors and plays a role in collecting environmental information and emotional data. Specifically, a LiDAR sensor captures the room layout and arrangement, and a microphone acquires ambient acoustic information. In addition, a camera and voice analysis system monitor the user's facial expressions and voice tone to collect emotional data. This data is transmitted to a server in real time.

[0366] The server integrates received environmental information and emotional data and performs analysis using an AI algorithm. Based on the analysis, the server determines the user's emotional state and generates voice guidance and vibration feedback optimized for that state. For example, if the user is feeling stressed, the server generates voice feedback in a calm tone and sends it to the device.

[0367] Users receive audio and haptic feedback from the device to understand information related to their environment. This feedback is provided using speech synthesis technology, ensuring it is presented in a user-friendly format. For example, if the user is detected as being under high stress, a message such as "You're doing well. Let's take a deep breath" might be provided.

[0368] Furthermore, the server continuously collects user feedback from terminals and uses it as training data. This makes it possible to improve the overall feedback accuracy of the system using a generative AI model.

[0369] An example of a prompt would be, "If the AI ​​determines that the user is currently experiencing high stress, what kind of voice feedback should be provided?" This serves as a specific guideline for the AI ​​to provide the user with the most appropriate guidance.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The device is responsible for collecting environmental information and emotional data. It uses a LiDAR sensor to acquire room layout data and a microphone to collect acoustic data. It also captures the user's facial expressions with a camera and analyzes their voice tone with a voice analysis system. The data from these sensors becomes input and is sent to the server as output from the device in a structured format.

[0373] Step 2:

[0374] The server receives environmental information and emotional data transmitted from the terminal and stores it in a database. Using the received data as input, an AI algorithm performs an integrated analysis. This data analysis evaluates the emotional state based on the user's facial expressions and tone of voice. Based on this analysis, the user's emotional state (e.g., relaxed, stressed) is output.

[0375] Step 3:

[0376] The server uses the results of analysis by an AI algorithm to generate voice guidance and vibration feedback optimized for the user's current emotional state. In this process, the analysis results are treated as input, and based on this, the optimal feedback content is selected from a template, and feedback data is constructed as output.

[0377] Step 4:

[0378] The terminal receives feedback data sent from the server and uses speech synthesis to provide voice guidance to the user. Specifically, the voice guidance system synthesizes a message and plays it through the speaker. It also activates a vibration motor as needed to provide haptic feedback. As a result, the user receives both the generated voice and haptic feedback.

[0379] Step 5:

[0380] The user responds to the provided feedback, and this feedback is collected by the device. The input here is the user's response data, which is output to the server via the device and stored as accumulated data. This data is used with a generative AI model to improve the accuracy of future feedback.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] In autonomous vehicles, it is crucial that passengers feel safe and secure during their journey. However, current technology does not adequately provide systems that adjust their behavior in response to passenger emotions. Therefore, there is a need to alleviate passenger anxiety and provide a comfortable travel experience.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for analyzing environmental information collected from a computing device and adjusting the operating style within the vehicle based on the passenger's emotional state, means for determining the passenger's emotions in real time and providing a sense of security, and means for collecting feedback from users and improving the entire system. This enables passengers to use autonomous vehicles with peace of mind and enjoy a comfortable travel experience.

[0386] A "detector" is a sensor device used to acquire environmental information and transmit this information to a computing device.

[0387] A "computation device" is an electronic device that acquires environmental information from detectors and transmits it to a computing server.

[0388] A "computation server" is a server that performs analysis based on information transmitted from a computing device and has the function of determining the emotional state of a user.

[0389] "Adjusting the operation plan" is the process of changing how a mobile device or system operates based on the user's emotional state.

[0390] "Feedback" refers to the opinions and reactions that users provide after using a system, and is information that is used to update and improve the system.

[0391] The system realizing this invention first uses LIDAR sensors, microphones, and cameras to acquire all environmental information in real time using a computing device. These devices can collect physical information, acoustic information, and facial expression information from the surroundings. The information transmitted from the computing device is transferred to a computing server. The server uses advanced AI algorithms to analyze the emotional state of the passengers. As specific AI algorithms, TensorFlow and Keras are used for emotion analysis, and Google Cloud Speech-to-Text is used for speech analysis.

[0392] The server adjusts the operation plan based on the analysis results. If the passenger is experiencing stress, it issues instructions to change the vehicle's driving style. If reassurance is needed, it uses a speech synthesis system to provide voice announcements such as "The vehicle is being driven safely." Speech synthesis technologies such as Amazon Polly can be applied to the backend. Feedback from the passenger is fed back to the computing server and used for system learning and improvement.

[0393] For example, if a passenger feels uneasy on a sharp curve, the server will instruct the vehicle to adjust its speed in real time and provide a smooth voice announcement saying, "We are driving safely in this section." It can also set prompts such as, "Use a generative AI model to create an optimal driving plan based on the passenger's emotional state."

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The device uses a LiDAR sensor, microphone, and camera to acquire physical, acoustic, and facial information about its surroundings in order to collect environmental information. In this step, data from each sensor is acquired in real time and prepared for processing. The input is environmental data from the sensors, and the output is initial data from the various sensors.

[0397] Step 2:

[0398] The terminal compresses the acquired environmental data and transmits it to the computing server via wireless communication. This step converts the data into a format suitable for data transfer, streamlining processing on the server side. The input is sensor information, and the output is the data to be transmitted to the server.

[0399] Step 3:

[0400] The server analyzes the received environmental data using an AI algorithm. This analysis includes a process that uses TensorFlow and Keras to determine the emotional state. The input is environmental data from the terminal, and the output is the analyzed emotional state of the passenger.

[0401] Step 4:

[0402] The server sends instructions to the vehicle to adjust its operation plan based on the analysis results. For example, if the server determines that the passenger is anxious, it will issue an instruction to adjust the vehicle's speed. The input is the result of the emotion analysis, and the output is a control instruction to the vehicle.

[0403] Step 5:

[0404] The terminal uses a speech synthesis system to provide voice guidance to users based on instructions from the server. Here, technologies such as Amazon Polly are used to generate voice messages in real time, providing passengers with a sense of security. Input is voice instructions from the server, and output is the generated voice guidance.

[0405] Step 6:

[0406] Users send feedback on their travel experiences to the server via their device, which is then used for data analysis in subsequent visits. This feedback information is also used as training data for the generated AI model, contributing to improvements in the services provided. The input is user feedback information, and the output is training data stored on the server.

[0407] 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.

[0408] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0409] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0414] 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.

[0415] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0416] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0417] 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.

[0418] 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.

[0419] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0420] The 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.

[0421] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0422] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0423] This invention provides a system that enables people with disabilities, particularly those with visual impairments, to live more independent lives. The system's main components are a terminal equipped with sensors and a server that performs data analysis.

[0424] Terminal part

[0425] The device is equipped with multiple sensors to acquire information about the surrounding environment in real time. This includes various environmental sensors such as ultrasound and LiDAR to acquire information about the room layout and the placement of objects. The collected data is initially processed, converted into a specific packet format, and sent to the server.

[0426] Server portion

[0427] The server maintains a database of environmental information in the cloud and performs data analysis based on this database. By analyzing the received environmental data using AI algorithms, it generates the information the user needs and constructs customized feedback. The generated feedback is sent to the terminal as commands to control speech synthesis and vibration motors.

[0428] Providing feedback to users

[0429] The user carries a device and receives information transmitted from the server through it. For example, when the user enters a room, the device provides voice guidance about the surrounding layout. This might include messages such as, "There is a table in the center of the room. Please be careful not to step over it." Furthermore, if an object is difficult to maneuver, the device can communicate the appropriate steps to the user through vibration patterns.

[0430] In actual use cases, the system also includes a function that allows users to input voice commands using a terminal. For example, if a user issues the voice command "Tell me the location of the table," the terminal sends a request to the server, and the server responds with the table's location information via voice as a result of its analysis. The feedback obtained during this process is sent to the server and used for future analysis and suggestions.

[0431] This system enables visually impaired individuals to overcome geographical and physical barriers and live their daily lives more smoothly. Furthermore, the program can be expanded to provide similar support to people with other disabilities.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The device scans the surrounding environment and acquires sensor data. The sensors include ultrasonic sensors, LiDAR, and microphones, and this data is used to generate a 3D map of the environment.

[0435] Step 2:

[0436] The terminal converts the initial data into packet format and sends it to the server. At this time, a timestamp is added to the data so that it can be used as time-series data for later analysis.

[0437] Step 3:

[0438] The server receives environmental data and stores it in a database. Simultaneously, an AI algorithm analyzes the data and extracts the environmental information requested by the user.

[0439] Step 4:

[0440] The server generates commands to provide voice guidance or vibration feedback based on the analysis results and sends them to the terminal.

[0441] Step 5:

[0442] The terminal receives commands from the server and transmits guidance messages via speech synthesis and vibration signals to the user.

[0443] Step 6:

[0444] The system acts based on the information received by the user and inputs voice commands into the terminal as needed.

[0445] Step 7:

[0446] The terminal analyzes the user's voice commands and sends them to the server. It also receives user feedback in real time and sends it to the server.

[0447] Step 8:

[0448] The server analyzes the feedback and updates the database. This feedback is then incorporated into the AI ​​algorithm as training data, helping to improve the accuracy of future analyses.

[0449] (Example 1)

[0450] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0451] There is a need to provide support to users with visual or mobility limitations so that they can move safely and comfortably in their daily lives and understand their surroundings. However, conventional technologies have limitations in acquiring environmental information and providing feedback to users, and have been unable to adequately meet the individual needs of users.

[0452] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0453] In this invention, the server includes a device means equipped with multiple environmental sensors for acquiring spatial information of the surroundings; an information processing device means for receiving spatial data transmitted from the device means and analyzing it using a machine learning algorithm; a means for controlling a speech synthesis engine and a vibration motor based on the analysis results to provide information to the user; and a means for receiving commands and feedback from the user and updating a history database. This enables users with visual or movement limitations to move safely and understand their surrounding physical situation through real-time, customized feedback.

[0454] "Device means" refers to a module equipped with multiple environmental sensors for acquiring surrounding spatial information and an initial filtering function for processing the data obtained from these sensors.

[0455] "Information processing device means" refers to a system that receives spatial data transmitted from a device means and performs data analysis using machine learning algorithms.

[0456] An "environmental sensor" refers to a measuring element that uses ultrasound, LIDAR, or other technologies to acquire information about surrounding objects and space in real time.

[0457] A "machine learning algorithm" refers to a computer program that analyzes received data and performs pattern recognition or data classification.

[0458] A "speech synthesis engine" refers to software equipped with processing functions to convert text data into speech and provide it to the user as auditory information.

[0459] A "vibration motor" refers to a device that provides tactile feedback to the user by generating physical vibrations.

[0460] A "history database" refers to an information recording system that stores user feedback and command history for use in future analysis and user profile construction.

[0461] This invention is a support system designed to enable visually impaired individuals and users with limited mobility to live their daily lives more independently. The system primarily consists of a terminal equipped with multiple environmental sensors and a server for data analysis.

[0462] terminal

[0463] The device is equipped with environmental sensors such as ultrasonic sensors and LiDAR sensors. This allows the device to acquire spatial information about its surroundings in real time. The data obtained from the sensors is filtered to remove noise, converted into a specific packet format, and sent to the server.

[0464] server

[0465] The server resides in the cloud and receives spatial data transmitted from terminals. This data is analyzed using machine learning algorithms to extract information useful to the user. This analysis applies generative AI models that perform data classification and pattern recognition. Specific examples include guidance on the user's movement within a room and location information of obstacles.

[0466] Feedback system

[0467] Based on the analysis results, the server generates voice feedback using a speech synthesis engine. It can also provide tactile feedback by controlling a vibration motor. This allows users to obtain safety information about their destination without relying on their vision.

[0468] User interaction

[0469] Users can input voice commands into the terminal. For example, if they give the command "Tell me where the table is," the server will analyze it and return specific location information via voice. User feedback and usage history are constantly recorded and stored in the server's history database, which improves the system's accuracy and responsiveness.

[0470] Prompt statements as concrete examples

[0471] "Please implement an audio guidance system that tells you what furniture is in the room and how it's arranged when you enter a new room."

[0472] This system allows users with visual or mobility limitations to more easily overcome physical obstacles in their surroundings, enabling them to live a safe and independent daily life.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The device uses multiple environmental sensors to acquire spatial information about its surroundings. These sensors utilize ultrasound and LiDAR to obtain information about the position and distance of objects. This data is collected in real time and filtered to remove noise. The input is raw data from the sensors, while the output is de-noised and formatted data. In practice, the device accurately measures the positions of walls and furniture in a room within a few seconds.

[0476] Step 2:

[0477] The terminal converts filtered data into a specific packet format and sends it to the server. The input is filtered environmental data, and the output is data packets corresponding to the server. In practice, after the conversion process, the data is quickly sent to a server in the cloud via wireless communication.

[0478] Step 3:

[0479] The server receives data packets sent from the terminal and performs analysis using a generative AI model. The input is data packets from the terminal, and the output is the analysis result based on environmental information. In this analysis, machine learning algorithms are used to extract important features and patterns. Specifically, the server reconstructs the room layout from the received data and identifies important obstacle information.

[0480] Step 4:

[0481] The server generates voice feedback using a speech synthesis engine based on the analysis results and creates control signals for the vibration motor. The input is the analysis results, and the output is voice information and control commands for the vibration pattern. In specific operation, the server generates messages such as "There is a table in the center of the room. Do not approach it."

[0482] Step 5:

[0483] The terminal provides feedback information received from the server to the user. It uses a speech synthesis engine to provide voice guidance and controls a vibration motor to deliver tactile alerts. Input is commands from the server, and output is actual voice and vibration feedback to the user. For example, it might provide a voice announcement such as "The door ahead is closed" and indicate its direction with vibration.

[0484] Step 6:

[0485] Users can operate the terminal and input voice commands. New requests are sent to the server, and the quality and content of the feedback are reviewed. The input is the user's voice command, and the output is a request signal to the server. For example, if the user commands, "Tell me the information for the next room," the system will acquire and analyze further information.

[0486] Step 7:

[0487] The server stores user feedback and command history in a history database. This data is then used for future analysis, improving system responsiveness. The input is user feedback information, and the output is the updated history database. Specifically, the server analyzes the user's action history and updates the model for predicting future actions.

[0488] (Application Example 1)

[0489] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0490] It is generally difficult for visually impaired individuals to search for products and move smoothly within physical stores. Their shopping experience is often limited due to their difficulty in understanding store layouts and product placement. There is a need to address this challenge and enable visually impaired individuals to move safely and effectively within stores and find products.

[0491] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0492] In this invention, the server includes means for providing end users with voice guidance on the location of items based on the installation environment, means for providing voice instructions to assist end users in moving around within the store, and means for predicting the location of items and generating instructions using a generation algorithm. This makes it easier for visually impaired people to understand their location within a physical store and enables them to search for products safely and efficiently.

[0493] "Environmental data" refers to information collected by measurement unit means to acquire physical information about the surrounding environment.

[0494] A "measurement unit means" is a device equipped with sensors for acquiring environmental data, mainly using graphic sensors or laser rangefinders.

[0495] The "processing unit means" refers to a server or computer system that analyzes environmental data received from the measurement unit means and generates information to be provided to the user.

[0496] "End users" refer to the final users of a system, and especially individuals who require assistance, such as those with visual impairments.

[0497] "Voice guidance" refers to voice instructions generated based on analyzed information, informing end users of the location of items and the route they should take.

[0498] "Voice instructions" refer to voice guidance provided to assist end users in navigating within a store.

[0499] A "generative algorithm" is a computational method and processing step used to predict the location of an object based on environmental data and generate instructions regarding that location.

[0500] This invention provides a system for visually impaired individuals to safely navigate a physical store and effectively search for products. The system is comprised of a measurement unit, a processing unit, and means for providing information to the end user.

[0501] The measurement unit is mounted on smart glasses or a mobile device and acquires environmental data using a LiDAR sensor and a camera. This data includes information such as the location and layout of items within a store.

[0502] The server receives environmental data transmitted from the measurement unit on a cloud platform. This process utilizes cloud services such as AWS Lambda or Google Cloud AI, and an AI model using TensorFlow analyzes the environmental data. As a result of the analysis, information necessary for the end user is generated.

[0503] Based on the analysis results, the server generates instructions to provide voice guidance to the user. These voice instructions are transmitted to the end user using speech synthesis software such as Google Text-to-Speech. The generation algorithm also predicts the user's current location and the route to the destination item, providing real-time guidance.

[0504] End users can request specific information from the server using voice commands. For example, if a voice input says, "I want to go to the latest novels section," that information is sent to the server, and appropriate navigation is initiated. The feedback collected during this process is stored on the server and used to improve the accuracy of navigation in the future.

[0505] As a concrete example, if an end-user inputs a prompt message via voice, such as "Please guide me to the candy aisle," the system will provide voice guidance for the shortest route to the desired aisle and also issue warnings about obstacles along the way. In this way, it provides support to help visually impaired individuals move safely and effectively within the store and find the products they are looking for.

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The device acquires ambient environmental data using a LiDAR sensor and camera. This data input consists of the physical structure and location of items within the store, and the output is environmental information in digital format. This information can be acquired in real time, recording the store's layout and the placement of items.

[0509] Step 2:

[0510] The terminal performs initial processing on the acquired environmental data and converts it into a specific packet format. This process compresses the data size and reshapes it into a format suitable for transmission. The output of this packet format is data ready to be sent to the server.

[0511] Step 3:

[0512] The server receives packet-formatted environmental data sent from the terminal. The received data is passed as input to an AI algorithm for analysis. The server analyzes the data using a generative AI model based on TensorFlow and generates instructions about the store layout and the location of items. The output is voice guidance information to be provided to the user.

[0513] Step 4:

[0514] The server converts the generated voice guidance information into audio data using speech synthesis software. The input for this conversion is the voice guidance information, and the output is audio in a format audible to the end user. Speech synthesis is performed using tools such as Google Text-to-Speech.

[0515] Step 5:

[0516] Users navigate the store based on voice guidance. They can also input voice commands into the terminal as needed. An example of input is a prompt such as "Take me to the beverage section." The server's response is a specific instruction to move to the destination.

[0517] Step 6:

[0518] The server receives feedback from users and updates its database based on this feedback. The input consists of data about the user's behavior history and navigation accuracy, while the output is an improved data model used to generate future instructions.

[0519] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0520] This invention is a system that acquires and analyzes environmental information to enable visually impaired and other disabled individuals to live independent lives. This system is characterized by its ability to recognize each user's emotions in real time and provide effective information tailored to those emotions.

[0521] Terminal part

[0522] The device is equipped with various sensors to acquire environmental information. These include a LiDAR sensor to capture the room layout and a microphone to acquire acoustic information. It also features a camera and voice analysis system as an emotion engine to recognize the user's voice tone and facial expressions. The device transmits the acquired data to a server in real time.

[0523] Server portion

[0524] The server receives environmental and emotional data transmitted from the terminal and analyzes both sets of data in an integrated manner. Using an AI algorithm, it determines the user's current emotional state and generates individually optimized voice guidance and vibration feedback. The analysis results are used to select information delivery templates based on the user's emotional state. This template selection changes depending on whether the user is calm or agitated.

[0525] Information provision and feedback

[0526] Users receive environment-related information and emotionally responsive feedback from the device. Speech synthesis is used to provide user-friendly guidance. For example, if a user is experiencing stress, the device will provide guidance in a calm tone, offering gentle encouragement and support.

[0527] By incorporating an emotion engine, the system can address the emotional needs of users that cannot be met by conventional voice guidance systems. This system is expected to significantly improve the quality of life for people with disabilities, as they will receive not only physical but also emotional support.

[0528] Ultimately, user feedback is collected on the server via the device and used for future analysis. It is also incorporated into the entire system as training data, improving the accuracy of user-specific customization.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] The device acquires environmental information and user emotion data. Environmental information includes distance measurement data from a LiDAR sensor, while emotion data is collected from the voice analysis sensor for the user's voice tone and from the camera for facial expression data.

[0532] Step 2:

[0533] The device converts the acquired environmental and emotional data into packets and sends them to the server. During this process, the data type and date / time information are transmitted in an identifiable format.

[0534] Step 3:

[0535] The server receives data from the terminal and begins analyzing environmental and emotional data. An AI algorithm generates a 3D map of the environment, while the emotion engine simultaneously analyzes the user's emotional state.

[0536] Step 4:

[0537] Based on the analysis results, the server generates voice guidance and vibration feedback that corresponds to the user's emotional state. For example, if a stressed state is detected, it selects a calm voice guidance that promotes stabilization.

[0538] Step 5:

[0539] The server generates guidance data and sends it to the terminal. The guidance templates are customized according to the user's emotional state.

[0540] Step 6:

[0541] The terminal receives guidance data from the server and provides information to the user using speech synthesis and vibration signals. The voice is played back at a speed and tone that is easy for the user to hear.

[0542] Step 7:

[0543] Users provide feedback, which is then sent to the server via their devices. This feedback is provided via voice or button input and is used to improve the accuracy of the analysis.

[0544] Step 8:

[0545] The server analyzes the feedback and updates the system's database. This update improves the accuracy and customization of future guidance.

[0546] (Example 2)

[0547] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0548] For people with disabilities, such as those with visual impairments, to live independently, they need to properly understand environmental information and receive appropriate emotional support. However, while conventional assistive devices can provide physical support, they lack the ability to provide appropriate feedback tailored to the user's emotional state.

[0549] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0550] In this invention, the server includes an information acquisition device means equipped with sensors for acquiring environmental information and emotional data; an information provision means for providing the user with audio and tactile information based on the analysis results; and a data storage device means for receiving feedback from the user and updating the learning data. This makes it possible to provide optimal support in real time according to the user's emotional state and to meet the user's emotional needs.

[0551] An "information acquisition device means" is a device that uses sensors to acquire environmental information and emotional data from a user.

[0552] A "data processing device" is a device that receives environmental information and emotional data acquired by an information acquisition device and analyzes it.

[0553] An "information provision means" is a device that provides users with necessary information through sound or touch based on the analyzed results.

[0554] A "data storage device" is a device that receives feedback from users, stores it as learning data, and updates it.

[0555] "Environmental information" refers to information that indicates the physical state and conditions around the user, and is data acquired through sensors.

[0556] "Emotional data" refers to data that analyzes the tone of a user's voice and facial expressions to indicate their emotional state.

[0557] A "server" is a central system that receives and analyzes data from multiple devices and provides feedback as needed.

[0558] This invention relates to a support system that uses environmental information and emotional state analysis to enable visually impaired and other disabled individuals to live independent lives.

[0559] The device is equipped with various sensors and plays a role in collecting environmental information and emotional data. Specifically, a LiDAR sensor captures the room layout and arrangement, and a microphone acquires ambient acoustic information. In addition, a camera and voice analysis system monitor the user's facial expressions and voice tone to collect emotional data. This data is transmitted to a server in real time.

[0560] The server integrates received environmental information and emotional data and performs analysis using an AI algorithm. Based on the analysis, the server determines the user's emotional state and generates voice guidance and vibration feedback optimized for that state. For example, if the user is feeling stressed, the server generates voice feedback in a calm tone and sends it to the device.

[0561] Users receive audio and haptic feedback from the device to understand information related to their environment. This feedback is provided using speech synthesis technology, ensuring it is presented in a user-friendly format. For example, if the user is detected as being under high stress, a message such as "You're doing well. Let's take a deep breath" might be provided.

[0562] Furthermore, the server continuously collects user feedback from terminals and uses it as training data. This makes it possible to improve the overall feedback accuracy of the system using a generative AI model.

[0563] An example of a prompt would be, "If the AI ​​determines that the user is currently experiencing high stress, what kind of voice feedback should be provided?" This serves as a specific guideline for the AI ​​to provide the user with the most appropriate guidance.

[0564] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0565] Step 1:

[0566] The device is responsible for collecting environmental information and emotional data. It uses a LiDAR sensor to acquire room layout data and a microphone to collect acoustic data. It also captures the user's facial expressions with a camera and analyzes their voice tone with a voice analysis system. The data from these sensors becomes input and is sent to the server as output from the device in a structured format.

[0567] Step 2:

[0568] The server receives environmental information and emotional data transmitted from the terminal and stores it in a database. Using the received data as input, an AI algorithm performs an integrated analysis. This data analysis evaluates the emotional state based on the user's facial expressions and tone of voice. Based on this analysis, the user's emotional state (e.g., relaxed, stressed) is output.

[0569] Step 3:

[0570] The server uses the results of analysis by an AI algorithm to generate voice guidance and vibration feedback optimized for the user's current emotional state. In this process, the analysis results are treated as input, and based on this, the optimal feedback content is selected from a template, and feedback data is constructed as output.

[0571] Step 4:

[0572] The terminal receives feedback data sent from the server and uses speech synthesis to provide voice guidance to the user. Specifically, the voice guidance system synthesizes a message and plays it through the speaker. It also activates a vibration motor as needed to provide haptic feedback. As a result, the user receives both the generated voice and haptic feedback.

[0573] Step 5:

[0574] The user responds to the provided feedback, and this feedback is collected by the device. The input here is the user's response data, which is output to the server via the device and stored as accumulated data. This data is used with a generative AI model to improve the accuracy of future feedback.

[0575] (Application Example 2)

[0576] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0577] In autonomous vehicles, it is crucial that passengers feel safe and secure during their journey. However, current technology does not adequately provide systems that adjust their behavior in response to passenger emotions. Therefore, there is a need to alleviate passenger anxiety and provide a comfortable travel experience.

[0578] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0579] In this invention, the server includes means for analyzing environmental information collected from a computing device and adjusting the operating style within the vehicle based on the passenger's emotional state, means for determining the passenger's emotions in real time and providing a sense of security, and means for collecting feedback from users and improving the entire system. This enables passengers to use autonomous vehicles with peace of mind and enjoy a comfortable travel experience.

[0580] A "detector" is a sensor device used to acquire environmental information and transmit this information to a computing device.

[0581] A "computation device" is an electronic device that acquires environmental information from detectors and transmits it to a computing server.

[0582] A "computation server" is a server that performs analysis based on information transmitted from a computing device and has the function of determining the emotional state of a user.

[0583] "Adjusting the operation plan" is the process of changing how a mobile device or system operates based on the user's emotional state.

[0584] "Feedback" refers to the opinions and reactions that users provide after using a system, and is information that is used to update and improve the system.

[0585] The system realizing this invention first uses LIDAR sensors, microphones, and cameras to acquire all environmental information in real time using a computing device. These devices can collect physical information, acoustic information, and facial expression information from the surroundings. The information transmitted from the computing device is transferred to a computing server. The server uses advanced AI algorithms to analyze the emotional state of the passengers. As specific AI algorithms, TensorFlow and Keras are used for emotion analysis, and Google Cloud Speech-to-Text is used for speech analysis.

[0586] The server adjusts the operation plan based on the analysis results. If the passenger is experiencing stress, it issues instructions to change the vehicle's driving style. If reassurance is needed, it uses a speech synthesis system to provide voice announcements such as "The vehicle is being driven safely." Speech synthesis technologies such as Amazon Polly can be applied to the backend. Feedback from the passenger is fed back to the computing server and used for system learning and improvement.

[0587] For example, if a passenger feels uneasy on a sharp curve, the server will instruct the vehicle to adjust its speed in real time and provide a smooth voice announcement saying, "We are driving safely in this section." It can also set prompts such as, "Use a generative AI model to create an optimal driving plan based on the passenger's emotional state."

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The device uses a LiDAR sensor, microphone, and camera to acquire physical, acoustic, and facial information about its surroundings in order to collect environmental information. In this step, data from each sensor is acquired in real time and prepared for processing. The input is environmental data from the sensors, and the output is initial data from the various sensors.

[0591] Step 2:

[0592] The terminal compresses the acquired environmental data and transmits it to the computing server via wireless communication. This step converts the data into a format suitable for data transfer, streamlining processing on the server side. The input is sensor information, and the output is the data to be transmitted to the server.

[0593] Step 3:

[0594] The server analyzes the received environmental data using an AI algorithm. This analysis includes a process that uses TensorFlow and Keras to determine the emotional state. The input is environmental data from the terminal, and the output is the analyzed emotional state of the passenger.

[0595] Step 4:

[0596] The server sends instructions to the vehicle to adjust its operation plan based on the analysis results. For example, if the server determines that the passenger is anxious, it will issue an instruction to adjust the vehicle's speed. The input is the result of the emotion analysis, and the output is a control instruction to the vehicle.

[0597] Step 5:

[0598] The terminal uses a speech synthesis system to provide voice guidance to users based on instructions from the server. Here, technologies such as Amazon Polly are used to generate voice messages in real time, providing passengers with a sense of security. Input is voice instructions from the server, and output is the generated voice guidance.

[0599] Step 6:

[0600] Users send feedback on their travel experiences to the server via their device, which is then used for data analysis in subsequent visits. This feedback information is also used as training data for the generated AI model, contributing to improvements in the services provided. The input is user feedback information, and the output is training data stored on the server.

[0601] 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.

[0602] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0603] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] 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.

[0607] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0608] 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.

[0609] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0610] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0611] 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.

[0612] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0613] 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.

[0614] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0615] The 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.

[0616] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0617] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0618] This invention provides a system that enables people with disabilities, particularly those with visual impairments, to live more independent lives. The system's main components are a terminal equipped with sensors and a server that performs data analysis.

[0619] Terminal part

[0620] The device is equipped with multiple sensors to acquire information about the surrounding environment in real time. This includes various environmental sensors such as ultrasound and LiDAR to acquire information about the room layout and the placement of objects. The collected data is initially processed, converted into a specific packet format, and sent to the server.

[0621] Server portion

[0622] The server maintains a database of environmental information in the cloud and performs data analysis based on this database. By analyzing the received environmental data using AI algorithms, it generates the information the user needs and constructs customized feedback. The generated feedback is sent to the terminal as commands to control speech synthesis and vibration motors.

[0623] Providing feedback to users

[0624] The user carries a device and receives information transmitted from the server through it. For example, when the user enters a room, the device provides voice guidance about the surrounding layout. This might include messages such as, "There is a table in the center of the room. Please be careful not to step over it." Furthermore, if an object is difficult to maneuver, the device can communicate the appropriate steps to the user through vibration patterns.

[0625] In actual use cases, the system also includes a function that allows users to input voice commands using a terminal. For example, if a user issues the voice command "Tell me the location of the table," the terminal sends a request to the server, and the server responds with the table's location information via voice as a result of its analysis. The feedback obtained during this process is sent to the server and used for future analysis and suggestions.

[0626] This system enables visually impaired individuals to overcome geographical and physical barriers and live their daily lives more smoothly. Furthermore, the program can be expanded to provide similar support to people with other disabilities.

[0627] The following describes the processing flow.

[0628] Step 1:

[0629] The device scans the surrounding environment and acquires sensor data. The sensors include ultrasonic sensors, LiDAR, and microphones, and this data is used to generate a 3D map of the environment.

[0630] Step 2:

[0631] The terminal converts the initial data into packet format and sends it to the server. At this time, a timestamp is added to the data so that it can be used as time-series data for later analysis.

[0632] Step 3:

[0633] The server receives environmental data and stores it in a database. Simultaneously, an AI algorithm analyzes the data and extracts the environmental information requested by the user.

[0634] Step 4:

[0635] The server generates commands to provide voice guidance or vibration feedback based on the analysis results and sends them to the terminal.

[0636] Step 5:

[0637] The terminal receives commands from the server and transmits guidance messages via speech synthesis and vibration signals to the user.

[0638] Step 6:

[0639] The system acts based on the information received by the user and inputs voice commands into the terminal as needed.

[0640] Step 7:

[0641] The terminal analyzes the user's voice commands and sends them to the server. It also receives user feedback in real time and sends it to the server.

[0642] Step 8:

[0643] The server analyzes the feedback and updates the database. This feedback is then incorporated into the AI ​​algorithm as training data, helping to improve the accuracy of future analyses.

[0644] (Example 1)

[0645] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] There is a need to provide support to users with visual or mobility limitations so that they can move safely and comfortably in their daily lives and understand their surroundings. However, conventional technologies have limitations in acquiring environmental information and providing feedback to users, and have been unable to adequately meet the individual needs of users.

[0647] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0648] In this invention, the server includes a device means equipped with multiple environmental sensors for acquiring spatial information of the surroundings; an information processing device means for receiving spatial data transmitted from the device means and analyzing it using a machine learning algorithm; a means for controlling a speech synthesis engine and a vibration motor based on the analysis results to provide information to the user; and a means for receiving commands and feedback from the user and updating a history database. This enables users with visual or movement limitations to move safely and understand their surrounding physical situation through real-time, customized feedback.

[0649] "Device means" refers to a module equipped with multiple environmental sensors for acquiring surrounding spatial information and an initial filtering function for processing the data obtained from these sensors.

[0650] "Information processing device means" refers to a system that receives spatial data transmitted from a device means and performs data analysis using machine learning algorithms.

[0651] An "environmental sensor" refers to a measuring element that uses ultrasound, LIDAR, or other technologies to acquire information about surrounding objects and space in real time.

[0652] A "machine learning algorithm" refers to a computer program that analyzes received data and performs pattern recognition or data classification.

[0653] A "speech synthesis engine" refers to software equipped with processing functions to convert text data into speech and provide it to the user as auditory information.

[0654] A "vibration motor" refers to a device that provides tactile feedback to the user by generating physical vibrations.

[0655] A "history database" refers to an information recording system that stores user feedback and command history for use in future analysis and user profile construction.

[0656] This invention is a support system designed to enable visually impaired individuals and users with limited mobility to live their daily lives more independently. The system primarily consists of a terminal equipped with multiple environmental sensors and a server for data analysis.

[0657] terminal

[0658] The device is equipped with environmental sensors such as ultrasonic sensors and LiDAR sensors. This allows the device to acquire spatial information about its surroundings in real time. The data obtained from the sensors is filtered to remove noise, converted into a specific packet format, and sent to the server.

[0659] server

[0660] The server resides in the cloud and receives spatial data transmitted from terminals. This data is analyzed using machine learning algorithms to extract information useful to the user. This analysis applies generative AI models that perform data classification and pattern recognition. Specific examples include guidance on the user's movement within a room and location information of obstacles.

[0661] Feedback system

[0662] Based on the analysis results, the server generates voice feedback using a speech synthesis engine. It can also provide tactile feedback by controlling a vibration motor. This allows users to obtain safety information about their destination without relying on their vision.

[0663] User interaction

[0664] Users can input voice commands into the terminal. For example, if they give the command "Tell me where the table is," the server will analyze it and return specific location information via voice. User feedback and usage history are constantly recorded and stored in the server's history database, which improves the system's accuracy and responsiveness.

[0665] Prompt statements as concrete examples

[0666] "Please implement an audio guidance system that tells you what furniture is in the room and how it's arranged when you enter a new room."

[0667] This system allows users with visual or mobility limitations to more easily overcome physical obstacles in their surroundings, enabling them to live a safe and independent daily life.

[0668] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0669] Step 1:

[0670] The device uses multiple environmental sensors to acquire spatial information about its surroundings. These sensors utilize ultrasound and LiDAR to obtain information about the position and distance of objects. This data is collected in real time and filtered to remove noise. The input is raw data from the sensors, while the output is de-noised and formatted data. In practice, the device accurately measures the positions of walls and furniture in a room within a few seconds.

[0671] Step 2:

[0672] The terminal converts filtered data into a specific packet format and sends it to the server. The input is filtered environmental data, and the output is data packets corresponding to the server. In practice, after the conversion process, the data is quickly sent to a server in the cloud via wireless communication.

[0673] Step 3:

[0674] The server receives data packets sent from the terminal and performs analysis using a generative AI model. The input is data packets from the terminal, and the output is the analysis result based on environmental information. In this analysis, machine learning algorithms are used to extract important features and patterns. Specifically, the server reconstructs the room layout from the received data and identifies important obstacle information.

[0675] Step 4:

[0676] The server generates voice feedback using a speech synthesis engine based on the analysis results and creates control signals for the vibration motor. The input is the analysis results, and the output is voice information and control commands for the vibration pattern. In specific operation, the server generates messages such as "There is a table in the center of the room. Do not approach it."

[0677] Step 5:

[0678] The terminal provides feedback information received from the server to the user. It uses a speech synthesis engine to provide voice guidance and controls a vibration motor to deliver tactile alerts. Input is commands from the server, and output is actual voice and vibration feedback to the user. For example, it might provide a voice announcement such as "The door ahead is closed" and indicate its direction with vibration.

[0679] Step 6:

[0680] Users can operate the terminal and input voice commands. New requests are sent to the server, and the quality and content of the feedback are reviewed. The input is the user's voice command, and the output is a request signal to the server. For example, if the user commands, "Tell me the information for the next room," the system will acquire and analyze further information.

[0681] Step 7:

[0682] The server stores user feedback and command history in a history database. This data is then used for future analysis, improving system responsiveness. The input is user feedback information, and the output is the updated history database. Specifically, the server analyzes the user's action history and updates the model for predicting future actions.

[0683] (Application Example 1)

[0684] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] It is generally difficult for visually impaired individuals to search for products and move smoothly within physical stores. Their shopping experience is often limited due to their difficulty in understanding store layouts and product placement. There is a need to address this challenge and enable visually impaired individuals to move safely and effectively within stores and find products.

[0686] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0687] In this invention, the server includes means for providing end users with voice guidance on the location of items based on the installation environment, means for providing voice instructions to assist end users in moving around within the store, and means for predicting the location of items and generating instructions using a generation algorithm. This makes it easier for visually impaired people to understand their location within a physical store and enables them to search for products safely and efficiently.

[0688] "Environmental data" refers to information collected by measurement unit means to acquire physical information about the surrounding environment.

[0689] A "measurement unit means" is a device equipped with sensors for acquiring environmental data, mainly using graphic sensors or laser rangefinders.

[0690] The "processing unit means" refers to a server or computer system that analyzes environmental data received from the measurement unit means and generates information to be provided to the user.

[0691] "End users" refer to the final users of a system, and especially individuals who require assistance, such as those with visual impairments.

[0692] "Voice guidance" refers to voice instructions generated based on analyzed information, informing end users of the location of items and the route they should take.

[0693] "Voice instructions" refer to voice guidance provided to assist end users in navigating within a store.

[0694] A "generative algorithm" is a computational method and processing step used to predict the location of an object based on environmental data and generate instructions regarding that location.

[0695] This invention provides a system for visually impaired individuals to safely navigate a physical store and effectively search for products. The system is comprised of a measurement unit, a processing unit, and means for providing information to the end user.

[0696] The measurement unit is mounted on smart glasses or a mobile device and acquires environmental data using a LiDAR sensor and a camera. This data includes information such as the location and layout of items within a store.

[0697] The server receives environmental data transmitted from the measurement unit on a cloud platform. This process utilizes cloud services such as AWS Lambda or Google Cloud AI, and an AI model using TensorFlow analyzes the environmental data. As a result of the analysis, information necessary for the end user is generated.

[0698] Based on the analysis results, the server generates instructions to provide voice guidance to the user. These voice instructions are transmitted to the end user using speech synthesis software such as Google Text-to-Speech. The generation algorithm also predicts the user's current location and the route to the destination item, providing real-time guidance.

[0699] End users can request specific information from the server using voice commands. For example, if a voice input says, "I want to go to the latest novels section," that information is sent to the server, and appropriate navigation is initiated. The feedback collected during this process is stored on the server and used to improve the accuracy of navigation in the future.

[0700] As a concrete example, if an end-user inputs a prompt message via voice, such as "Please guide me to the candy aisle," the system will provide voice guidance for the shortest route to the desired aisle and also issue warnings about obstacles along the way. In this way, it provides support to help visually impaired individuals move safely and effectively within the store and find the products they are looking for.

[0701] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0702] Step 1:

[0703] The device acquires ambient environmental data using a LiDAR sensor and camera. This data input consists of the physical structure and location of items within the store, and the output is environmental information in digital format. This information can be acquired in real time, recording the store's layout and the placement of items.

[0704] Step 2:

[0705] The terminal performs initial processing on the acquired environmental data and converts it into a specific packet format. This process compresses the data size and reshapes it into a format suitable for transmission. The output of this packet format is data ready to be sent to the server.

[0706] Step 3:

[0707] The server receives packet-formatted environmental data sent from the terminal. The received data is passed as input to an AI algorithm for analysis. The server analyzes the data using a generative AI model based on TensorFlow and generates instructions about the store layout and the location of items. The output is voice guidance information to be provided to the user.

[0708] Step 4:

[0709] The server converts the generated voice guidance information into audio data using speech synthesis software. The input for this conversion is the voice guidance information, and the output is audio in a format audible to the end user. Speech synthesis is performed using tools such as Google Text-to-Speech.

[0710] Step 5:

[0711] Users navigate the store based on voice guidance. They can also input voice commands into the terminal as needed. An example of input is a prompt such as "Take me to the beverage section." The server's response is a specific instruction to move to the destination.

[0712] Step 6:

[0713] The server receives feedback from users and updates its database based on this feedback. The input consists of data about the user's behavior history and navigation accuracy, while the output is an improved data model used to generate future instructions.

[0714] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0715] This invention is a system that acquires and analyzes environmental information to enable visually impaired and other disabled individuals to live independent lives. This system is characterized by its ability to recognize each user's emotions in real time and provide effective information tailored to those emotions.

[0716] Terminal part

[0717] The device is equipped with various sensors to acquire environmental information. These include a LiDAR sensor to capture the room layout and a microphone to acquire acoustic information. It also features a camera and voice analysis system as an emotion engine to recognize the user's voice tone and facial expressions. The device transmits the acquired data to a server in real time.

[0718] Server portion

[0719] The server receives environmental and emotional data transmitted from the terminal and analyzes both sets of data in an integrated manner. Using an AI algorithm, it determines the user's current emotional state and generates individually optimized voice guidance and vibration feedback. The analysis results are used to select information delivery templates based on the user's emotional state. This template selection changes depending on whether the user is calm or agitated.

[0720] Information provision and feedback

[0721] Users receive environment-related information and emotionally responsive feedback from the device. Speech synthesis is used to provide user-friendly guidance. For example, if a user is experiencing stress, the device will provide guidance in a calm tone, offering gentle encouragement and support.

[0722] By incorporating an emotion engine, the system can address the emotional needs of users that cannot be met by conventional voice guidance systems. This system is expected to significantly improve the quality of life for people with disabilities, as they will receive not only physical but also emotional support.

[0723] Ultimately, user feedback is collected on the server via the device and used for future analysis. It is also incorporated into the entire system as training data, improving the accuracy of user-specific customization.

[0724] The following describes the processing flow.

[0725] Step 1:

[0726] The device acquires environmental information and user emotion data. Environmental information includes distance measurement data from a LiDAR sensor, while emotion data is collected from the voice analysis sensor for the user's voice tone and from the camera for facial expression data.

[0727] Step 2:

[0728] The device converts the acquired environmental and emotional data into packets and sends them to the server. During this process, the data type and date / time information are transmitted in an identifiable format.

[0729] Step 3:

[0730] The server receives data from the terminal and begins analyzing environmental and emotional data. An AI algorithm generates a 3D map of the environment, while the emotion engine simultaneously analyzes the user's emotional state.

[0731] Step 4:

[0732] Based on the analysis results, the server generates voice guidance and vibration feedback that corresponds to the user's emotional state. For example, if a stressed state is detected, it selects a calm voice guidance that promotes stabilization.

[0733] Step 5:

[0734] The server generates guidance data and sends it to the terminal. The guidance templates are customized according to the user's emotional state.

[0735] Step 6:

[0736] The terminal receives guidance data from the server and provides information to the user using speech synthesis and vibration signals. The voice is played back at a speed and tone that is easy for the user to hear.

[0737] Step 7:

[0738] Users provide feedback, which is then sent to the server via their devices. This feedback is provided via voice or button input and is used to improve the accuracy of the analysis.

[0739] Step 8:

[0740] The server analyzes the feedback and updates the system's database. This update improves the accuracy and customization of future guidance.

[0741] (Example 2)

[0742] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] For people with disabilities, such as those with visual impairments, to live independently, they need to properly understand environmental information and receive appropriate emotional support. However, while conventional assistive devices can provide physical support, they lack the ability to provide appropriate feedback tailored to the user's emotional state.

[0744] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0745] In this invention, the server includes an information acquisition device means equipped with sensors for acquiring environmental information and emotional data; an information provision means for providing the user with audio and tactile information based on the analysis results; and a data storage device means for receiving feedback from the user and updating the learning data. This makes it possible to provide optimal support in real time according to the user's emotional state and to meet the user's emotional needs.

[0746] An "information acquisition device means" is a device that uses sensors to acquire environmental information and emotional data from a user.

[0747] A "data processing device" is a device that receives environmental information and emotional data acquired by an information acquisition device and analyzes it.

[0748] An "information provision means" is a device that provides users with necessary information through sound or touch based on the analyzed results.

[0749] A "data storage device" is a device that receives feedback from users, stores it as learning data, and updates it.

[0750] "Environmental information" refers to information that indicates the physical state and conditions around the user, and is data acquired through sensors.

[0751] "Emotional data" refers to data that analyzes the tone of a user's voice and facial expressions to indicate their emotional state.

[0752] A "server" is a central system that receives and analyzes data from multiple devices and provides feedback as needed.

[0753] This invention relates to a support system that uses environmental information and emotional state analysis to enable visually impaired and other disabled individuals to live independent lives.

[0754] The device is equipped with various sensors and plays a role in collecting environmental information and emotional data. Specifically, a LiDAR sensor captures the room layout and arrangement, and a microphone acquires ambient acoustic information. In addition, a camera and voice analysis system monitor the user's facial expressions and voice tone to collect emotional data. This data is transmitted to a server in real time.

[0755] The server integrates received environmental information and emotional data and performs analysis using an AI algorithm. Based on the analysis, the server determines the user's emotional state and generates voice guidance and vibration feedback optimized for that state. For example, if the user is feeling stressed, the server generates voice feedback in a calm tone and sends it to the device.

[0756] Users receive audio and haptic feedback from the device to understand information related to their environment. This feedback is provided using speech synthesis technology, ensuring it is presented in a user-friendly format. For example, if the user is detected as being under high stress, a message such as "You're doing well. Let's take a deep breath" might be provided.

[0757] Furthermore, the server continuously collects user feedback from terminals and uses it as training data. This makes it possible to improve the overall feedback accuracy of the system using a generative AI model.

[0758] An example of a prompt would be, "If the AI ​​determines that the user is currently experiencing high stress, what kind of voice feedback should be provided?" This serves as a specific guideline for the AI ​​to provide the user with the most appropriate guidance.

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] The device is responsible for collecting environmental information and emotional data. It uses a LiDAR sensor to acquire room layout data and a microphone to collect acoustic data. It also captures the user's facial expressions with a camera and analyzes their voice tone with a voice analysis system. The data from these sensors becomes input and is sent to the server as output from the device in a structured format.

[0762] Step 2:

[0763] The server receives environmental information and emotional data transmitted from the terminal and stores it in a database. Using the received data as input, an AI algorithm performs an integrated analysis. This data analysis evaluates the emotional state based on the user's facial expressions and tone of voice. Based on this analysis, the user's emotional state (e.g., relaxed, stressed) is output.

[0764] Step 3:

[0765] The server uses the results of analysis by an AI algorithm to generate voice guidance and vibration feedback optimized for the user's current emotional state. In this process, the analysis results are treated as input, and based on this, the optimal feedback content is selected from a template, and feedback data is constructed as output.

[0766] Step 4:

[0767] The terminal receives feedback data sent from the server and uses speech synthesis to provide voice guidance to the user. Specifically, the voice guidance system synthesizes a message and plays it through the speaker. It also activates a vibration motor as needed to provide haptic feedback. As a result, the user receives both the generated voice and haptic feedback.

[0768] Step 5:

[0769] The user responds to the provided feedback, and this feedback is collected by the device. The input here is the user's response data, which is output to the server via the device and stored as accumulated data. This data is used with a generative AI model to improve the accuracy of future feedback.

[0770] (Application Example 2)

[0771] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0772] In autonomous vehicles, it is crucial that passengers feel safe and secure during their journey. However, current technology does not adequately provide systems that adjust their behavior in response to passenger emotions. Therefore, there is a need to alleviate passenger anxiety and provide a comfortable travel experience.

[0773] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0774] In this invention, the server includes means for analyzing environmental information collected from a computing device and adjusting the operating style within the vehicle based on the passenger's emotional state, means for determining the passenger's emotions in real time and providing a sense of security, and means for collecting feedback from users and improving the entire system. This enables passengers to use autonomous vehicles with peace of mind and enjoy a comfortable travel experience.

[0775] A "detector" is a sensor device used to acquire environmental information and transmit this information to a computing device.

[0776] A "computation device" is an electronic device that acquires environmental information from detectors and transmits it to a computing server.

[0777] A "computation server" is a server that performs analysis based on information transmitted from a computing device and has the function of determining the emotional state of a user.

[0778] "Adjusting the operation plan" is the process of changing how a mobile device or system operates based on the user's emotional state.

[0779] "Feedback" refers to the opinions and reactions that users provide after using a system, and is information that is used to update and improve the system.

[0780] The system realizing this invention first uses LIDAR sensors, microphones, and cameras to acquire all environmental information in real time using a computing device. These devices can collect physical information, acoustic information, and facial expression information from the surroundings. The information transmitted from the computing device is transferred to a computing server. The server uses advanced AI algorithms to analyze the emotional state of the passengers. As specific AI algorithms, TensorFlow and Keras are used for emotion analysis, and Google Cloud Speech-to-Text is used for speech analysis.

[0781] The server adjusts the operation plan based on the analysis results. If the passenger is experiencing stress, it issues instructions to change the vehicle's driving style. If reassurance is needed, it uses a speech synthesis system to provide voice announcements such as "The vehicle is being driven safely." Speech synthesis technologies such as Amazon Polly can be applied to the backend. Feedback from the passenger is fed back to the computing server and used for system learning and improvement.

[0782] For example, if a passenger feels uneasy on a sharp curve, the server will instruct the vehicle to adjust its speed in real time and provide a smooth voice announcement saying, "We are driving safely in this section." It can also set prompts such as, "Use a generative AI model to create an optimal driving plan based on the passenger's emotional state."

[0783] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0784] Step 1:

[0785] The device uses a LiDAR sensor, microphone, and camera to acquire physical, acoustic, and facial information about its surroundings in order to collect environmental information. In this step, data from each sensor is acquired in real time and prepared for processing. The input is environmental data from the sensors, and the output is initial data from the various sensors.

[0786] Step 2:

[0787] The terminal compresses the acquired environmental data and transmits it to the computing server via wireless communication. This step converts the data into a format suitable for data transfer, streamlining processing on the server side. The input is sensor information, and the output is the data to be transmitted to the server.

[0788] Step 3:

[0789] The server analyzes the received environmental data using an AI algorithm. This analysis includes a process that uses TensorFlow and Keras to determine the emotional state. The input is environmental data from the terminal, and the output is the analyzed emotional state of the passenger.

[0790] Step 4:

[0791] The server sends instructions to the vehicle to adjust its operation plan based on the analysis results. For example, if the server determines that the passenger is anxious, it will issue an instruction to adjust the vehicle's speed. The input is the result of the emotion analysis, and the output is a control instruction to the vehicle.

[0792] Step 5:

[0793] The terminal uses a speech synthesis system to provide voice guidance to users based on instructions from the server. Here, technologies such as Amazon Polly are used to generate voice messages in real time, providing passengers with a sense of security. Input is voice instructions from the server, and output is the generated voice guidance.

[0794] Step 6:

[0795] Users send feedback on their travel experiences to the server via their device, which is then used for data analysis in subsequent visits. This feedback information is also used as training data for the generated AI model, contributing to improvements in the services provided. The input is user feedback information, and the output is training data stored on the server.

[0796] 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.

[0797] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0798] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0799] 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.

[0800] Figure 9 shows an 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.

[0801] 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.

[0802] 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.

[0803] 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, motorcycles, etc., 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, for example, based 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.

[0804] 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."

[0805] 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.

[0806] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0807] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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 the like 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.

[0816] 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.

[0817] The following is further disclosed regarding the embodiments described above.

[0818] (Claim 1)

[0819] A terminal means equipped with a sensor for acquiring environmental information,

[0820] A server means for receiving and analyzing environmental information transmitted from the terminal means,

[0821] A means for providing information to the user through voice and vibration based on the analysis results,

[0822] A means of receiving user feedback and updating accumulated data,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, characterized by comprising means for proposing the optimal arrangement of articles based on acquired environmental information.

[0826] (Claim 3)

[0827] The system according to claim 1, characterized by comprising means for analyzing the user's behavior history and providing information based on predictions.

[0828] "Example 1"

[0829] (Claim 1)

[0830] A device equipped with multiple environmental sensors for acquiring surrounding spatial information,

[0831] Information processing device means for receiving spatial data transmitted from the aforementioned device means and analyzing it using a machine learning algorithm,

[0832] Based on the analysis results, a means for controlling a speech synthesis engine and a vibration motor to provide information to the user,

[0833] A means of receiving commands and feedback from users and updating the history database,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, characterized by comprising means for generating dynamic arrangement information based on received spatial data and for suggesting recommended arrangements of furniture and items.

[0837] (Claim 3)

[0838] The system according to claim 1, characterized by comprising means for analyzing recorded behavioral patterns within the device and providing information to support the next movement based on estimations.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] A measurement unit means for acquiring environmental data,

[0842] A processing unit means for receiving and analyzing environmental data transmitted from the aforementioned measurement unit means,

[0843] A means of providing end-users with audio and haptic information based on the analysis results,

[0844] A means of receiving responses from end users and updating the stored database,

[0845] A means of providing end users with voice guidance on the location of items based on the installation environment,

[0846] A means of providing voice instructions to assist end users in moving around within a store,

[0847] A means for predicting the position of an item using a generation algorithm and generating instructions,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, comprising means for proposing a method of arranging articles based on acquired environmental data.

[0851] (Claim 3)

[0852] The system according to claim 1, comprising means for analyzing the end user's movement history and providing guidance based on predictions.

[0853] "Example 2 of combining an emotion engine"

[0854] (Claim 1)

[0855] An information acquisition device means equipped with sensors for acquiring environmental information and emotional data,

[0856] A data processing device means for receiving and analyzing environmental information and emotion data transmitted from the aforementioned information acquisition device means,

[0857] Information provision means that provides the user with audio and haptic information based on the analysis results,

[0858] A data storage device means for receiving user feedback and updating learning data,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, characterized by comprising means for generating optimal voice guidance or vibration feedback for the user based on acquired environmental information and analyzed emotional state.

[0862] (Claim 3)

[0863] The system according to claim 1, characterized in that it provides feedback in accordance with the user's emotional state, and further comprises means for optimizing this feedback to satisfy the user's emotional needs.

[0864] "Application example 2 when combining with an emotional engine"

[0865] (Claim 1)

[0866] A computing device equipped with a detector for acquiring environmental information,

[0867] A computing server means for receiving and analyzing environmental information transmitted from the aforementioned computing device means,

[0868] A means for determining the user's emotional state based on the analysis results and adjusting the action plan to provide a sense of security,

[0869] A means of receiving feedback from users and updating accumulated information,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, characterized by comprising means for adjusting the operating style within the mobile body based on acquired environmental information and emotional information.

[0873] (Claim 3)

[0874] The system according to claim 1, characterized by comprising means for providing information in real time based on the emotional state of the user. [Explanation of Symbols]

[0875] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A terminal means equipped with a sensor for acquiring environmental information, A server means for receiving and analyzing environmental information transmitted from the terminal means, A means for providing information to the user through voice and vibration based on the analysis results, A means of receiving user feedback and updating accumulated data, A system that includes this.

2. The system according to claim 1, characterized by comprising means for proposing the optimal arrangement of articles based on acquired environmental information.

3. The system according to claim 1, characterized by comprising means for analyzing the user's behavior history and providing information based on predictions.

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