System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring a pedaling force of a user from a pedal force sensor; means for acquiring a current speed from a speed sensor; means for acquiring current position information from a position information sensor; means for analyzing the acquired pedaling force, speed, and position information to identify a current running state; means for controlling an output of an electric motor based on the running state; means for transmitting an instruction to control the motor output to an electric motor control unit; and means for notifying the user of information related to the output control of the electric motor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional electric bicycles have short battery life and are difficult to manage efficiently. When driving long distances or climbing hills, the battery quickly drains, making driving difficult for users. Furthermore, there is a lack of means to properly notify users of the remaining battery power or the optimal route. This makes it difficult for users to use their electric bicycles efficiently, resulting in inconvenience. The present invention aims to solve these problems by extending battery life and providing a comfortable ride. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring a user's pedaling force from a pedal force sensor, a means for acquiring current speed from a speed sensor, and a means for acquiring current location information from a location information sensor. It also provides a means for analyzing the acquired data and identifying the current riding state. It also includes a means for determining an appropriate motor output based on the analysis results and sending that instruction to an electric motor control unit. The system also includes a means for notifying the user of the current remaining battery power, estimated remaining distance, and the nearest charging station, thereby improving the user's driving experience. This allows for efficient battery management and a comfortable ride.

[0006] A "pedal force sensor" is a sensor that measures the force with which a user pedals.

[0007] A "speed sensor" is a sensor that measures the current speed of an electric bicycle.

[0008] A "location information sensor" is a sensor that measures the current location of an electric bicycle, and usually uses GPS.

[0009] "Data" refers to information obtained from sensors such as pedal force, speed, and position information.

[0010] "Analysis" is the process of identifying the current driving conditions based on the acquired data.

[0011] "Riding status" is information that indicates the current conditions under which the electric bicycle is traveling (flat road, uphill, downhill, etc.).

[0012] "Motor power" refers to the force an electric motor generates, usually measured in watts (W).

[0013] An "electric motor control unit" is a device for controlling the output of a motor.

[0014] "User notification" is the process by which a system provides information to a user (such as remaining battery life, optimal route, current situation, etc.).

[0015] "Remaining battery capacity" refers to the amount of power currently remaining in the electric bicycle's battery.

[0016] "Estimated remaining distance" refers to the estimated distance that can be traveled with the current remaining battery charge.

[0017] A "charging station" is a facility for charging batteries. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] To implement the present invention, a system is required in which each sensor on the electric bicycle acquires data, and the server analyzes and optimizes that data. A specific implementation method for this system is described below.

[0040] System Overview

[0041] The system consists of a pedal force sensor, a speed sensor, a location information sensor (GPS), a server, and a terminal (the control unit of the electric bicycle). These components work together to collect data in real time and optimize motor output according to the user's riding conditions.

[0042] Program processing

[0043] Initialize

[0044] First, the server starts the system and initializes all sensors and control units, which confirms that each sensor is working properly and completes the initial setup.

[0045] Data collection

[0046] Next, the device collects real-time data, specifically the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the GPS sensor. This information is very important and will be used in subsequent analysis steps.

[0047] Data transmission

[0048] The device then sends the collected data to the server, where it is collected in JSON format at regular intervals.

[0049] Data analysis

[0050] The server analyzes the received data and identifies the current riding condition, such as flat road, uphill, or downhill, based on pedaling force, speed, and GPS data.

[0051] Output control decision

[0052] Based on the analysis results, the server determines the motor output. On flat roads, the motor output is set low to minimize battery consumption. On uphill roads, the output is increased to reduce the user's burden.

[0053] Output Transmission

[0054] The server sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time.

[0055] Specific examples

[0056] Scenario 1: Driving on a flat road

[0057] When the user is riding on a flat road, the pedal force sensor measures the user's pedaling force, and the speed sensor measures the speed. Based on this data, the server determines that the road is flat and issues instructions to reduce motor output, thereby minimizing battery consumption.

[0058] Scenario 2: Driving uphill

[0059] When the user is riding uphill, their pedaling force increases and their speed decreases. Based on this data and GPS information, the server determines that they are riding uphill and issues a command to increase motor output. This makes it easier for the user to pedal and climb the hill.

[0060] Battery level notification

[0061] When the battery is low, the server detects this and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to use the electric bicycle more efficiently.

[0062] By using this invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided on various terrains, allowing users to ride the electric bicycle comfortably for long periods of time.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The server initializes the system. The server checks that all sensors and control units are working properly and completes the initial setup. This calibrates each sensor and prepares it for data collection.

[0066] Step 2:

[0067] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor (GPS).

[0068] Step 3:

[0069] The device sends the collected data to the server. Pedaling force, speed, and location data are packaged in JSON format at regular intervals (e.g., every second) and sent to the server in real time.

[0070] Step 4:

[0071] The server analyzes the received data. The server takes in each piece of data and performs analysis to determine the current driving condition (flat road, uphill, downhill, etc.). Analysis methods include data filtering, anomaly detection, and comparison with a terrain database.

[0072] Step 5:

[0073] The server determines the motor output based on the analysis results. On flat roads, the motor output is reduced to minimize battery consumption. On uphill roads, the motor output is increased to reduce the user's pedaling load.

[0074] Step 6:

[0075] The server sends the determined motor output instructions to the terminal, which then transmits the received instructions to the motor control unit, which adjusts the motor output as a specific operation, thereby providing an output that is appropriate for the driving situation in real time.

[0076] Step 7:

[0077] The device notifies the user of the necessary information. Specifically, it displays the remaining battery level, estimated remaining distance, and current power consumption status in real time on the display. When the battery level is low, it also notifies the user of the nearest charging station.

[0078] Step 8:

[0079] The server continues to analyze the data and readjusts the motor output as needed. It periodically collects new data to monitor changes in driving conditions and dynamically reconfigures the motor output. This enables efficient battery management and a comfortable ride even over long periods of use.

[0080] This is the specific processing flow of the electric bicycle system. By implementing this system, users can ride efficiently and comfortably with optimized motor output.

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] With conventional electric bicycles, it is difficult to optimally control motor output according to the user's riding conditions, making it difficult to provide efficient battery consumption and a comfortable riding experience. Furthermore, measures to deal with low battery levels and real-time adjustments according to riding conditions are insufficient. This makes it difficult for users to ride efficiently over various terrains or for long periods of time.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current position information from a position information sensor, means for analyzing the acquired pedaling force, speed, and position information to identify the current riding state, means for controlling the output of the electric motor based on the riding state, means for sending an instruction to control the motor output to an electric motor control unit, means for notifying the user of information related to the output control of the electric motor, means for the server to initialize each sensor and control unit when the system starts up, and means for the terminal to collect data from the sensors and send it to the server, thereby enabling optimal motor output control according to the user's riding state.

[0086] A "pedal force sensor" is a device that detects the force a user applies to the pedals and collects the data in real time.

[0087] A "speed sensor" is a device that measures the speed at which an electric bicycle is moving and provides speed data.

[0088] A "location sensor" is a device that obtains current location information using technology such as GPS.

[0089] The "server" is a central control unit that analyzes the collected data and generates instructions to control the output of the electric motor based on driving conditions.

[0090] A "terminal" is a control unit installed on an electric bicycle, and is a device that collects data from sensors and communicates data with a server.

[0091] The "electric motor control unit" is a device that adjusts the output of the electric motor based on instructions from the server, supporting the user's driving.

[0092] The "riding state" refers to the current riding conditions of the bicycle (e.g., flat road, uphill, downhill) that are determined based on pedaling force, speed, and position information.

[0093] "Initialization" is the process by which the server configures all sensors and control units to operate normally when starting the system.

[0094] "Data collection" is the process in which the terminal acquires data from each sensor in real time and temporarily stores it in a buffer.

[0095] "Data transmission" is the process in which the terminal transmits collected data to the server at regular intervals.

[0096] "Data analysis" is the process of analyzing the data received by the server to determine the current driving condition.

[0097] "Power control" is the process in which the server determines the power level of the electric motor based on the analysis results and sends instructions to the motor control unit.

[0098] "Notification" is the process of conveying information about the output control of the electric motor to the user.

[0099] The present invention relates to a system that acquires data from various sensors on an electric bicycle, analyzes that data, and optimizes it to optimize motor output according to the user's riding conditions. A specific method for implementing the present invention will be described below.

[0100] System Configuration

[0101] This system consists of the following hardware components:

[0102] Pedal force sensor: Detects the force the user applies to the pedal and collects data.

[0103] Speed ​​sensor: Measures the speed at which the electric bicycle is moving.

[0104] Location sensor (GPS): Obtains current location information.

[0105] Server: Analyzes the collected data and generates control instructions for motor output based on the driving conditions.

[0106] Terminal (control unit): Collects data from each sensor and communicates with the server.

[0107] Electric motor control unit: Adjusts the motor output based on instructions from the server.

[0108] Data acquisition and analysis

[0109] First, the server starts the system and initializes each sensor and control unit. This is the process of configuring each sensor so that they can operate normally. Next, the device collects data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location information from the GPS sensor.

[0110] The device sends the collected data to the server at regular intervals (e.g., every second). The data is sent in JSON format and is imported by the server. The server analyzes the received data and identifies the riding condition (flat road, uphill, downhill, etc.). Pedaling force, speed, and GPS data are used for this analysis.

[0111] Motor output control

[0112] Based on the analysis results, the server determines the output of the electric motor and sends the instructions to the device. For example, it sets the motor output low on flat roads and increases it on uphill slopes. The device transmits this instruction to the electric motor control unit, which adjusts the motor output in real time.

[0113] Battery Management

[0114] When the battery level is low, the server detects this and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to use the electric bicycle more efficiently.

[0115] Specific examples

[0116] Scenario 1: Driving on a flat road

[0117] When the user is riding on a flat road, the pedaling force obtained from the pedal force sensor is low and the speed obtained from the speed sensor is constant. Based on this data, the server determines that the road is flat and issues instructions to reduce motor output, thereby minimizing battery consumption.

[0118] Scenario 2: Driving uphill

[0119] When a user is riding uphill, their pedaling force increases and their speed decreases. Based on this data and GPS information, the server determines that they are riding uphill and issues a command to increase motor output. This makes it easier for the user to pedal, allowing them to climb hills more easily.

[0120] Example prompts for generative AI models

[0121] Below are some prompts you can input to your generative AI model:

[0122] "Explain how an electric bicycle's system detects flat roads and optimizes motor power."

[0123] "Describe the process for increasing motor power for a user traveling uphill."

[0124] "Please explain the procedure to notify the user of the nearest charging station when the battery is low."

[0125] The present invention allows users to ride their electric bicycles comfortably for extended periods of time, extending the battery life of the electric bicycle and providing a comfortable ride on a variety of terrains.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1: Initialize

[0128] The server starts the system and initializes each sensor and control unit. Specifically, the server sends an initialization signal and checks that each sensor and control unit is operating normally. The input data is the status information of each sensor, and the output is the state after initial settings are complete. Specifically, the pedal force sensor performs initial calibration, and the speed sensor and GPS sensor obtain initial location and speed data and send it to the server.

[0129] Step 2: Data collection

[0130] The device collects data in real time. The input data is the measurement data from each sensor, and the output data is the data collected by each sensor. Specifically, the device receives the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location information from the GPS sensor, and temporarily stores this data in a buffer. For example, the device obtains the user's pedaling force (N), current speed (km / h), and location information (latitude and longitude) every second.

[0131] Step 3: Send data

[0132] The terminal sends the data it collects to the server. The input data is the sensor measurement data stored on the terminal, and the output data is the data sent to the server. Specifically, the terminal compiles the collected data into transmission packets at regular intervals (for example, every second) and sends them to the server, with the data being sent in JSON format. The server receives this data and stores it in a database.

[0133] Step 4: Data analysis

[0134] The server analyzes the received data and identifies the current riding state. The input data is the sensor measurement data sent to the server, and the output data is the identified riding state. Specifically, data processing and calculations are performed based on pedaling force, speed, and GPS data to determine the riding state, such as flat road, uphill, or downhill. For example, if pedaling force is high and speed is decreasing, it is determined to be an uphill road.

[0135] Step 5: Output control decision

[0136] The server determines the output of the electric motor based on the analysis results. The input data is the identified driving state, and the output data is instructions for motor control. Specifically, the server sets the motor output level according to the driving state. For example, on flat roads, the output is set low to minimize battery consumption, and the output is increased on uphill roads. This optimizes the user's driving.

[0137] Step 6: Send output

[0138] The server sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit. The input data is the motor output instruction sent from the server, and the output data is the output level set in the motor control unit. Specifically, the server sends the generated output instruction to the terminal, and the terminal communicates this instruction to the motor control unit, thereby adjusting the motor output level in real time.

[0139] Step 7: Battery Management

[0140] When the battery level is low, the server detects this and suggests the optimal route, while notifying the user via the device of information about the nearest charging station. The input data is the remaining battery level and current location information, and the output data is the proposed route information and the location information of the charging station. Specifically, the server monitors the remaining battery level, and when it falls below 20%, it references a database of nearby charging stations and calculates the location of the nearest station and the route from the current location. The device notifies the user of this on the display.

[0141] (Application example 1)

[0142] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0143] Autonomous vehicles require the optimization of motor output according to various driving conditions. They also need to suggest efficient routes and provide guidance to charging stations when the battery level is low. This leads to challenges in improving fuel efficiency, driving comfort, and safety.

[0144] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0145] In this invention, the server includes: means for acquiring the user's pedaling force from a pedal force sensor; means for acquiring the current speed from a speed sensor; means for acquiring current position information from a position information sensor; means for analyzing the acquired pedaling force, speed, and position information to identify the current riding state; means for controlling the output of the electric motor based on the riding state; means for sending an instruction to control the motor output to an electric motor control unit; means for notifying the user of information regarding the output control of the electric motor; means for issuing an instruction to reduce the motor output to minimize energy consumption when the vehicle is riding on a flat road; means for calculating the next optimal motor output and adjusting the motor output based on the calculated next optimal motor output when the user's pedal force and riding speed fall below a set standard; means for proposing an optimal route when the battery level is low and notifying the user of information about the nearest charging station; and means for transmitting information regarding optimization of the motor output to the server and adjusting the motor output based on instructions from the server. This enables real-time optimization of the motor output according to riding conditions and efficient route proposals according to the remaining battery level.

[0146] A "pedal force sensor" is a device that measures the force with which a user pedals a bicycle.

[0147] A "speed sensor" is a device that measures the current speed at which a bicycle is traveling.

[0148] A "location information sensor" is a device that measures the current geographical location using GPS or other devices.

[0149] An "electric motor" is a device that converts electrical energy into mechanical power to move a bicycle.

[0150] A "control unit" is a device that analyzes data collected from sensors and provides instructions for adjusting the output of a motor.

[0151] "Real-time analytics" is the process of analyzing data immediately as it is collected.

[0152] "Energy consumption" refers to the power consumed when an electric motor operates.

[0153] "Route suggestion" is the process of showing the optimal travel route based on current location information and driving conditions.

[0154] A "charging station" is a facility where you can replenish power when your battery is low.

[0155] A "server" is a central processing unit that analyzes data and issues instructions.

[0156] "User notification" refers to the action of informing the user of information such as driving status, motor output, and remaining battery power.

[0157] "Optimization" is the process of adjusting to maximize performance under specific conditions.

[0158] The above definitions will enable a clearer understanding of the present invention.

[0159] The present invention relates to a system for optimizing motor output of an autonomous vehicle and performing appropriate control according to the driving situation. Specific embodiments for carrying out the invention will be described below.

[0160] System Overview

[0161] The system consists of a pedal force sensor, a speed sensor, a location information sensor, a server, and a terminal (vehicle control unit). These components collect data in real time and work together to optimize motor output according to the driving conditions of the autonomous vehicle.

[0162] Specific examples of programs and their processing

[0163] First, the server initializes all sensors and control units. This verifies that each sensor is working properly and completes the initial setup. The device collects data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor. This information is very important and will be used in subsequent analysis steps.

[0164] The device then sends the collected data to a server at regular intervals. The data is sent in JSON format and is imported by the server. The server analyzes the received data and identifies the current riding state. Based on pedal force, speed, and location information, the riding state is determined, such as flat road, uphill, or downhill. Based on the analysis results, the server determines the motor output. On flat roads, the motor output is set low to minimize energy consumption. On uphill roads, the output is increased to reduce the user's burden.

[0165] The server then sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit. This allows the motor output to be adjusted in real time. When the battery level is low, the server detects this and suggests an optimal route, while also notifying the user of the nearest charging station. This allows the user to prevent the battery from running out.

[0166] Specific examples

[0167] For example, when the vehicle is running on a flat road, if the user's pedal force is light and the speed is constant, the server keeps the motor output low. This results in less energy consumption and improved fuel efficiency. On the other hand, when going uphill, the pedal force increases and the speed decreases, so the server increases the motor output. This allows the user to climb the slope comfortably.

[0168] Prompt Sentence Examples

[0169] "Judge the current driving conditions (flat road, uphill, etc.) and adjust engine output accordingly. For example, reduce output when driving on flat roads and increase output when driving uphill. Also, if the battery is low, suggest the optimal charging route."

[0170] In this way, this invention enables real-time optimization of motor output according to driving conditions and efficient energy utilization, thereby improving the performance of autonomous vehicles and realizing safe and comfortable driving.

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1:

[0173] The server initializes the system. The inputs are all the sensors and control units that make up the system. The server configures these to operate normally and verifies that the initial configuration is complete. The output is the status of each sensor and control unit functioning normally. Specifically, the server obtains status information from each sensor and checks whether it is normal.

[0174] Step 2:

[0175] The terminal collects data in real time. The input is data from the pedal force sensor, speed sensor, and location information sensor. The terminal acquires and stores this data. The output is the collected pedal force, speed, and location information data. Specifically, the terminal reads data from each sensor at regular intervals and stores it in temporary storage.

[0176] Step 3:

[0177] The device sends the collected data to the server. The input is pedal force, speed, and location data. The device converts this data into JSON format and sends it to the server. The output is the data sent to the server. Specifically, the device converts the data into an appropriate format (JSON) and sends it to the server using the HTTP protocol, etc.

[0178] Step 4:

[0179] The server analyzes the received data. The input is the pedaling force, speed, and position information data sent from the device. The server analyzes these data and identifies the current riding condition (flat road, uphill, downhill, etc.). The output is the identified riding condition. Specifically, the server analyzes each data point and classifies the riding condition based on the conditions.

[0180] Step 5:

[0181] The server determines the motor output. The input is the current driving state. Based on this state, the server calculates the optimal value of motor output. On flat roads, the output is lower, and on uphill roads, the output is higher. The output is the calculated motor output. In concrete terms, the server uses a specific algorithm to calculate the output value and record it.

[0182] Step 6:

[0183] The server sends output instructions to the device. The input is the calculated motor output. The server sends this instruction to the device, which receives it. The output is the sent output instruction. Specifically, the server converts the calculation result into JSON format and sends it to the device using the HTTP protocol, etc.

[0184] Step 7:

[0185] The terminal transmits instructions to the motor control unit. The input is the motor output instruction received from the server. The terminal transmits the instruction to the motor control unit to adjust the motor output. The output is the adjusted motor output. In specific operations, the terminal sends the received instruction to the motor control unit, and the motor output is adjusted accordingly.

[0186] Step 8:

[0187] When the battery level is low, the server proposes a route. The input is the remaining battery level data. The server searches for the optimal route and sends information about the nearest charging station to the terminal. The output is the proposed route and information about the charging station. Specifically, the server refers to a map database to search for the optimal route and charging station.

[0188] Step 9:

[0189] The terminal notifies the user. The input is the route and charging station information sent from the server. The terminal notifies the user of this information. The output is the information provided to the user. As a specific operation, the terminal conveys information to the user using a notification mechanism (display, audio output, etc.).

[0190] The above steps will enable real-time motor output optimization and efficient energy utilization for autonomous vehicles.

[0191] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0192] The embodiment of the present invention is a system that realizes optimal motor output according to the riding condition and the user's emotions by having the sensors of the electric bicycle and the emotion engine work in coordination. A specific implementation method thereof will be described in detail below.

[0193] System Overview

[0194] This system consists of a pedal force sensor, a speed sensor, a location information sensor (GPS), an emotion engine, a server, and a terminal (the control unit of the electric bicycle). These components collect and analyze data in real time, and optimize motor output according to the user's riding condition and emotions.

[0195] Program processing

[0196] Initialize

[0197] First, the server starts the system and initializes all sensors, control units, and emotion engines. This confirms that each sensor and emotion engine is operating normally and completes the initial setup.

[0198] Data collection

[0199] The device then begins collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the GPS sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[0200] Data transmission

[0201] The device sends the collected data (pedaling force, speed, location information, and emotional state) to the server. The data is packaged in JSON format at regular intervals and sent to the server in real time.

[0202] Data analysis

[0203] The server analyzes the received data. The server takes each piece of data and performs analysis to identify the current driving condition (flat road, uphill, downhill, etc.) and the user's emotional state (excited, tired, etc.). Analysis methods include data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms.

[0204] Output control decision

[0205] Based on the analysis results, the server determines the motor output. If fatigue is detected on flat roads, the motor output is set low to minimize battery consumption and reduce the user's burden. If the user becomes excited on an uphill slope, the motor output is increased to provide more pedaling support.

[0206] Output Transmission

[0207] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time to provide optimal assistance according to the user's emotional state and driving conditions.

[0208] Specific examples

[0209] Scenario 1: Flat road and fatigue

[0210] When the user is driving on a flat road, if the emotion engine recognizes the user's state of fatigue from their facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[0211] Scenario 2: Uphill driving and excitement

[0212] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[0213] Battery level notifications and emotional state

[0214] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[0215] By using the present invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided according to the user's emotional state, allowing the electric bicycle to be used comfortably and efficiently for a long period of time.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] The server initializes the system. The server checks that all sensors (pedal force sensor, speed sensor, location information sensor), control unit, and emotion engine are operating normally, and completes the initial setup. This calibrates each sensor and emotion engine, and prepares for data collection.

[0219] Step 2:

[0220] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor (GPS). At the same time, the emotion engine analyzes the user's facial recognition data and voice data to acquire the user's emotional state.

[0221] Step 3:

[0222] The device sends the collected data to the server. The collected data is packaged in JSON format at regular intervals (e.g., every second) and sent to the server in real time. This allows the server to grasp the latest driving and emotional state.

[0223] Step 4:

[0224] The server analyzes the received data. The server takes in each piece of data and determines the current riding condition (flat road, uphill, downhill, etc.) from pedaling force, speed, and location information. It also determines the user's emotional state (excited, tired, etc.) based on data sent from the emotion engine.

[0225] Step 5:

[0226] The server determines the motor output based on the analysis results. For example, if the server determines that the user is fatigued on a flat road, it will set the motor output low to continue supporting the user while minimizing battery consumption. On the other hand, if the server determines that the user is excited on an uphill slope, it will increase the motor output to provide stronger assistance.

[0227] Step 6:

[0228] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time to provide optimal assistance according to the user's emotional state and driving conditions.

[0229] Step 7:

[0230] The device notifies the user of the necessary information. Specifically, the remaining battery level, estimated remaining distance, and current power consumption status are displayed in real time on the screen. When the battery level is low, the device also notifies the user of the nearest charging station, helping the user take appropriate action.

[0231] Step 8:

[0232] The server continues to analyze the data and readjusts the motor output as needed. It periodically collects new data, monitors changes in the driving situation and the user's emotional state, and dynamically reconfigures the motor output. This allows for efficient battery management and a comfortable ride even over long periods of use.

[0233] Specific examples

[0234] Scenario 1: Flat road and fatigue

[0235] When the user is driving on a flat road, if the emotion engine recognizes the user's state of fatigue from their facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[0236] Scenario 2: Uphill driving and excitement

[0237] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[0238] Battery level notifications and emotional state

[0239] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[0240] By using the present invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided according to the user's emotional state, allowing the electric bicycle to be used comfortably and efficiently for a long period of time.

[0241] Example 2

[0242] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0243] Conventional electric bicycle systems do not take into account the user's emotional state, and therefore are unable to provide optimal motor output according to the user's fatigue or excitement level, resulting in insufficient improvements in riding comfort and battery efficiency. In particular, there is a need to reduce user fatigue and battery consumption during long periods of use.

[0244] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current position information from a position information sensor, means for acquiring the user's emotional state from an emotion engine, means for analyzing the acquired pedaling force, speed, position information, and emotional state to identify the current riding state and the user's emotional state, means for controlling the output of the electric motor based on the riding state and the emotional state, means for sending an instruction to control the motor output to an electric motor control unit, and means for notifying the user of information related to the output control of the electric motor. This enables optimal motor output according to the user's riding state and emotional state, thereby improving ride comfort and optimizing battery efficiency.

[0245] A "pedal force sensor" is a sensor that measures the force with which a user pedals a bicycle.

[0246] A "speed sensor" is a sensor for measuring the current speed of an electric bicycle.

[0247] A "location sensor" is a sensor used to measure the current location of an electric bicycle, and typically uses GPS.

[0248] An "emotion engine" is software or hardware that analyzes a user's facial recognition data and voice data to identify the user's emotional state.

[0249] The "server" is a centralized processing device that analyzes data and controls the electric bicycle system.

[0250] The "terminal" is a device that functions as a control unit for the electric bicycle, collects data from various sensors, and communicates with the server.

[0251] The "means for controlling the electric motor output" is a function in which the server generates instructions for adjusting the motor output of the electric bicycle.

[0252] An "electric motor control unit" is the hardware that actually controls the output of the electric bicycle's motor.

[0253] "Means for notifying the user of information related to motor output control" refers to a function that enables a server or terminal to notify the user of the motor output state and changes thereto.

[0254] MODE FOR CARRYING OUT THE INVENTION

[0255] The present invention is a system that realizes optimal motor output according to the riding condition and emotional state of the user by having various sensors mounted on the electric bicycle and an emotion engine work in coordination. A specific implementation method for this system is described in detail below.

[0256] System Configuration

[0257] This system consists of the following hardware and software:

[0258] 1. Pedal force sensor: Measures the force with which the user presses the pedal.

[0259] 2. Speed ​​sensor: Measures the current speed.

[0260] 3. Location sensor (GPS): Measures current location information.

[0261] 4. Emotion Engine: Analyzes the user's facial recognition and voice data to identify their emotional state.

[0262] 5. Server: Analyzes data and generates control instructions.

[0263] 6. Terminal (electric bicycle control unit): Collects data from various sensors and communicates with the server.

[0264] Data collection and transmission

[0265] First, the device starts collecting data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location from the location sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state (e.g., fatigue, excitement, etc.). The collected data is packaged in JSON format at regular intervals and sent from the device to the server in real time.

[0266] Data analysis and output control

[0267] The server analyzes the received data. Specifically, the analysis is performed according to the following procedure.

[0268] 1. Filter each data to remove noise.

[0269] 2. Identify anomalous data using an anomaly detection algorithm.

[0270] 3. The location data is compared with a terrain database to determine the current driving conditions (flat, uphill, downhill, etc.).

[0271] 4. Identify the user's emotional state using emotion recognition algorithms.

[0272] Based on the analysis results, the server determines the motor output and sends the instructions to the device. For example, if the server determines that the user is tired on a flat road, it will set the motor output low. If the server determines that the user is excited on an uphill slope, it will increase the motor output.

[0273] Specific examples

[0274] Specific examples are shown below.

[0275] Scenario 1: Flat road and fatigue

[0276] When the user is driving on a flat road, if the emotion engine recognizes a state of fatigue from the user's facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[0277] Scenario 2: Uphill driving and excitement

[0278] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[0279] Battery level notifications and emotional state

[0280] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[0281] Prompt Sentence Examples

[0282] How does the server detect anomalies when receiving data from the pedal sensor?

[0283]

[0284] How does the server determine motor output when fatigue is detected while driving on a flat road?

[0285]

[0286] How does the server adjust motor power if the user gets excited while going uphill?

[0287] By using this invention, users can extend the battery life of their electric bicycles and ride comfortably with optimal support according to their emotional state.

[0288] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0289] The flow of this system's program processing

[0290] Step 1: Initialize the system

[0291] The server starts the system, initializes all sensors (pedal force sensor, speed sensor, location sensor), control unit, and emotion engine, and checks whether initialization is completed successfully.

[0292] Input: System startup instructions

[0293] Output: Initialization completion status of each sensor and emotion engine

[0294] Step 2: Start collecting data

[0295] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the location sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[0296] Input: Start data acquisition for each sensor

[0297] Output: Pedaling force, speed, position information, emotional state

[0298] Step 3: Packaging the data

[0299] The data collected by the device (pedaling force, speed, location information, and emotional state) is packaged in JSON format at regular intervals.

[0300] Input: Various collected data

[0301] Output: Data packaged in JSON format

[0302] Step 4: Sending data

[0303] The device sends the packaged data to the server, which transmits the data in real time.

[0304] Input: JSON format data package

[0305] Output: Data package sent

[0306] Step 5: Data filtering

[0307] The server takes the received data and removes noise. It uses anomaly detection algorithms to identify and filter out abnormal data.

[0308] Input: Data package sent

[0309] Output: Filtered data

[0310] Step 6: Identify driving conditions

[0311] The server compares the location information with a terrain database to determine the current driving conditions (flat, uphill, downhill, etc.).

[0312] Input: Filtered data

[0313] Output: Driving status information

[0314] Step 7: Identify your emotional state

[0315] The server uses an emotion recognition algorithm to identify the user's emotional state.

[0316] Input: Filtered data

[0317] Output: Emotional state information

[0318] Step 8: Determine the motor power

[0319] The server determines the motor output based on the analysis results. If fatigue is detected on a flat road, the motor output is set low. If excitement is detected on an uphill road, the motor output is increased.

[0320] Input: Driving state and emotional state information

[0321] Output: Motor output indication

[0322] Step 9: Sending motor power instructions

[0323] The server sends the determined motor output instructions to the terminal.

[0324] Input: Motor output instruction

[0325] Output: Motor power output instructions sent

[0326] Step 10: Control and check motor output

[0327] The terminal transmits the motor output instructions received from the server to the motor control unit, adjusting the motor output in real time. The terminal checks whether the motor control unit is operating according to the instructions.

[0328] Input: Motor output instruction

[0329] Output: Regulated motor output, operation check status

[0330] Specific actions

[0331] Scenario 1: While the user is driving on a flat road, the emotion engine recognizes the user's level of fatigue from their facial expression. The device sends the collected data to the server, which analyzes the user's level of fatigue and sends instructions to the device to reduce motor output. The motor control unit follows the instructions and sets the motor output low, providing minimal assistance while saving battery power.

[0332] Scenario 2: When a user is riding uphill, the emotion engine recognizes the user's excitement level from the tone of their voice. The device sends the collected data to the server, which analyzes the excitement level and sends an instruction to the device to increase motor output. The motor control unit increases motor output as instructed, enhancing pedaling assistance.

[0333] (Application example 2)

[0334] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0335] Conventional electric bicycles offer a function to control motor output based on the user's pedaling force, speed, and location information, but they do not optimize the output based on the user's emotional state, which means they are unable to fully realize a comfortable ride. They also lack a system to improve the shopping experience, such as providing recommended product information or discount information based on the user's emotional state. Furthermore, they do not provide appropriate responses when the battery is low. These issues need to be resolved.

[0336] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current location information from a location information sensor, means for acquiring the user's emotional state using an emotion recognition engine for recognizing the emotional state, means for analyzing the acquired pedaling force, speed, location information, and emotional state to identify the current riding state and the user's emotional state, means for controlling the output of the electric motor based on the riding state and the emotional state, means for sending an instruction to control the motor output to an electric motor control unit, means for notifying the user of information related to the output control of the electric motor, means for notifying the user of specific product information or discount information according to the emotional state, and means for notifying the user of information about the nearest charging station when the remaining battery power is low. This improves the user's riding experience, optimizes the shopping experience, and enables efficient battery management.

[0337] A "pedal force sensor" is a device that measures a user's pedaling force in real time.

[0338] A "speed sensor" is a device that acquires the current traveling speed of an electric bicycle.

[0339] A "location information sensor" is a device that uses GPS or other devices to obtain current location information.

[0340] An "emotion recognition engine" is a combination of software and hardware for recognizing a user's emotional state through facial expression analysis and voice analysis.

[0341] The "analysis means" is a system that analyzes the acquired pedaling force, speed, position information, and emotional state, and identifies the riding condition and the emotional state of the user.

[0342] The "electric motor output control means" is a system for appropriately controlling the output of the electric motor based on the driving state and emotional state.

[0343] The "notification means" is a function for notifying the user of information relating to the output control of the electric motor, specific product information, and discount information.

[0344] The "battery remaining amount notification means" is a system for notifying the user of information about the nearest charging station when the battery remaining amount is low.

[0345] The embodiment of the present invention utilizes an emotion recognition engine to obtain the user's emotional state and optimize the electric bicycle and virtual shopping experience. Specific implementation methods are described in detail below.

[0346] The system components include a pedal force sensor, a speed sensor, a location information sensor, an emotion recognition engine, a server, and a terminal (smart glasses or smartphone).

[0347] Program processing overview

[0348] 1. Data Collection

[0349] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the location sensor. In addition, the emotion recognition engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[0350] 2. Data Transmission

[0351] The device sends the collected data to the server. The data is packaged at regular intervals and sent to the server in real time in JSON format.

[0352] 3. Data Analysis

[0353] The server analyzes the received data, specifically using pedaling force, speed, location information, and emotional state to identify the current riding condition and the user's emotional state, using data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms.

[0354] 4. Output control decision

[0355] Based on the analysis results, the server determines the motor output. For example, if the user is fatigued on a flat road, the motor output is set low to minimize battery consumption. On the other hand, if the user is excited on an uphill slope, the motor output is increased to provide more pedaling support.

[0356] 5. Output Transmission and Notification

[0357] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit. The device also notifies the user of specific product information and discount information based on their emotional state. Furthermore, when the battery level is low, it notifies the user of information about the nearest charging station.

[0358] Hardware and software used

[0359] Emotion Recognition Engine: Uses facial recognition camera and voice recognition technology.

[0360] Pedal force sensor, speed sensor, location sensor: Uses standard sensor modules built into the bicycle.

[0361] Server: A high-performance server for large-scale data analysis.

[0362] Terminal (smart glasses, smartphone): A device for collecting data and notifying the user.

[0363] Specific examples

[0364] 1. Driving scenario

[0365] If the emotion engine recognizes a user's fatigue from their facial expression while driving on a flat road, the server will set the motor output low to provide minimal assistance while saving battery power.

[0366] 2. Shopping Scenario

[0367] If the emotion engine recognizes an excited state from the tone of a user's voice while they are shopping in a virtual store, it will notify the smart glasses of specific product information or discount information, helping the user continue shopping comfortably.

[0368] Generative AI model prompt example

[0369] "Generate a program for a smart glasses app that detects the user's emotional state while shopping and provides relaxing content or special discounts accordingly. As a concrete example, please explain the processing flow when the user is feeling stressed, including the definitions of related classes and methods."

[0370] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0371] Step 1:

[0372] The server starts the system and initializes all sensors and emotion recognition engines. This confirms that each sensor and emotion engine is operating normally and completes the initial setup. The input is the system startup command, and the output is the normal operating status of the sensors and emotion recognition engines.

[0373] Step 2:

[0374] The device starts collecting data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location from the location sensor. At the same time, the emotion recognition engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state. The input is data from each sensor and the emotion recognition engine, and the output is a dataset of pedaling force, speed, location, and emotional state.

[0375] Step 3:

[0376] The terminal sends the data collected to the server. The data is packaged at regular intervals and sent to the server in JSON format. The input is the dataset collected by the terminal, and the output is the JSON data sent to the server.

[0377] Step 4:

[0378] The server analyzes the received data. Specifically, it uses pedaling force, speed, location information, and emotional state to determine the current riding state and the user's emotional state. The server uses data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms. The input is the JSON data sent to the server, and the output is the analyzed riding state and emotional state.

[0379] Step 5:

[0380] The server determines the motor output based on the analysis results. For example, if fatigue is detected on a flat road, the motor output is set low to minimize battery consumption. If the user becomes excited while going uphill, the motor output is increased to provide stronger pedaling support. The input is the analysis result of the riding state and emotional state, and the output is the motor control command.

[0381] Step 6:

[0382] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit. It also notifies the user of specific product information and discount information depending on their emotional state. Furthermore, if the battery level is low, it notifies the user of information about the nearest charging station. The inputs are motor control instructions and notification information, and the outputs are control signals to the motor control unit and notification content to the user.

[0383] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0384] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0385] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0386] [Second embodiment]

[0387] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0388] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0389] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0390] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0391] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0393] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0394] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0395] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0396] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0397] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0398] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0399] To implement the present invention, a system is required in which each sensor on the electric bicycle acquires data, and the server analyzes and optimizes that data. A specific implementation method for this system is described below.

[0400] System Overview

[0401] The system consists of a pedal force sensor, a speed sensor, a location information sensor (GPS), a server, and a terminal (the control unit of the electric bicycle). These components work together to collect data in real time and optimize motor output according to the user's riding conditions.

[0402] Program processing

[0403] Initialize

[0404] First, the server starts the system and initializes all sensors and control units, which confirms that each sensor is working properly and completes the initial setup.

[0405] Data collection

[0406] Next, the device collects real-time data, specifically the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the GPS sensor. This information is very important and will be used in subsequent analysis steps.

[0407] Data transmission

[0408] The device then sends the collected data to the server, where it is collected in JSON format at regular intervals.

[0409] Data analysis

[0410] The server analyzes the received data and identifies the current riding condition, such as flat road, uphill, or downhill, based on pedaling force, speed, and GPS data.

[0411] Output control decision

[0412] Based on the analysis results, the server determines the motor output. On flat roads, the motor output is set low to minimize battery consumption. On uphill roads, the output is increased to reduce the user's burden.

[0413] Output Transmission

[0414] The server sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time.

[0415] Specific examples

[0416] Scenario 1: Driving on a flat road

[0417] When the user is riding on a flat road, the pedal force sensor measures the user's pedaling force, and the speed sensor measures the speed. Based on this data, the server determines that the road is flat and issues instructions to reduce motor output, thereby minimizing battery consumption.

[0418] Scenario 2: Driving uphill

[0419] When the user is riding uphill, their pedaling force increases and their speed decreases. Based on this data and GPS information, the server determines that they are riding uphill and issues a command to increase motor output. This makes it easier for the user to pedal and climb the hill.

[0420] Battery level notification

[0421] When the battery is low, the server detects this and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to use the electric bicycle more efficiently.

[0422] By using this invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided on various terrains, allowing users to ride the electric bicycle comfortably for long periods of time.

[0423] The processing flow will be explained below.

[0424] Step 1:

[0425] The server initializes the system. The server checks that all sensors and control units are working properly and completes the initial setup. This calibrates each sensor and prepares it for data collection.

[0426] Step 2:

[0427] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor (GPS).

[0428] Step 3:

[0429] The device sends the collected data to the server. Pedaling force, speed, and location data are packaged in JSON format at regular intervals (e.g., every second) and sent to the server in real time.

[0430] Step 4:

[0431] The server analyzes the received data. The server takes in each piece of data and performs analysis to determine the current driving condition (flat road, uphill, downhill, etc.). Analysis methods include data filtering, anomaly detection, and comparison with a terrain database.

[0432] Step 5:

[0433] The server determines the motor output based on the analysis results. On flat roads, the motor output is reduced to minimize battery consumption. On uphill roads, the motor output is increased to reduce the user's pedaling load.

[0434] Step 6:

[0435] The server sends the determined motor output instructions to the terminal, which then transmits the received instructions to the motor control unit, which adjusts the motor output as a specific operation, thereby providing an output that is appropriate for the driving situation in real time.

[0436] Step 7:

[0437] The device notifies the user of the necessary information. Specifically, it displays the remaining battery level, estimated remaining distance, and current power consumption status in real time on the display. When the battery level is low, it also notifies the user of the nearest charging station.

[0438] Step 8:

[0439] The server continues to analyze the data and readjusts the motor output as needed. It periodically collects new data to monitor changes in driving conditions and dynamically reconfigures the motor output. This enables efficient battery management and a comfortable ride even over long periods of use.

[0440] This is the specific processing flow of the electric bicycle system. By implementing this system, users can ride efficiently and comfortably with optimized motor output.

[0441] Example 1

[0442] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0443] With conventional electric bicycles, it is difficult to optimally control motor output according to the user's riding conditions, making it difficult to provide efficient battery consumption and a comfortable riding experience. Furthermore, measures to deal with low battery levels and real-time adjustments according to riding conditions are insufficient. This makes it difficult for users to ride efficiently over various terrains or for long periods of time.

[0444] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0445] In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current position information from a position information sensor, means for analyzing the acquired pedaling force, speed, and position information to identify the current riding state, means for controlling the output of the electric motor based on the riding state, means for sending an instruction to control the motor output to an electric motor control unit, means for notifying the user of information related to the output control of the electric motor, means for the server to initialize each sensor and control unit when the system starts up, and means for the terminal to collect data from the sensors and send it to the server, thereby enabling optimal motor output control according to the user's riding state.

[0446] A "pedal force sensor" is a device that detects the force a user applies to the pedals and collects the data in real time.

[0447] A "speed sensor" is a device that measures the speed at which an electric bicycle is moving and provides speed data.

[0448] A "location sensor" is a device that obtains current location information using technology such as GPS.

[0449] The "server" is a central control unit that analyzes the collected data and generates instructions to control the output of the electric motor based on driving conditions.

[0450] A "terminal" is a control unit installed on an electric bicycle, and is a device that collects data from sensors and communicates data with a server.

[0451] The "electric motor control unit" is a device that adjusts the output of the electric motor based on instructions from the server, supporting the user's driving.

[0452] The "riding state" refers to the current riding conditions of the bicycle (e.g., flat road, uphill, downhill) that are determined based on pedaling force, speed, and position information.

[0453] "Initialization" is the process by which the server configures all sensors and control units to operate normally when starting the system.

[0454] "Data collection" is the process in which the terminal acquires data from each sensor in real time and temporarily stores it in a buffer.

[0455] "Data transmission" is the process in which the terminal transmits collected data to the server at regular intervals.

[0456] "Data analysis" is the process of analyzing the data received by the server to determine the current driving condition.

[0457] "Power control" is the process in which the server determines the power level of the electric motor based on the analysis results and sends instructions to the motor control unit.

[0458] "Notification" is the process of conveying information about the output control of the electric motor to the user.

[0459] The present invention relates to a system that acquires data from various sensors on an electric bicycle, analyzes that data, and optimizes it to optimize motor output according to the user's riding conditions. A specific method for implementing the present invention will be described below.

[0460] System Configuration

[0461] This system consists of the following hardware components:

[0462] Pedal force sensor: Detects the force the user applies to the pedal and collects data.

[0463] Speed ​​sensor: Measures the speed at which the electric bicycle is moving.

[0464] Location sensor (GPS): Obtains current location information.

[0465] Server: Analyzes the collected data and generates control instructions for motor output based on the driving conditions.

[0466] Terminal (control unit): Collects data from each sensor and communicates with the server.

[0467] Electric motor control unit: Adjusts the motor output based on instructions from the server.

[0468] Data acquisition and analysis

[0469] First, the server starts the system and initializes each sensor and control unit. This is the process of configuring each sensor so that they can operate normally. Next, the device collects data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location information from the GPS sensor.

[0470] The device sends the collected data to the server at regular intervals (e.g., every second). The data is sent in JSON format and is imported by the server. The server analyzes the received data and identifies the riding condition (flat road, uphill, downhill, etc.). Pedaling force, speed, and GPS data are used for this analysis.

[0471] Motor output control

[0472] Based on the analysis results, the server determines the output of the electric motor and sends the instructions to the device. For example, it sets the motor output low on flat roads and increases it on uphill slopes. The device transmits this instruction to the electric motor control unit, which adjusts the motor output in real time.

[0473] Battery Management

[0474] When the battery level is low, the server detects this and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to use the electric bicycle more efficiently.

[0475] Specific examples

[0476] Scenario 1: Driving on a flat road

[0477] When the user is riding on a flat road, the pedaling force obtained from the pedal force sensor is low and the speed obtained from the speed sensor is constant. Based on this data, the server determines that the road is flat and issues instructions to reduce motor output, thereby minimizing battery consumption.

[0478] Scenario 2: Driving uphill

[0479] When a user is riding uphill, their pedaling force increases and their speed decreases. Based on this data and GPS information, the server determines that they are riding uphill and issues a command to increase motor output. This makes it easier for the user to pedal, allowing them to climb hills more easily.

[0480] Example prompts for generative AI models

[0481] Below are some prompts you can input to your generative AI model:

[0482] "Explain how an electric bicycle's system detects flat roads and optimizes motor power."

[0483] "Describe the process for increasing motor power for a user traveling uphill."

[0484] "Please explain the procedure to notify the user of the nearest charging station when the battery is low."

[0485] The present invention allows users to ride their electric bicycles comfortably for extended periods of time, extending the battery life of the electric bicycle and providing a comfortable ride on a variety of terrains.

[0486] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0487] Step 1: Initialize

[0488] The server starts the system and initializes each sensor and control unit. Specifically, the server sends an initialization signal and checks that each sensor and control unit is operating normally. The input data is the status information of each sensor, and the output is the state after initial settings are complete. Specifically, the pedal force sensor performs initial calibration, and the speed sensor and GPS sensor obtain initial location and speed data and send it to the server.

[0489] Step 2: Data collection

[0490] The device collects data in real time. The input data is the measurement data from each sensor, and the output data is the data collected by each sensor. Specifically, the device receives the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location information from the GPS sensor, and temporarily stores this data in a buffer. For example, the device obtains the user's pedaling force (N), current speed (km / h), and location information (latitude and longitude) every second.

[0491] Step 3: Send data

[0492] The terminal sends the data it collects to the server. The input data is the sensor measurement data stored on the terminal, and the output data is the data sent to the server. Specifically, the terminal compiles the collected data into transmission packets at regular intervals (for example, every second) and sends them to the server, with the data being sent in JSON format. The server receives this data and stores it in a database.

[0493] Step 4: Data analysis

[0494] The server analyzes the received data and identifies the current riding state. The input data is the sensor measurement data sent to the server, and the output data is the identified riding state. Specifically, data processing and calculations are performed based on pedaling force, speed, and GPS data to determine the riding state, such as flat road, uphill, or downhill. For example, if pedaling force is high and speed is decreasing, it is determined to be an uphill road.

[0495] Step 5: Output control decision

[0496] The server determines the output of the electric motor based on the analysis results. The input data is the identified driving state, and the output data is instructions for motor control. Specifically, the server sets the motor output level according to the driving state. For example, on flat roads, the output is set low to minimize battery consumption, and the output is increased on uphill roads. This optimizes the user's driving.

[0497] Step 6: Send output

[0498] The server sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit. The input data is the motor output instruction sent from the server, and the output data is the output level set in the motor control unit. Specifically, the server sends the generated output instruction to the terminal, and the terminal communicates this instruction to the motor control unit, thereby adjusting the motor output level in real time.

[0499] Step 7: Battery Management

[0500] When the battery level is low, the server detects this and suggests the optimal route, while notifying the user via the device of information about the nearest charging station. The input data is the remaining battery level and current location information, and the output data is the proposed route information and the location information of the charging station. Specifically, the server monitors the remaining battery level, and when it falls below 20%, it references a database of nearby charging stations and calculates the location of the nearest station and the route from the current location. The device notifies the user of this on the display.

[0501] (Application example 1)

[0502] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0503] Autonomous vehicles require the optimization of motor output according to various driving conditions. They also need to suggest efficient routes and provide guidance to charging stations when the battery level is low. This leads to challenges in improving fuel efficiency, driving comfort, and safety.

[0504] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0505] In this invention, the server includes: means for acquiring the user's pedaling force from a pedal force sensor; means for acquiring the current speed from a speed sensor; means for acquiring current position information from a position information sensor; means for analyzing the acquired pedaling force, speed, and position information to identify the current riding state; means for controlling the output of the electric motor based on the riding state; means for sending an instruction to control the motor output to an electric motor control unit; means for notifying the user of information regarding the output control of the electric motor; means for issuing an instruction to reduce the motor output to minimize energy consumption when the vehicle is riding on a flat road; means for calculating the next optimal motor output and adjusting the motor output based on the calculated next optimal motor output when the user's pedal force and riding speed fall below a set standard; means for proposing an optimal route when the battery level is low and notifying the user of information about the nearest charging station; and means for transmitting information regarding optimization of the motor output to the server and adjusting the motor output based on instructions from the server. This enables real-time optimization of the motor output according to riding conditions and efficient route proposals according to the remaining battery level.

[0506] A "pedal force sensor" is a device that measures the force with which a user pedals a bicycle.

[0507] A "speed sensor" is a device that measures the current speed at which a bicycle is traveling.

[0508] A "location information sensor" is a device that measures the current geographical location using GPS or other devices.

[0509] An "electric motor" is a device that converts electrical energy into mechanical power to move a bicycle.

[0510] A "control unit" is a device that analyzes data collected from sensors and provides instructions for adjusting the output of a motor.

[0511] "Real-time analytics" is the process of analyzing data immediately as it is collected.

[0512] "Energy consumption" refers to the power consumed when an electric motor operates.

[0513] "Route suggestion" is the process of showing the optimal travel route based on current location information and driving conditions.

[0514] A "charging station" is a facility where you can replenish power when your battery is low.

[0515] A "server" is a central processing unit that analyzes data and issues instructions.

[0516] "User notification" refers to the action of informing the user of information such as driving status, motor output, and remaining battery power.

[0517] "Optimization" is the process of adjusting to maximize performance under specific conditions.

[0518] The above definitions will enable a clearer understanding of the present invention.

[0519] The present invention relates to a system for optimizing motor output of an autonomous vehicle and performing appropriate control according to the driving situation. Specific embodiments for carrying out the invention will be described below.

[0520] System Overview

[0521] The system consists of a pedal force sensor, a speed sensor, a location information sensor, a server, and a terminal (vehicle control unit). These components collect data in real time and work together to optimize motor output according to the driving conditions of the autonomous vehicle.

[0522] Specific examples of programs and their processing

[0523] First, the server initializes all sensors and control units. This verifies that each sensor is working properly and completes the initial setup. The device collects data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor. This information is very important and will be used in subsequent analysis steps.

[0524] The device then sends the collected data to a server at regular intervals. The data is sent in JSON format and is imported by the server. The server analyzes the received data and identifies the current riding state. Based on pedal force, speed, and location information, the riding state is determined, such as flat road, uphill, or downhill. Based on the analysis results, the server determines the motor output. On flat roads, the motor output is set low to minimize energy consumption. On uphill roads, the output is increased to reduce the user's burden.

[0525] The server then sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit. This allows the motor output to be adjusted in real time. When the battery level is low, the server detects this and suggests an optimal route, while also notifying the user of the nearest charging station. This allows the user to prevent the battery from running out.

[0526] Specific examples

[0527] For example, when the vehicle is running on a flat road, if the user's pedal force is light and the speed is constant, the server keeps the motor output low. This results in less energy consumption and improved fuel efficiency. On the other hand, when going uphill, the pedal force increases and the speed decreases, so the server increases the motor output. This allows the user to climb the slope comfortably.

[0528] Prompt Sentence Examples

[0529] "Judge the current driving conditions (flat road, uphill, etc.) and adjust engine output accordingly. For example, reduce output when driving on flat roads and increase output when driving uphill. Also, if the battery is low, suggest the optimal charging route."

[0530] In this way, this invention enables real-time optimization of motor output according to driving conditions and efficient energy utilization, thereby improving the performance of autonomous vehicles and realizing safe and comfortable driving.

[0531] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0532] Step 1:

[0533] The server initializes the system. The inputs are all the sensors and control units that make up the system. The server configures these to operate normally and verifies that the initial configuration is complete. The output is the status of each sensor and control unit functioning normally. Specifically, the server obtains status information from each sensor and checks whether it is normal.

[0534] Step 2:

[0535] The terminal collects data in real time. The input is data from the pedal force sensor, speed sensor, and location information sensor. The terminal acquires and stores this data. The output is the collected pedal force, speed, and location information data. Specifically, the terminal reads data from each sensor at regular intervals and stores it in temporary storage.

[0536] Step 3:

[0537] The device sends the collected data to the server. The input is pedal force, speed, and location data. The device converts this data into JSON format and sends it to the server. The output is the data sent to the server. Specifically, the device converts the data into an appropriate format (JSON) and sends it to the server using the HTTP protocol, etc.

[0538] Step 4:

[0539] The server analyzes the received data. The input is the pedaling force, speed, and position information data sent from the device. The server analyzes these data and identifies the current riding condition (flat road, uphill, downhill, etc.). The output is the identified riding condition. Specifically, the server analyzes each data point and classifies the riding condition based on the conditions.

[0540] Step 5:

[0541] The server determines the motor output. The input is the current driving state. Based on this state, the server calculates the optimal value of motor output. On flat roads, the output is lower, and on uphill roads, the output is higher. The output is the calculated motor output. In concrete terms, the server uses a specific algorithm to calculate the output value and record it.

[0542] Step 6:

[0543] The server sends output instructions to the device. The input is the calculated motor output. The server sends this instruction to the device, which receives it. The output is the sent output instruction. Specifically, the server converts the calculation result into JSON format and sends it to the device using the HTTP protocol, etc.

[0544] Step 7:

[0545] The terminal transmits instructions to the motor control unit. The input is the motor output instruction received from the server. The terminal transmits the instruction to the motor control unit to adjust the motor output. The output is the adjusted motor output. In specific operations, the terminal sends the received instruction to the motor control unit, and the motor output is adjusted accordingly.

[0546] Step 8:

[0547] When the battery level is low, the server proposes a route. The input is the remaining battery level data. The server searches for the optimal route and sends information about the nearest charging station to the terminal. The output is the proposed route and information about the charging station. Specifically, the server refers to a map database to search for the optimal route and charging station.

[0548] Step 9:

[0549] The terminal notifies the user. The input is the route and charging station information sent from the server. The terminal notifies the user of this information. The output is the information provided to the user. As a specific operation, the terminal conveys information to the user using a notification mechanism (display, audio output, etc.).

[0550] The above steps will enable real-time motor output optimization and efficient energy utilization for autonomous vehicles.

[0551] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0552] The embodiment of the present invention is a system that realizes optimal motor output according to the riding condition and the user's emotions by having the sensors of the electric bicycle and the emotion engine work in coordination. A specific implementation method thereof will be described in detail below.

[0553] System Overview

[0554] This system consists of a pedal force sensor, a speed sensor, a location information sensor (GPS), an emotion engine, a server, and a terminal (the control unit of the electric bicycle). These components collect and analyze data in real time, and optimize motor output according to the user's riding condition and emotions.

[0555] Program processing

[0556] Initialize

[0557] First, the server starts the system and initializes all sensors, control units, and emotion engines. This confirms that each sensor and emotion engine is operating normally and completes the initial setup.

[0558] Data collection

[0559] The device then begins collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the GPS sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[0560] Data transmission

[0561] The device sends the collected data (pedaling force, speed, location information, and emotional state) to the server. The data is packaged in JSON format at regular intervals and sent to the server in real time.

[0562] Data analysis

[0563] The server analyzes the received data. The server takes each piece of data and performs analysis to identify the current driving condition (flat road, uphill, downhill, etc.) and the user's emotional state (excited, tired, etc.). Analysis methods include data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms.

[0564] Output control decision

[0565] Based on the analysis results, the server determines the motor output. If fatigue is detected on flat roads, the motor output is set low to minimize battery consumption and reduce the user's burden. If the user becomes excited on an uphill slope, the motor output is increased to provide more pedaling support.

[0566] Output Transmission

[0567] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time to provide optimal assistance according to the user's emotional state and driving conditions.

[0568] Specific examples

[0569] Scenario 1: Flat road and fatigue

[0570] When the user is driving on a flat road, if the emotion engine recognizes the user's state of fatigue from their facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[0571] Scenario 2: Uphill driving and excitement

[0572] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[0573] Battery level notifications and emotional state

[0574] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[0575] By using the present invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided according to the user's emotional state, allowing the electric bicycle to be used comfortably and efficiently for a long period of time.

[0576] The processing flow will be explained below.

[0577] Step 1:

[0578] The server initializes the system. The server checks that all sensors (pedal force sensor, speed sensor, location information sensor), control unit, and emotion engine are operating normally, and completes the initial setup. This calibrates each sensor and emotion engine, and prepares for data collection.

[0579] Step 2:

[0580] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor (GPS). At the same time, the emotion engine analyzes the user's facial recognition data and voice data to acquire the user's emotional state.

[0581] Step 3:

[0582] The device sends the collected data to the server. The collected data is packaged in JSON format at regular intervals (e.g., every second) and sent to the server in real time. This allows the server to grasp the latest driving and emotional state.

[0583] Step 4:

[0584] The server analyzes the received data. The server takes in each piece of data and determines the current riding condition (flat road, uphill, downhill, etc.) from pedaling force, speed, and location information. It also determines the user's emotional state (excited, tired, etc.) based on data sent from the emotion engine.

[0585] Step 5:

[0586] The server determines the motor output based on the analysis results. For example, if the server determines that the user is fatigued on a flat road, it will set the motor output low to continue supporting the user while minimizing battery consumption. On the other hand, if the server determines that the user is excited on an uphill slope, it will increase the motor output to provide stronger assistance.

[0587] Step 6:

[0588] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time to provide optimal assistance according to the user's emotional state and driving conditions.

[0589] Step 7:

[0590] The device notifies the user of the necessary information. Specifically, the remaining battery level, estimated remaining distance, and current power consumption status are displayed in real time on the screen. When the battery level is low, the device also notifies the user of the nearest charging station, helping the user take appropriate action.

[0591] Step 8:

[0592] The server continues to analyze the data and readjusts the motor output as needed. It periodically collects new data, monitors changes in the driving situation and the user's emotional state, and dynamically reconfigures the motor output. This allows for efficient battery management and a comfortable ride even over long periods of use.

[0593] Specific examples

[0594] Scenario 1: Flat road and fatigue

[0595] When the user is driving on a flat road, if the emotion engine recognizes the user's state of fatigue from their facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[0596] Scenario 2: Uphill driving and excitement

[0597] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[0598] Battery level notifications and emotional state

[0599] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[0600] By using the present invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided according to the user's emotional state, allowing the electric bicycle to be used comfortably and efficiently for a long period of time.

[0601] Example 2

[0602] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0603] Conventional electric bicycle systems do not take into account the user's emotional state, and therefore are unable to provide optimal motor output according to the user's fatigue or excitement level, resulting in insufficient improvements in riding comfort and battery efficiency. In particular, there is a need to reduce user fatigue and battery consumption during long periods of use.

[0604] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current position information from a position information sensor, means for acquiring the user's emotional state from an emotion engine, means for analyzing the acquired pedaling force, speed, position information, and emotional state to identify the current riding state and the user's emotional state, means for controlling the output of the electric motor based on the riding state and the emotional state, means for sending an instruction to control the motor output to an electric motor control unit, and means for notifying the user of information related to the output control of the electric motor. This enables optimal motor output according to the user's riding state and emotional state, thereby improving ride comfort and optimizing battery efficiency.

[0605] A "pedal force sensor" is a sensor that measures the force with which a user pedals a bicycle.

[0606] A "speed sensor" is a sensor for measuring the current speed of an electric bicycle.

[0607] A "location sensor" is a sensor used to measure the current location of an electric bicycle, and typically uses GPS.

[0608] An "emotion engine" is software or hardware that analyzes a user's facial recognition data and voice data to identify the user's emotional state.

[0609] The "server" is a centralized processing device that analyzes data and controls the electric bicycle system.

[0610] The "terminal" is a device that functions as a control unit for the electric bicycle, collects data from various sensors, and communicates with the server.

[0611] The "means for controlling the electric motor output" is a function in which the server generates instructions for adjusting the motor output of the electric bicycle.

[0612] An "electric motor control unit" is the hardware that actually controls the output of the electric bicycle's motor.

[0613] "Means for notifying the user of information related to motor output control" refers to a function that enables a server or terminal to notify the user of the motor output state and changes thereto.

[0614] MODE FOR CARRYING OUT THE INVENTION

[0615] The present invention is a system that realizes optimal motor output according to the riding condition and emotional state of the user by having various sensors mounted on the electric bicycle and an emotion engine work in coordination. A specific implementation method for this system is described in detail below.

[0616] System Configuration

[0617] This system consists of the following hardware and software:

[0618] 1. Pedal force sensor: Measures the force with which the user presses the pedal.

[0619] 2. Speed ​​sensor: Measures the current speed.

[0620] 3. Location sensor (GPS): Measures current location information.

[0621] 4. Emotion Engine: Analyzes the user's facial recognition and voice data to identify their emotional state.

[0622] 5. Server: Analyzes data and generates control instructions.

[0623] 6. Terminal (electric bicycle control unit): Collects data from various sensors and communicates with the server.

[0624] Data collection and transmission

[0625] First, the device starts collecting data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location from the location sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state (e.g., fatigue, excitement, etc.). The collected data is packaged in JSON format at regular intervals and sent from the device to the server in real time.

[0626] Data analysis and output control

[0627] The server analyzes the received data. Specifically, the analysis is performed according to the following procedure.

[0628] 1. Filter each data to remove noise.

[0629] 2. Identify anomalous data using an anomaly detection algorithm.

[0630] 3. The location data is compared with a terrain database to determine the current driving conditions (flat, uphill, downhill, etc.).

[0631] 4. Identify the user's emotional state using emotion recognition algorithms.

[0632] Based on the analysis results, the server determines the motor output and sends the instructions to the device. For example, if the server determines that the user is tired on a flat road, it will set the motor output low. If the server determines that the user is excited on an uphill slope, it will increase the motor output.

[0633] Specific examples

[0634] Specific examples are shown below.

[0635] Scenario 1: Flat road and fatigue

[0636] When the user is driving on a flat road, if the emotion engine recognizes a state of fatigue from the user's facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[0637] Scenario 2: Uphill driving and excitement

[0638] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[0639] Battery level notifications and emotional state

[0640] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[0641] Prompt Sentence Examples

[0642] How does the server detect anomalies when receiving data from the pedal sensor?

[0643]

[0644] How does the server determine motor output when fatigue is detected while driving on a flat road?

[0645]

[0646] How does the server adjust motor power if the user gets excited while going uphill?

[0647] By using this invention, users can extend the battery life of their electric bicycles and ride comfortably with optimal support according to their emotional state.

[0648] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0649] The flow of this system's program processing

[0650] Step 1: Initialize the system

[0651] The server starts the system, initializes all sensors (pedal force sensor, speed sensor, location sensor), control unit, and emotion engine, and checks whether initialization is completed successfully.

[0652] Input: System startup instructions

[0653] Output: Initialization completion status of each sensor and emotion engine

[0654] Step 2: Start collecting data

[0655] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the location sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[0656] Input: Start data acquisition for each sensor

[0657] Output: Pedaling force, speed, position information, emotional state

[0658] Step 3: Packaging the data

[0659] The data collected by the device (pedaling force, speed, location information, and emotional state) is packaged in JSON format at regular intervals.

[0660] Input: Various collected data

[0661] Output: Data packaged in JSON format

[0662] Step 4: Sending data

[0663] The device sends the packaged data to the server, which transmits the data in real time.

[0664] Input: JSON format data package

[0665] Output: Data package sent

[0666] Step 5: Data filtering

[0667] The server takes the received data and removes noise. It uses anomaly detection algorithms to identify and filter out abnormal data.

[0668] Input: Data package sent

[0669] Output: Filtered data

[0670] Step 6: Identify driving conditions

[0671] The server compares the location information with a terrain database to determine the current driving conditions (flat, uphill, downhill, etc.).

[0672] Input: Filtered data

[0673] Output: Driving status information

[0674] Step 7: Identify your emotional state

[0675] The server uses an emotion recognition algorithm to identify the user's emotional state.

[0676] Input: Filtered data

[0677] Output: Emotional state information

[0678] Step 8: Determine the motor power

[0679] The server determines the motor output based on the analysis results. If fatigue is detected on a flat road, the motor output is set low. If excitement is detected on an uphill road, the motor output is increased.

[0680] Input: Driving state and emotional state information

[0681] Output: Motor output indication

[0682] Step 9: Sending motor power instructions

[0683] The server sends the determined motor output instructions to the terminal.

[0684] Input: Motor output instruction

[0685] Output: Motor power output instructions sent

[0686] Step 10: Control and check motor output

[0687] The terminal transmits the motor output instructions received from the server to the motor control unit, adjusting the motor output in real time. The terminal checks whether the motor control unit is operating according to the instructions.

[0688] Input: Motor output instruction

[0689] Output: Regulated motor output, operation check status

[0690] Specific actions

[0691] Scenario 1: While the user is driving on a flat road, the emotion engine recognizes the user's level of fatigue from their facial expression. The device sends the collected data to the server, which analyzes the user's level of fatigue and sends instructions to the device to reduce motor output. The motor control unit follows the instructions and sets the motor output low, providing minimal assistance while saving battery power.

[0692] Scenario 2: When a user is riding uphill, the emotion engine recognizes the user's excitement level from the tone of their voice. The device sends the collected data to the server, which analyzes the excitement level and sends an instruction to the device to increase motor output. The motor control unit increases motor output as instructed, enhancing pedaling assistance.

[0693] (Application example 2)

[0694] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0695] Conventional electric bicycles offer a function to control motor output based on the user's pedaling force, speed, and location information, but they do not optimize the output based on the user's emotional state, which means they are unable to fully realize a comfortable ride. They also lack a system to improve the shopping experience, such as providing recommended product information or discount information based on the user's emotional state. Furthermore, they do not provide appropriate responses when the battery is low. These issues need to be resolved.

[0696] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current location information from a location information sensor, means for acquiring the user's emotional state using an emotion recognition engine for recognizing the emotional state, means for analyzing the acquired pedaling force, speed, location information, and emotional state to identify the current riding state and the user's emotional state, means for controlling the output of the electric motor based on the riding state and the emotional state, means for sending an instruction to control the motor output to an electric motor control unit, means for notifying the user of information related to the output control of the electric motor, means for notifying the user of specific product information or discount information according to the emotional state, and means for notifying the user of information about the nearest charging station when the remaining battery power is low. This improves the user's riding experience, optimizes the shopping experience, and enables efficient battery management.

[0697] A "pedal force sensor" is a device that measures a user's pedaling force in real time.

[0698] A "speed sensor" is a device that acquires the current traveling speed of an electric bicycle.

[0699] A "location information sensor" is a device that uses GPS or other devices to obtain current location information.

[0700] An "emotion recognition engine" is a combination of software and hardware for recognizing a user's emotional state through facial expression analysis and voice analysis.

[0701] The "analysis means" is a system that analyzes the acquired pedaling force, speed, position information, and emotional state, and identifies the riding condition and the emotional state of the user.

[0702] The "electric motor output control means" is a system for appropriately controlling the output of the electric motor based on the driving state and emotional state.

[0703] The "notification means" is a function for notifying the user of information relating to the output control of the electric motor, specific product information, and discount information.

[0704] The "battery remaining amount notification means" is a system for notifying the user of information about the nearest charging station when the battery remaining amount is low.

[0705] The embodiment of the present invention utilizes an emotion recognition engine to obtain the user's emotional state and optimize the electric bicycle and virtual shopping experience. Specific implementation methods are described in detail below.

[0706] The system components include a pedal force sensor, a speed sensor, a location information sensor, an emotion recognition engine, a server, and a terminal (smart glasses or smartphone).

[0707] Program processing overview

[0708] 1. Data Collection

[0709] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the location sensor. In addition, the emotion recognition engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[0710] 2. Data Transmission

[0711] The device sends the collected data to the server. The data is packaged at regular intervals and sent to the server in real time in JSON format.

[0712] 3. Data Analysis

[0713] The server analyzes the received data, specifically using pedaling force, speed, location information, and emotional state to identify the current riding condition and the user's emotional state, using data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms.

[0714] 4. Output control decision

[0715] Based on the analysis results, the server determines the motor output. For example, if the user is fatigued on a flat road, the motor output is set low to minimize battery consumption. On the other hand, if the user is excited on an uphill slope, the motor output is increased to provide more pedaling support.

[0716] 5. Output Transmission and Notification

[0717] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit. The device also notifies the user of specific product information and discount information based on their emotional state. Furthermore, when the battery level is low, it notifies the user of information about the nearest charging station.

[0718] Hardware and software used

[0719] Emotion Recognition Engine: Uses facial recognition camera and voice recognition technology.

[0720] Pedal force sensor, speed sensor, location sensor: Uses standard sensor modules built into the bicycle.

[0721] Server: A high-performance server for large-scale data analysis.

[0722] Terminal (smart glasses, smartphone): A device for collecting data and notifying the user.

[0723] Specific examples

[0724] 1. Driving scenario

[0725] If the emotion engine recognizes a user's fatigue from their facial expression while driving on a flat road, the server will set the motor output low to provide minimal assistance while saving battery power.

[0726] 2. Shopping Scenario

[0727] If the emotion engine recognizes an excited state from the tone of a user's voice while they are shopping in a virtual store, it will notify the smart glasses of specific product information or discount information, helping the user continue shopping comfortably.

[0728] Generative AI model prompt example

[0729] "Generate a program for a smart glasses app that detects the user's emotional state while shopping and provides relaxing content or special discounts accordingly. As a concrete example, please explain the processing flow when the user is feeling stressed, including the definitions of related classes and methods."

[0730] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0731] Step 1:

[0732] The server starts the system and initializes all sensors and emotion recognition engines. This confirms that each sensor and emotion engine is operating normally and completes the initial setup. The input is the system startup command, and the output is the normal operating status of the sensors and emotion recognition engines.

[0733] Step 2:

[0734] The device starts collecting data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location from the location sensor. At the same time, the emotion recognition engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state. The input is data from each sensor and the emotion recognition engine, and the output is a dataset of pedaling force, speed, location, and emotional state.

[0735] Step 3:

[0736] The terminal sends the data collected to the server. The data is packaged at regular intervals and sent to the server in JSON format. The input is the dataset collected by the terminal, and the output is the JSON data sent to the server.

[0737] Step 4:

[0738] The server analyzes the received data. Specifically, it uses pedaling force, speed, location information, and emotional state to determine the current riding state and the user's emotional state. The server uses data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms. The input is the JSON data sent to the server, and the output is the analyzed riding state and emotional state.

[0739] Step 5:

[0740] The server determines the motor output based on the analysis results. For example, if fatigue is detected on a flat road, the motor output is set low to minimize battery consumption. If the user becomes excited while going uphill, the motor output is increased to provide stronger pedaling support. The input is the analysis result of the riding state and emotional state, and the output is the motor control command.

[0741] Step 6:

[0742] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit. It also notifies the user of specific product information and discount information depending on their emotional state. Furthermore, if the battery level is low, it notifies the user of information about the nearest charging station. The inputs are motor control instructions and notification information, and the outputs are control signals to the motor control unit and notification content to the user.

[0743] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0744] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0745] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0746] [Third embodiment]

[0747] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0748] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0749] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0750] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0751] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0752] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0753] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0754] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0755] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0756] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0757] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0758] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0759] To implement the present invention, a system is required in which each sensor on the electric bicycle acquires data, and the server analyzes and optimizes that data. A specific implementation method for this system is described below.

[0760] System Overview

[0761] The system consists of a pedal force sensor, a speed sensor, a location information sensor (GPS), a server, and a terminal (the control unit of the electric bicycle). These components work together to collect data in real time and optimize motor output according to the user's riding conditions.

[0762] Program processing

[0763] Initialize

[0764] First, the server starts the system and initializes all sensors and control units, which confirms that each sensor is working properly and completes the initial setup.

[0765] Data collection

[0766] Next, the device collects real-time data, specifically the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the GPS sensor. This information is very important and will be used in subsequent analysis steps.

[0767] Data transmission

[0768] The device then sends the collected data to the server, where it is collected in JSON format at regular intervals.

[0769] Data analysis

[0770] The server analyzes the received data and identifies the current riding condition, such as flat road, uphill, or downhill, based on pedaling force, speed, and GPS data.

[0771] Output control decision

[0772] Based on the analysis results, the server determines the motor output. On flat roads, the motor output is set low to minimize battery consumption. On uphill roads, the output is increased to reduce the user's burden.

[0773] Output Transmission

[0774] The server sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time.

[0775] Specific examples

[0776] Scenario 1: Driving on a flat road

[0777] When the user is riding on a flat road, the pedal force sensor measures the user's pedaling force, and the speed sensor measures the speed. Based on this data, the server determines that the road is flat and issues instructions to reduce motor output, thereby minimizing battery consumption.

[0778] Scenario 2: Driving uphill

[0779] When the user is riding uphill, their pedaling force increases and their speed decreases. Based on this data and GPS information, the server determines that they are riding uphill and issues a command to increase motor output. This makes it easier for the user to pedal and climb the hill.

[0780] Battery level notification

[0781] When the battery is low, the server detects this and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to use the electric bicycle more efficiently.

[0782] By using this invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided on various terrains, allowing users to ride the electric bicycle comfortably for long periods of time.

[0783] The processing flow will be explained below.

[0784] Step 1:

[0785] The server initializes the system. The server checks that all sensors and control units are working properly and completes the initial setup. This calibrates each sensor and prepares it for data collection.

[0786] Step 2:

[0787] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor (GPS).

[0788] Step 3:

[0789] The device sends the collected data to the server. Pedaling force, speed, and location data are packaged in JSON format at regular intervals (e.g., every second) and sent to the server in real time.

[0790] Step 4:

[0791] The server analyzes the received data. The server takes in each piece of data and performs analysis to determine the current driving condition (flat road, uphill, downhill, etc.). Analysis methods include data filtering, anomaly detection, and comparison with a terrain database.

[0792] Step 5:

[0793] The server determines the motor output based on the analysis results. On flat roads, the motor output is reduced to minimize battery consumption. On uphill roads, the motor output is increased to reduce the user's pedaling load.

[0794] Step 6:

[0795] The server sends the determined motor output instructions to the terminal, which then transmits the received instructions to the motor control unit, which adjusts the motor output as a specific operation, thereby providing an output that is appropriate for the driving situation in real time.

[0796] Step 7:

[0797] The device notifies the user of the necessary information. Specifically, it displays the remaining battery level, estimated remaining distance, and current power consumption status in real time on the display. When the battery level is low, it also notifies the user of the nearest charging station.

[0798] Step 8:

[0799] The server continues to analyze the data and readjusts the motor output as needed. It periodically collects new data to monitor changes in driving conditions and dynamically reconfigures the motor output. This enables efficient battery management and a comfortable ride even over long periods of use.

[0800] This is the specific processing flow of the electric bicycle system. By implementing this system, users can ride efficiently and comfortably with optimized motor output.

[0801] Example 1

[0802] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0803] With conventional electric bicycles, it is difficult to optimally control motor output according to the user's riding conditions, making it difficult to provide efficient battery consumption and a comfortable riding experience. Furthermore, measures to deal with low battery levels and real-time adjustments according to riding conditions are insufficient. This makes it difficult for users to ride efficiently over various terrains or for long periods of time.

[0804] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0805] In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current position information from a position information sensor, means for analyzing the acquired pedaling force, speed, and position information to identify the current riding state, means for controlling the output of the electric motor based on the riding state, means for sending an instruction to control the motor output to an electric motor control unit, means for notifying the user of information related to the output control of the electric motor, means for the server to initialize each sensor and control unit when the system starts up, and means for the terminal to collect data from the sensors and send it to the server, thereby enabling optimal motor output control according to the user's riding state.

[0806] A "pedal force sensor" is a device that detects the force a user applies to the pedals and collects the data in real time.

[0807] A "speed sensor" is a device that measures the speed at which an electric bicycle is moving and provides speed data.

[0808] A "location sensor" is a device that obtains current location information using technology such as GPS.

[0809] The "server" is a central control unit that analyzes the collected data and generates instructions to control the output of the electric motor based on driving conditions.

[0810] A "terminal" is a control unit installed on an electric bicycle, and is a device that collects data from sensors and communicates data with a server.

[0811] The "electric motor control unit" is a device that adjusts the output of the electric motor based on instructions from the server, supporting the user's driving.

[0812] The "riding state" refers to the current riding conditions of the bicycle (e.g., flat road, uphill, downhill) that are determined based on pedaling force, speed, and position information.

[0813] "Initialization" is the process by which the server configures all sensors and control units to operate normally when starting the system.

[0814] "Data collection" is the process in which the terminal acquires data from each sensor in real time and temporarily stores it in a buffer.

[0815] "Data transmission" is the process in which the terminal transmits collected data to the server at regular intervals.

[0816] "Data analysis" is the process of analyzing the data received by the server to determine the current driving condition.

[0817] "Power control" is the process in which the server determines the power level of the electric motor based on the analysis results and sends instructions to the motor control unit.

[0818] "Notification" is the process of conveying information about the output control of the electric motor to the user.

[0819] The present invention relates to a system that acquires data from various sensors on an electric bicycle, analyzes that data, and optimizes it to optimize motor output according to the user's riding conditions. A specific method for implementing the present invention will be described below.

[0820] System Configuration

[0821] This system consists of the following hardware components:

[0822] Pedal force sensor: Detects the force the user applies to the pedal and collects data.

[0823] Speed ​​sensor: Measures the speed at which the electric bicycle is moving.

[0824] Location sensor (GPS): Obtains current location information.

[0825] Server: Analyzes the collected data and generates control instructions for motor output based on the driving conditions.

[0826] Terminal (control unit): Collects data from each sensor and communicates with the server.

[0827] Electric motor control unit: Adjusts the motor output based on instructions from the server.

[0828] Data acquisition and analysis

[0829] First, the server starts the system and initializes each sensor and control unit. This is the process of configuring each sensor so that they can operate normally. Next, the device collects data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location information from the GPS sensor.

[0830] The device sends the collected data to the server at regular intervals (e.g., every second). The data is sent in JSON format and is imported by the server. The server analyzes the received data and identifies the riding condition (flat road, uphill, downhill, etc.). Pedaling force, speed, and GPS data are used for this analysis.

[0831] Motor output control

[0832] Based on the analysis results, the server determines the output of the electric motor and sends the instructions to the device. For example, it sets the motor output low on flat roads and increases it on uphill slopes. The device transmits this instruction to the electric motor control unit, which adjusts the motor output in real time.

[0833] Battery Management

[0834] When the battery level is low, the server detects this and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to use the electric bicycle more efficiently.

[0835] Specific examples

[0836] Scenario 1: Driving on a flat road

[0837] When the user is riding on a flat road, the pedaling force obtained from the pedal force sensor is low and the speed obtained from the speed sensor is constant. Based on this data, the server determines that the road is flat and issues instructions to reduce motor output, thereby minimizing battery consumption.

[0838] Scenario 2: Driving uphill

[0839] When a user is riding uphill, their pedaling force increases and their speed decreases. Based on this data and GPS information, the server determines that they are riding uphill and issues a command to increase motor output. This makes it easier for the user to pedal, allowing them to climb hills more easily.

[0840] Example prompts for generative AI models

[0841] Below are some prompts you can input to your generative AI model:

[0842] "Explain how an electric bicycle's system detects flat roads and optimizes motor power."

[0843] "Describe the process for increasing motor power for a user traveling uphill."

[0844] "Please explain the procedure to notify the user of the nearest charging station when the battery is low."

[0845] The present invention allows users to ride their electric bicycles comfortably for extended periods of time, extending the battery life of the electric bicycle and providing a comfortable ride on a variety of terrains.

[0846] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0847] Step 1: Initialize

[0848] The server starts the system and initializes each sensor and control unit. Specifically, the server sends an initialization signal and checks that each sensor and control unit is operating normally. The input data is the status information of each sensor, and the output is the state after initial settings are complete. Specifically, the pedal force sensor performs initial calibration, and the speed sensor and GPS sensor obtain initial location and speed data and send it to the server.

[0849] Step 2: Data collection

[0850] The device collects data in real time. The input data is the measurement data from each sensor, and the output data is the data collected by each sensor. Specifically, the device receives the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location information from the GPS sensor, and temporarily stores this data in a buffer. For example, the device obtains the user's pedaling force (N), current speed (km / h), and location information (latitude and longitude) every second.

[0851] Step 3: Send data

[0852] The terminal sends the data it collects to the server. The input data is the sensor measurement data stored on the terminal, and the output data is the data sent to the server. Specifically, the terminal compiles the collected data into transmission packets at regular intervals (for example, every second) and sends them to the server, with the data being sent in JSON format. The server receives this data and stores it in a database.

[0853] Step 4: Data analysis

[0854] The server analyzes the received data and identifies the current riding state. The input data is the sensor measurement data sent to the server, and the output data is the identified riding state. Specifically, data processing and calculations are performed based on pedaling force, speed, and GPS data to determine the riding state, such as flat road, uphill, or downhill. For example, if pedaling force is high and speed is decreasing, it is determined to be an uphill road.

[0855] Step 5: Output control decision

[0856] The server determines the output of the electric motor based on the analysis results. The input data is the identified driving state, and the output data is instructions for motor control. Specifically, the server sets the motor output level according to the driving state. For example, on flat roads, the output is set low to minimize battery consumption, and the output is increased on uphill roads. This optimizes the user's driving.

[0857] Step 6: Send output

[0858] The server sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit. The input data is the motor output instruction sent from the server, and the output data is the output level set in the motor control unit. Specifically, the server sends the generated output instruction to the terminal, and the terminal communicates this instruction to the motor control unit, thereby adjusting the motor output level in real time.

[0859] Step 7: Battery Management

[0860] When the battery level is low, the server detects this and suggests the optimal route, while notifying the user via the device of information about the nearest charging station. The input data is the remaining battery level and current location information, and the output data is the proposed route information and the location information of the charging station. Specifically, the server monitors the remaining battery level, and when it falls below 20%, it references a database of nearby charging stations and calculates the location of the nearest station and the route from the current location. The device notifies the user of this on the display.

[0861] (Application example 1)

[0862] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0863] Autonomous vehicles require the optimization of motor output according to various driving conditions. They also need to suggest efficient routes and provide guidance to charging stations when the battery level is low. This leads to challenges in improving fuel efficiency, driving comfort, and safety.

[0864] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0865] In this invention, the server includes: means for acquiring the user's pedaling force from a pedal force sensor; means for acquiring the current speed from a speed sensor; means for acquiring current position information from a position information sensor; means for analyzing the acquired pedaling force, speed, and position information to identify the current riding state; means for controlling the output of the electric motor based on the riding state; means for sending an instruction to control the motor output to an electric motor control unit; means for notifying the user of information regarding the output control of the electric motor; means for issuing an instruction to reduce the motor output to minimize energy consumption when the vehicle is riding on a flat road; means for calculating the next optimal motor output and adjusting the motor output based on the calculated next optimal motor output when the user's pedal force and riding speed fall below a set standard; means for proposing an optimal route when the battery level is low and notifying the user of information about the nearest charging station; and means for transmitting information regarding optimization of the motor output to the server and adjusting the motor output based on instructions from the server. This enables real-time optimization of the motor output according to riding conditions and efficient route proposals according to the remaining battery level.

[0866] A "pedal force sensor" is a device that measures the force with which a user pedals a bicycle.

[0867] A "speed sensor" is a device that measures the current speed at which a bicycle is traveling.

[0868] A "location information sensor" is a device that measures the current geographical location using GPS or other devices.

[0869] An "electric motor" is a device that converts electrical energy into mechanical power to move a bicycle.

[0870] A "control unit" is a device that analyzes data collected from sensors and provides instructions for adjusting the output of a motor.

[0871] "Real-time analytics" is the process of analyzing data immediately as it is collected.

[0872] "Energy consumption" refers to the power consumed when an electric motor operates.

[0873] "Route suggestion" is the process of showing the optimal travel route based on current location information and driving conditions.

[0874] A "charging station" is a facility where you can replenish power when your battery is low.

[0875] A "server" is a central processing unit that analyzes data and issues instructions.

[0876] "User notification" refers to the action of informing the user of information such as driving status, motor output, and remaining battery power.

[0877] "Optimization" is the process of adjusting to maximize performance under specific conditions.

[0878] The above definitions will enable a clearer understanding of the present invention.

[0879] The present invention relates to a system for optimizing motor output of an autonomous vehicle and performing appropriate control according to the driving situation. Specific embodiments for carrying out the invention will be described below.

[0880] System Overview

[0881] The system consists of a pedal force sensor, a speed sensor, a location information sensor, a server, and a terminal (vehicle control unit). These components collect data in real time and work together to optimize motor output according to the driving conditions of the autonomous vehicle.

[0882] Specific examples of programs and their processing

[0883] First, the server initializes all sensors and control units. This verifies that each sensor is working properly and completes the initial setup. The device collects data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor. This information is very important and will be used in subsequent analysis steps.

[0884] The device then sends the collected data to a server at regular intervals. The data is sent in JSON format and is imported by the server. The server analyzes the received data and identifies the current riding state. Based on pedal force, speed, and location information, the riding state is determined, such as flat road, uphill, or downhill. Based on the analysis results, the server determines the motor output. On flat roads, the motor output is set low to minimize energy consumption. On uphill roads, the output is increased to reduce the user's burden.

[0885] The server then sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit. This allows the motor output to be adjusted in real time. When the battery level is low, the server detects this and suggests an optimal route, while also notifying the user of the nearest charging station. This allows the user to prevent the battery from running out.

[0886] Specific examples

[0887] For example, when the vehicle is running on a flat road, if the user's pedal force is light and the speed is constant, the server keeps the motor output low. This results in less energy consumption and improved fuel efficiency. On the other hand, when going uphill, the pedal force increases and the speed decreases, so the server increases the motor output. This allows the user to climb the slope comfortably.

[0888] Prompt Sentence Examples

[0889] "Judge the current driving conditions (flat road, uphill, etc.) and adjust engine output accordingly. For example, reduce output when driving on flat roads and increase output when driving uphill. Also, if the battery is low, suggest the optimal charging route."

[0890] In this way, this invention enables real-time optimization of motor output according to driving conditions and efficient energy utilization, thereby improving the performance of autonomous vehicles and realizing safe and comfortable driving.

[0891] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0892] Step 1:

[0893] The server initializes the system. The inputs are all the sensors and control units that make up the system. The server configures these to operate normally and verifies that the initial configuration is complete. The output is the status of each sensor and control unit functioning normally. Specifically, the server obtains status information from each sensor and checks whether it is normal.

[0894] Step 2:

[0895] The terminal collects data in real time. The input is data from the pedal force sensor, speed sensor, and location information sensor. The terminal acquires and stores this data. The output is the collected pedal force, speed, and location information data. Specifically, the terminal reads data from each sensor at regular intervals and stores it in temporary storage.

[0896] Step 3:

[0897] The device sends the collected data to the server. The input is pedal force, speed, and location data. The device converts this data into JSON format and sends it to the server. The output is the data sent to the server. Specifically, the device converts the data into an appropriate format (JSON) and sends it to the server using the HTTP protocol, etc.

[0898] Step 4:

[0899] The server analyzes the received data. The input is the pedaling force, speed, and position information data sent from the device. The server analyzes these data and identifies the current riding condition (flat road, uphill, downhill, etc.). The output is the identified riding condition. Specifically, the server analyzes each data point and classifies the riding condition based on the conditions.

[0900] Step 5:

[0901] The server determines the motor output. The input is the current driving state. Based on this state, the server calculates the optimal value of motor output. On flat roads, the output is lower, and on uphill roads, the output is higher. The output is the calculated motor output. In concrete terms, the server uses a specific algorithm to calculate the output value and record it.

[0902] Step 6:

[0903] The server sends output instructions to the device. The input is the calculated motor output. The server sends this instruction to the device, which receives it. The output is the sent output instruction. Specifically, the server converts the calculation result into JSON format and sends it to the device using the HTTP protocol, etc.

[0904] Step 7:

[0905] The terminal transmits instructions to the motor control unit. The input is the motor output instruction received from the server. The terminal transmits the instruction to the motor control unit to adjust the motor output. The output is the adjusted motor output. In specific operations, the terminal sends the received instruction to the motor control unit, and the motor output is adjusted accordingly.

[0906] Step 8:

[0907] When the battery level is low, the server proposes a route. The input is the remaining battery level data. The server searches for the optimal route and sends information about the nearest charging station to the terminal. The output is the proposed route and information about the charging station. Specifically, the server refers to a map database to search for the optimal route and charging station.

[0908] Step 9:

[0909] The terminal notifies the user. The input is the route and charging station information sent from the server. The terminal notifies the user of this information. The output is the information provided to the user. As a specific operation, the terminal conveys information to the user using a notification mechanism (display, audio output, etc.).

[0910] The above steps will enable real-time motor output optimization and efficient energy utilization for autonomous vehicles.

[0911] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0912] The embodiment of the present invention is a system that realizes optimal motor output according to the riding condition and the user's emotions by having the sensors of the electric bicycle and the emotion engine work in coordination. A specific implementation method thereof will be described in detail below.

[0913] System Overview

[0914] This system consists of a pedal force sensor, a speed sensor, a location information sensor (GPS), an emotion engine, a server, and a terminal (the control unit of the electric bicycle). These components collect and analyze data in real time, and optimize motor output according to the user's riding condition and emotions.

[0915] Program processing

[0916] Initialize

[0917] First, the server starts the system and initializes all sensors, control units, and emotion engines. This confirms that each sensor and emotion engine is operating normally and completes the initial setup.

[0918] Data collection

[0919] The device then begins collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the GPS sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[0920] Data transmission

[0921] The device sends the collected data (pedaling force, speed, location information, and emotional state) to the server. The data is packaged in JSON format at regular intervals and sent to the server in real time.

[0922] Data analysis

[0923] The server analyzes the received data. The server takes each piece of data and performs analysis to identify the current driving condition (flat road, uphill, downhill, etc.) and the user's emotional state (excited, tired, etc.). Analysis methods include data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms.

[0924] Output control decision

[0925] Based on the analysis results, the server determines the motor output. If fatigue is detected on flat roads, the motor output is set low to minimize battery consumption and reduce the user's burden. If the user becomes excited on an uphill slope, the motor output is increased to provide more pedaling support.

[0926] Output Transmission

[0927] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time to provide optimal assistance according to the user's emotional state and driving conditions.

[0928] Specific examples

[0929] Scenario 1: Flat road and fatigue

[0930] When the user is driving on a flat road, if the emotion engine recognizes the user's state of fatigue from their facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[0931] Scenario 2: Uphill driving and excitement

[0932] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[0933] Battery level notifications and emotional state

[0934] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[0935] By using the present invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided according to the user's emotional state, allowing the electric bicycle to be used comfortably and efficiently for a long period of time.

[0936] The processing flow will be explained below.

[0937] Step 1:

[0938] The server initializes the system. The server checks that all sensors (pedal force sensor, speed sensor, location information sensor), control unit, and emotion engine are operating normally, and completes the initial setup. This calibrates each sensor and emotion engine, and prepares for data collection.

[0939] Step 2:

[0940] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor (GPS). At the same time, the emotion engine analyzes the user's facial recognition data and voice data to acquire the user's emotional state.

[0941] Step 3:

[0942] The device sends the collected data to the server. The collected data is packaged in JSON format at regular intervals (e.g., every second) and sent to the server in real time. This allows the server to grasp the latest driving and emotional state.

[0943] Step 4:

[0944] The server analyzes the received data. The server takes in each piece of data and determines the current riding condition (flat road, uphill, downhill, etc.) from pedaling force, speed, and location information. It also determines the user's emotional state (excited, tired, etc.) based on data sent from the emotion engine.

[0945] Step 5:

[0946] The server determines the motor output based on the analysis results. For example, if the server determines that the user is fatigued on a flat road, it will set the motor output low to continue supporting the user while minimizing battery consumption. On the other hand, if the server determines that the user is excited on an uphill slope, it will increase the motor output to provide stronger assistance.

[0947] Step 6:

[0948] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time to provide optimal assistance according to the user's emotional state and driving conditions.

[0949] Step 7:

[0950] The device notifies the user of the necessary information. Specifically, the remaining battery level, estimated remaining distance, and current power consumption status are displayed in real time on the screen. When the battery level is low, the device also notifies the user of the nearest charging station, helping the user take appropriate action.

[0951] Step 8:

[0952] The server continues to analyze the data and readjusts the motor output as needed. It periodically collects new data, monitors changes in the driving situation and the user's emotional state, and dynamically reconfigures the motor output. This allows for efficient battery management and a comfortable ride even over long periods of use.

[0953] Specific examples

[0954] Scenario 1: Flat road and fatigue

[0955] When the user is driving on a flat road, if the emotion engine recognizes the user's state of fatigue from their facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[0956] Scenario 2: Uphill driving and excitement

[0957] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[0958] Battery level notifications and emotional state

[0959] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[0960] By using the present invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided according to the user's emotional state, allowing the electric bicycle to be used comfortably and efficiently for a long period of time.

[0961] Example 2

[0962] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0963] Conventional electric bicycle systems do not take into account the user's emotional state, and therefore are unable to provide optimal motor output according to the user's fatigue or excitement level, resulting in insufficient improvements in riding comfort and battery efficiency. In particular, there is a need to reduce user fatigue and battery consumption during long periods of use.

[0964] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current position information from a position information sensor, means for acquiring the user's emotional state from an emotion engine, means for analyzing the acquired pedaling force, speed, position information, and emotional state to identify the current riding state and the user's emotional state, means for controlling the output of the electric motor based on the riding state and the emotional state, means for sending an instruction to control the motor output to an electric motor control unit, and means for notifying the user of information related to the output control of the electric motor. This enables optimal motor output according to the user's riding state and emotional state, thereby improving ride comfort and optimizing battery efficiency.

[0965] A "pedal force sensor" is a sensor that measures the force with which a user pedals a bicycle.

[0966] A "speed sensor" is a sensor for measuring the current speed of an electric bicycle.

[0967] A "location sensor" is a sensor used to measure the current location of an electric bicycle, and typically uses GPS.

[0968] An "emotion engine" is software or hardware that analyzes a user's facial recognition data and voice data to identify the user's emotional state.

[0969] The "server" is a centralized processing device that analyzes data and controls the electric bicycle system.

[0970] The "terminal" is a device that functions as a control unit for the electric bicycle, collects data from various sensors, and communicates with the server.

[0971] The "means for controlling the electric motor output" is a function in which the server generates instructions for adjusting the motor output of the electric bicycle.

[0972] An "electric motor control unit" is the hardware that actually controls the output of the electric bicycle's motor.

[0973] "Means for notifying the user of information related to motor output control" refers to a function that enables a server or terminal to notify the user of the motor output state and changes thereto.

[0974] MODE FOR CARRYING OUT THE INVENTION

[0975] The present invention is a system that realizes optimal motor output according to the riding condition and emotional state of the user by having various sensors mounted on the electric bicycle and an emotion engine work in coordination. A specific implementation method for this system is described in detail below.

[0976] System Configuration

[0977] This system consists of the following hardware and software:

[0978] 1. Pedal force sensor: Measures the force with which the user presses the pedal.

[0979] 2. Speed ​​sensor: Measures the current speed.

[0980] 3. Location sensor (GPS): Measures current location information.

[0981] 4. Emotion Engine: Analyzes the user's facial recognition and voice data to identify their emotional state.

[0982] 5. Server: Analyzes data and generates control instructions.

[0983] 6. Terminal (electric bicycle control unit): Collects data from various sensors and communicates with the server.

[0984] Data collection and transmission

[0985] First, the device starts collecting data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location from the location sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state (e.g., fatigue, excitement, etc.). The collected data is packaged in JSON format at regular intervals and sent from the device to the server in real time.

[0986] Data analysis and output control

[0987] The server analyzes the received data. Specifically, the analysis is performed according to the following procedure.

[0988] 1. Filter each data to remove noise.

[0989] 2. Identify anomalous data using an anomaly detection algorithm.

[0990] 3. The location data is compared with a terrain database to determine the current driving conditions (flat, uphill, downhill, etc.).

[0991] 4. Identify the user's emotional state using emotion recognition algorithms.

[0992] Based on the analysis results, the server determines the motor output and sends the instructions to the device. For example, if the server determines that the user is tired on a flat road, it will set the motor output low. If the server determines that the user is excited on an uphill slope, it will increase the motor output.

[0993] Specific examples

[0994] Specific examples are shown below.

[0995] Scenario 1: Flat road and fatigue

[0996] When the user is driving on a flat road, if the emotion engine recognizes a state of fatigue from the user's facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[0997] Scenario 2: Uphill driving and excitement

[0998] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[0999] Battery level notifications and emotional state

[1000] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[1001] Prompt Sentence Examples

[1002] How does the server detect anomalies when receiving data from the pedal sensor?

[1003]

[1004] How does the server determine motor output when fatigue is detected while driving on a flat road?

[1005]

[1006] How does the server adjust motor power if the user gets excited while going uphill?

[1007] By using this invention, users can extend the battery life of their electric bicycles and ride comfortably with optimal support according to their emotional state.

[1008] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1009] The flow of this system's program processing

[1010] Step 1: Initialize the system

[1011] The server starts the system, initializes all sensors (pedal force sensor, speed sensor, location sensor), control unit, and emotion engine, and checks whether initialization is completed successfully.

[1012] Input: System startup instructions

[1013] Output: Initialization completion status of each sensor and emotion engine

[1014] Step 2: Start collecting data

[1015] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the location sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[1016] Input: Start data acquisition for each sensor

[1017] Output: Pedaling force, speed, position information, emotional state

[1018] Step 3: Packaging the data

[1019] The data collected by the device (pedaling force, speed, location information, and emotional state) is packaged in JSON format at regular intervals.

[1020] Input: Various collected data

[1021] Output: Data packaged in JSON format

[1022] Step 4: Sending data

[1023] The device sends the packaged data to the server, which transmits the data in real time.

[1024] Input: JSON format data package

[1025] Output: Data package sent

[1026] Step 5: Data filtering

[1027] The server takes the received data and removes noise. It uses anomaly detection algorithms to identify and filter out abnormal data.

[1028] Input: Data package sent

[1029] Output: Filtered data

[1030] Step 6: Identify driving conditions

[1031] The server compares the location information with a terrain database to determine the current driving conditions (flat, uphill, downhill, etc.).

[1032] Input: Filtered data

[1033] Output: Driving status information

[1034] Step 7: Identify your emotional state

[1035] The server uses an emotion recognition algorithm to identify the user's emotional state.

[1036] Input: Filtered data

[1037] Output: Emotional state information

[1038] Step 8: Determine the motor power

[1039] The server determines the motor output based on the analysis results. If fatigue is detected on a flat road, the motor output is set low. If excitement is detected on an uphill road, the motor output is increased.

[1040] Input: Driving state and emotional state information

[1041] Output: Motor output indication

[1042] Step 9: Sending motor power instructions

[1043] The server sends the determined motor output instructions to the terminal.

[1044] Input: Motor output instruction

[1045] Output: Motor power output instructions sent

[1046] Step 10: Control and check motor output

[1047] The terminal transmits the motor output instructions received from the server to the motor control unit, adjusting the motor output in real time. The terminal checks whether the motor control unit is operating according to the instructions.

[1048] Input: Motor output instruction

[1049] Output: Regulated motor output, operation check status

[1050] Specific actions

[1051] Scenario 1: While the user is driving on a flat road, the emotion engine recognizes the user's level of fatigue from their facial expression. The device sends the collected data to the server, which analyzes the user's level of fatigue and sends instructions to the device to reduce motor output. The motor control unit follows the instructions and sets the motor output low, providing minimal assistance while saving battery power.

[1052] Scenario 2: When a user is riding uphill, the emotion engine recognizes the user's excitement level from the tone of their voice. The device sends the collected data to the server, which analyzes the excitement level and sends an instruction to the device to increase motor output. The motor control unit increases motor output as instructed, enhancing pedaling assistance.

[1053] (Application example 2)

[1054] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1055] Conventional electric bicycles offer a function to control motor output based on the user's pedaling force, speed, and location information, but they do not optimize the output based on the user's emotional state, which means they are unable to fully realize a comfortable ride. They also lack a system to improve the shopping experience, such as providing recommended product information or discount information based on the user's emotional state. Furthermore, they do not provide appropriate responses when the battery is low. These issues need to be resolved.

[1056] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current location information from a location information sensor, means for acquiring the user's emotional state using an emotion recognition engine for recognizing the emotional state, means for analyzing the acquired pedaling force, speed, location information, and emotional state to identify the current riding state and the user's emotional state, means for controlling the output of the electric motor based on the riding state and the emotional state, means for sending an instruction to control the motor output to an electric motor control unit, means for notifying the user of information related to the output control of the electric motor, means for notifying the user of specific product information or discount information according to the emotional state, and means for notifying the user of information about the nearest charging station when the remaining battery power is low. This improves the user's riding experience, optimizes the shopping experience, and enables efficient battery management.

[1057] A "pedal force sensor" is a device that measures a user's pedaling force in real time.

[1058] A "speed sensor" is a device that acquires the current traveling speed of an electric bicycle.

[1059] A "location information sensor" is a device that uses GPS or other devices to obtain current location information.

[1060] An "emotion recognition engine" is a combination of software and hardware for recognizing a user's emotional state through facial expression analysis and voice analysis.

[1061] The "analysis means" is a system that analyzes the acquired pedaling force, speed, position information, and emotional state, and identifies the riding condition and the emotional state of the user.

[1062] The "electric motor output control means" is a system for appropriately controlling the output of the electric motor based on the driving state and emotional state.

[1063] The "notification means" is a function for notifying the user of information relating to the output control of the electric motor, specific product information, and discount information.

[1064] The "battery remaining amount notification means" is a system for notifying the user of information about the nearest charging station when the battery remaining amount is low.

[1065] The embodiment of the present invention utilizes an emotion recognition engine to obtain the user's emotional state and optimize the electric bicycle and virtual shopping experience. Specific implementation methods are described in detail below.

[1066] The system components include a pedal force sensor, a speed sensor, a location information sensor, an emotion recognition engine, a server, and a terminal (smart glasses or smartphone).

[1067] Program processing overview

[1068] 1. Data Collection

[1069] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the location sensor. In addition, the emotion recognition engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[1070] 2. Data Transmission

[1071] The device sends the collected data to the server. The data is packaged at regular intervals and sent to the server in real time in JSON format.

[1072] 3. Data Analysis

[1073] The server analyzes the received data, specifically using pedaling force, speed, location information, and emotional state to identify the current riding condition and the user's emotional state, using data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms.

[1074] 4. Output control decision

[1075] Based on the analysis results, the server determines the motor output. For example, if the user is fatigued on a flat road, the motor output is set low to minimize battery consumption. On the other hand, if the user is excited on an uphill slope, the motor output is increased to provide more pedaling support.

[1076] 5. Output Transmission and Notification

[1077] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit. The device also notifies the user of specific product information and discount information based on their emotional state. Furthermore, when the battery level is low, it notifies the user of information about the nearest charging station.

[1078] Hardware and software used

[1079] Emotion Recognition Engine: Uses facial recognition camera and voice recognition technology.

[1080] Pedal force sensor, speed sensor, location sensor: Uses standard sensor modules built into the bicycle.

[1081] Server: A high-performance server for large-scale data analysis.

[1082] Terminal (smart glasses, smartphone): A device for collecting data and notifying the user.

[1083] Specific examples

[1084] 1. Driving scenario

[1085] If the emotion engine recognizes a user's fatigue from their facial expression while driving on a flat road, the server will set the motor output low to provide minimal assistance while saving battery power.

[1086] 2. Shopping Scenario

[1087] If the emotion engine recognizes an excited state from the tone of a user's voice while they are shopping in a virtual store, it will notify the smart glasses of specific product information or discount information, helping the user continue shopping comfortably.

[1088] Generative AI model prompt example

[1089] "Generate a program for a smart glasses app that detects the user's emotional state while shopping and provides relaxing content or special discounts accordingly. As a concrete example, please explain the processing flow when the user is feeling stressed, including the definitions of related classes and methods."

[1090] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1091] Step 1:

[1092] The server starts the system and initializes all sensors and emotion recognition engines. This confirms that each sensor and emotion engine is operating normally and completes the initial setup. The input is the system startup command, and the output is the normal operating status of the sensors and emotion recognition engines.

[1093] Step 2:

[1094] The device starts collecting data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location from the location sensor. At the same time, the emotion recognition engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state. The input is data from each sensor and the emotion recognition engine, and the output is a dataset of pedaling force, speed, location, and emotional state.

[1095] Step 3:

[1096] The terminal sends the data collected to the server. The data is packaged at regular intervals and sent to the server in JSON format. The input is the dataset collected by the terminal, and the output is the JSON data sent to the server.

[1097] Step 4:

[1098] The server analyzes the received data. Specifically, it uses pedaling force, speed, location information, and emotional state to determine the current riding state and the user's emotional state. The server uses data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms. The input is the JSON data sent to the server, and the output is the analyzed riding state and emotional state.

[1099] Step 5:

[1100] The server determines the motor output based on the analysis results. For example, if fatigue is detected on a flat road, the motor output is set low to minimize battery consumption. If the user becomes excited while going uphill, the motor output is increased to provide stronger pedaling support. The input is the analysis result of the riding state and emotional state, and the output is the motor control command.

[1101] Step 6:

[1102] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit. It also notifies the user of specific product information and discount information depending on their emotional state. Furthermore, if the battery level is low, it notifies the user of information about the nearest charging station. The inputs are motor control instructions and notification information, and the outputs are control signals to the motor control unit and notification content to the user.

[1103] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1105] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1106] [Fourth embodiment]

[1107] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1108] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1110] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1111] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1114] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1115] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1116] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1118] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1119] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1120] To implement the present invention, a system is required in which each sensor on the electric bicycle acquires data, and the server analyzes and optimizes that data. A specific implementation method for this system is described below.

[1121] System Overview

[1122] The system consists of a pedal force sensor, a speed sensor, a location information sensor (GPS), a server, and a terminal (the control unit of the electric bicycle). These components work together to collect data in real time and optimize motor output according to the user's riding conditions.

[1123] Program processing

[1124] Initialize

[1125] First, the server starts the system and initializes all sensors and control units, which confirms that each sensor is working properly and completes the initial setup.

[1126] Data collection

[1127] Next, the device collects real-time data, specifically the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the GPS sensor. This information is very important and will be used in subsequent analysis steps.

[1128] Data transmission

[1129] The device then sends the collected data to the server, where it is collected in JSON format at regular intervals.

[1130] Data analysis

[1131] The server analyzes the received data and identifies the current riding condition, such as flat road, uphill, or downhill, based on pedaling force, speed, and GPS data.

[1132] Output control decision

[1133] Based on the analysis results, the server determines the motor output. On flat roads, the motor output is set low to minimize battery consumption. On uphill roads, the output is increased to reduce the user's burden.

[1134] Output Transmission

[1135] The server sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time.

[1136] Specific examples

[1137] Scenario 1: Driving on a flat road

[1138] When the user is riding on a flat road, the pedal force sensor measures the user's pedaling force, and the speed sensor measures the speed. Based on this data, the server determines that the road is flat and issues instructions to reduce motor output, thereby minimizing battery consumption.

[1139] Scenario 2: Driving uphill

[1140] When the user is riding uphill, their pedaling force increases and their speed decreases. Based on this data and GPS information, the server determines that they are riding uphill and issues a command to increase motor output. This makes it easier for the user to pedal and climb the hill.

[1141] Battery level notification

[1142] When the battery is low, the server detects this and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to use the electric bicycle more efficiently.

[1143] By using this invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided on various terrains, allowing users to ride the electric bicycle comfortably for long periods of time.

[1144] The processing flow will be explained below.

[1145] Step 1:

[1146] The server initializes the system. The server checks that all sensors and control units are working properly and completes the initial setup. This calibrates each sensor and prepares it for data collection.

[1147] Step 2:

[1148] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor (GPS).

[1149] Step 3:

[1150] The device sends the collected data to the server. Pedaling force, speed, and location data are packaged in JSON format at regular intervals (e.g., every second) and sent to the server in real time.

[1151] Step 4:

[1152] The server analyzes the received data. The server takes in each piece of data and performs analysis to determine the current driving condition (flat road, uphill, downhill, etc.). Analysis methods include data filtering, anomaly detection, and comparison with a terrain database.

[1153] Step 5:

[1154] The server determines the motor output based on the analysis results. On flat roads, the motor output is reduced to minimize battery consumption. On uphill roads, the motor output is increased to reduce the user's pedaling load.

[1155] Step 6:

[1156] The server sends the determined motor output instructions to the terminal, which then transmits the received instructions to the motor control unit, which adjusts the motor output as a specific operation, thereby providing an output that is appropriate for the driving situation in real time.

[1157] Step 7:

[1158] The device notifies the user of the necessary information. Specifically, it displays the remaining battery level, estimated remaining distance, and current power consumption status in real time on the display. When the battery level is low, it also notifies the user of the nearest charging station.

[1159] Step 8:

[1160] The server continues to analyze the data and readjusts the motor output as needed. It periodically collects new data to monitor changes in driving conditions and dynamically reconfigures the motor output. This enables efficient battery management and a comfortable ride even over long periods of use.

[1161] This is the specific processing flow of the electric bicycle system. By implementing this system, users can ride efficiently and comfortably with optimized motor output.

[1162] Example 1

[1163] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1164] With conventional electric bicycles, it is difficult to optimally control motor output according to the user's riding conditions, making it difficult to provide efficient battery consumption and a comfortable riding experience. Furthermore, measures to deal with low battery levels and real-time adjustments according to riding conditions are insufficient. This makes it difficult for users to ride efficiently over various terrains or for long periods of time.

[1165] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1166] In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current position information from a position information sensor, means for analyzing the acquired pedaling force, speed, and position information to identify the current riding state, means for controlling the output of the electric motor based on the riding state, means for sending an instruction to control the motor output to an electric motor control unit, means for notifying the user of information related to the output control of the electric motor, means for the server to initialize each sensor and control unit when the system starts up, and means for the terminal to collect data from the sensors and send it to the server, thereby enabling optimal motor output control according to the user's riding state.

[1167] A "pedal force sensor" is a device that detects the force a user applies to the pedals and collects the data in real time.

[1168] A "speed sensor" is a device that measures the speed at which an electric bicycle is moving and provides speed data.

[1169] A "location sensor" is a device that obtains current location information using technology such as GPS.

[1170] The "server" is a central control unit that analyzes the collected data and generates instructions to control the output of the electric motor based on driving conditions.

[1171] A "terminal" is a control unit installed on an electric bicycle, and is a device that collects data from sensors and communicates data with a server.

[1172] The "electric motor control unit" is a device that adjusts the output of the electric motor based on instructions from the server, supporting the user's driving.

[1173] The "riding state" refers to the current riding conditions of the bicycle (e.g., flat road, uphill, downhill) that are determined based on pedaling force, speed, and position information.

[1174] "Initialization" is the process by which the server configures all sensors and control units to operate normally when starting the system.

[1175] "Data collection" is the process in which the terminal acquires data from each sensor in real time and temporarily stores it in a buffer.

[1176] "Data transmission" is the process in which the terminal transmits collected data to the server at regular intervals.

[1177] "Data analysis" is the process of analyzing the data received by the server to determine the current driving condition.

[1178] "Power control" is the process in which the server determines the power level of the electric motor based on the analysis results and sends instructions to the motor control unit.

[1179] "Notification" is the process of conveying information about the output control of the electric motor to the user.

[1180] The present invention relates to a system that acquires data from various sensors on an electric bicycle, analyzes that data, and optimizes it to optimize motor output according to the user's riding conditions. A specific method for implementing the present invention will be described below.

[1181] System Configuration

[1182] This system consists of the following hardware components:

[1183] Pedal force sensor: Detects the force the user applies to the pedal and collects data.

[1184] Speed ​​sensor: Measures the speed at which the electric bicycle is moving.

[1185] Location sensor (GPS): Obtains current location information.

[1186] Server: Analyzes the collected data and generates control instructions for motor output based on the driving conditions.

[1187] Terminal (control unit): Collects data from each sensor and communicates with the server.

[1188] Electric motor control unit: Adjusts the motor output based on instructions from the server.

[1189] Data acquisition and analysis

[1190] First, the server starts the system and initializes each sensor and control unit. This is the process of configuring each sensor so that they can operate normally. Next, the device collects data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location information from the GPS sensor.

[1191] The device sends the collected data to the server at regular intervals (e.g., every second). The data is sent in JSON format and is imported by the server. The server analyzes the received data and identifies the riding condition (flat road, uphill, downhill, etc.). Pedaling force, speed, and GPS data are used for this analysis.

[1192] Motor output control

[1193] Based on the analysis results, the server determines the output of the electric motor and sends the instructions to the device. For example, it sets the motor output low on flat roads and increases it on uphill slopes. The device transmits this instruction to the electric motor control unit, which adjusts the motor output in real time.

[1194] Battery Management

[1195] When the battery level is low, the server detects this and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to use the electric bicycle more efficiently.

[1196] Specific examples

[1197] Scenario 1: Driving on a flat road

[1198] When the user is riding on a flat road, the pedaling force obtained from the pedal force sensor is low and the speed obtained from the speed sensor is constant. Based on this data, the server determines that the road is flat and issues instructions to reduce motor output, thereby minimizing battery consumption.

[1199] Scenario 2: Driving uphill

[1200] When a user is riding uphill, their pedaling force increases and their speed decreases. Based on this data and GPS information, the server determines that they are riding uphill and issues a command to increase motor output. This makes it easier for the user to pedal, allowing them to climb hills more easily.

[1201] Example prompts for generative AI models

[1202] Below are some prompts you can input to your generative AI model:

[1203] "Explain how an electric bicycle's system detects flat roads and optimizes motor power."

[1204] "Describe the process for increasing motor power for a user traveling uphill."

[1205] "Please explain the procedure to notify the user of the nearest charging station when the battery is low."

[1206] The present invention allows users to ride their electric bicycles comfortably for extended periods of time, extending the battery life of the electric bicycle and providing a comfortable ride on a variety of terrains.

[1207] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1208] Step 1: Initialize

[1209] The server starts the system and initializes each sensor and control unit. Specifically, the server sends an initialization signal and checks that each sensor and control unit is operating normally. The input data is the status information of each sensor, and the output is the state after initial settings are complete. Specifically, the pedal force sensor performs initial calibration, and the speed sensor and GPS sensor obtain initial location and speed data and send it to the server.

[1210] Step 2: Data collection

[1211] The device collects data in real time. The input data is the measurement data from each sensor, and the output data is the data collected by each sensor. Specifically, the device receives the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location information from the GPS sensor, and temporarily stores this data in a buffer. For example, the device obtains the user's pedaling force (N), current speed (km / h), and location information (latitude and longitude) every second.

[1212] Step 3: Send data

[1213] The terminal sends the data it collects to the server. The input data is the sensor measurement data stored on the terminal, and the output data is the data sent to the server. Specifically, the terminal compiles the collected data into transmission packets at regular intervals (for example, every second) and sends them to the server, with the data being sent in JSON format. The server receives this data and stores it in a database.

[1214] Step 4: Data analysis

[1215] The server analyzes the received data and identifies the current riding state. The input data is the sensor measurement data sent to the server, and the output data is the identified riding state. Specifically, data processing and calculations are performed based on pedaling force, speed, and GPS data to determine the riding state, such as flat road, uphill, or downhill. For example, if pedaling force is high and speed is decreasing, it is determined to be an uphill road.

[1216] Step 5: Output control decision

[1217] The server determines the output of the electric motor based on the analysis results. The input data is the identified driving state, and the output data is instructions for motor control. Specifically, the server sets the motor output level according to the driving state. For example, on flat roads, the output is set low to minimize battery consumption, and the output is increased on uphill roads. This optimizes the user's driving.

[1218] Step 6: Send output

[1219] The server sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit. The input data is the motor output instruction sent from the server, and the output data is the output level set in the motor control unit. Specifically, the server sends the generated output instruction to the terminal, and the terminal communicates this instruction to the motor control unit, thereby adjusting the motor output level in real time.

[1220] Step 7: Battery Management

[1221] When the battery level is low, the server detects this and suggests the optimal route, while notifying the user via the device of information about the nearest charging station. The input data is the remaining battery level and current location information, and the output data is the proposed route information and the location information of the charging station. Specifically, the server monitors the remaining battery level, and when it falls below 20%, it references a database of nearby charging stations and calculates the location of the nearest station and the route from the current location. The device notifies the user of this on the display.

[1222] (Application example 1)

[1223] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1224] Autonomous vehicles require the optimization of motor output according to various driving conditions. They also need to suggest efficient routes and provide guidance to charging stations when the battery level is low. This leads to challenges in improving fuel efficiency, driving comfort, and safety.

[1225] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1226] In this invention, the server includes: means for acquiring the user's pedaling force from a pedal force sensor; means for acquiring the current speed from a speed sensor; means for acquiring current position information from a position information sensor; means for analyzing the acquired pedaling force, speed, and position information to identify the current riding state; means for controlling the output of the electric motor based on the riding state; means for sending an instruction to control the motor output to an electric motor control unit; means for notifying the user of information regarding the output control of the electric motor; means for issuing an instruction to reduce the motor output to minimize energy consumption when the vehicle is riding on a flat road; means for calculating the next optimal motor output and adjusting the motor output based on the calculated next optimal motor output when the user's pedal force and riding speed fall below a set standard; means for proposing an optimal route when the battery level is low and notifying the user of information about the nearest charging station; and means for transmitting information regarding optimization of the motor output to the server and adjusting the motor output based on instructions from the server. This enables real-time optimization of the motor output according to riding conditions and efficient route proposals according to the remaining battery level.

[1227] A "pedal force sensor" is a device that measures the force with which a user pedals a bicycle.

[1228] A "speed sensor" is a device that measures the current speed at which a bicycle is traveling.

[1229] A "location information sensor" is a device that measures the current geographical location using GPS or other devices.

[1230] An "electric motor" is a device that converts electrical energy into mechanical power to move a bicycle.

[1231] A "control unit" is a device that analyzes data collected from sensors and provides instructions for adjusting the output of a motor.

[1232] "Real-time analytics" is the process of analyzing data immediately as it is collected.

[1233] "Energy consumption" refers to the power consumed when an electric motor operates.

[1234] "Route suggestion" is the process of showing the optimal travel route based on current location information and driving conditions.

[1235] A "charging station" is a facility where you can replenish power when your battery is low.

[1236] A "server" is a central processing unit that analyzes data and issues instructions.

[1237] "User notification" refers to the action of informing the user of information such as driving status, motor output, and remaining battery power.

[1238] "Optimization" is the process of adjusting to maximize performance under specific conditions.

[1239] The above definitions will enable a clearer understanding of the present invention.

[1240] The present invention relates to a system for optimizing motor output of an autonomous vehicle and performing appropriate control according to the driving situation. Specific embodiments for carrying out the invention will be described below.

[1241] System Overview

[1242] The system consists of a pedal force sensor, a speed sensor, a location information sensor, a server, and a terminal (vehicle control unit). These components collect data in real time and work together to optimize motor output according to the driving conditions of the autonomous vehicle.

[1243] Specific examples of programs and their processing

[1244] First, the server initializes all sensors and control units. This verifies that each sensor is working properly and completes the initial setup. The device collects data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor. This information is very important and will be used in subsequent analysis steps.

[1245] The device then sends the collected data to a server at regular intervals. The data is sent in JSON format and is imported by the server. The server analyzes the received data and identifies the current riding state. Based on pedal force, speed, and location information, the riding state is determined, such as flat road, uphill, or downhill. Based on the analysis results, the server determines the motor output. On flat roads, the motor output is set low to minimize energy consumption. On uphill roads, the output is increased to reduce the user's burden.

[1246] The server then sends the determined output instructions to the terminal, which then transmits the instructions to the motor control unit. This allows the motor output to be adjusted in real time. When the battery level is low, the server detects this and suggests an optimal route, while also notifying the user of the nearest charging station. This allows the user to prevent the battery from running out.

[1247] Specific examples

[1248] For example, when the vehicle is running on a flat road, if the user's pedal force is light and the speed is constant, the server keeps the motor output low. This results in less energy consumption and improved fuel efficiency. On the other hand, when going uphill, the pedal force increases and the speed decreases, so the server increases the motor output. This allows the user to climb the slope comfortably.

[1249] Prompt Sentence Examples

[1250] "Judge the current driving conditions (flat road, uphill, etc.) and adjust engine output accordingly. For example, reduce output when driving on flat roads and increase output when driving uphill. Also, if the battery is low, suggest the optimal charging route."

[1251] In this way, this invention enables real-time optimization of motor output according to driving conditions and efficient energy utilization, thereby improving the performance of autonomous vehicles and realizing safe and comfortable driving.

[1252] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1253] Step 1:

[1254] The server initializes the system. The inputs are all the sensors and control units that make up the system. The server configures these to operate normally and verifies that the initial configuration is complete. The output is the status of each sensor and control unit functioning normally. Specifically, the server obtains status information from each sensor and checks whether it is normal.

[1255] Step 2:

[1256] The terminal collects data in real time. The input is data from the pedal force sensor, speed sensor, and location information sensor. The terminal acquires and stores this data. The output is the collected pedal force, speed, and location information data. Specifically, the terminal reads data from each sensor at regular intervals and stores it in temporary storage.

[1257] Step 3:

[1258] The device sends the collected data to the server. The input is pedal force, speed, and location data. The device converts this data into JSON format and sends it to the server. The output is the data sent to the server. Specifically, the device converts the data into an appropriate format (JSON) and sends it to the server using the HTTP protocol, etc.

[1259] Step 4:

[1260] The server analyzes the received data. The input is the pedaling force, speed, and position information data sent from the device. The server analyzes these data and identifies the current riding condition (flat road, uphill, downhill, etc.). The output is the identified riding condition. Specifically, the server analyzes each data point and classifies the riding condition based on the conditions.

[1261] Step 5:

[1262] The server determines the motor output. The input is the current driving state. Based on this state, the server calculates the optimal value of motor output. On flat roads, the output is lower, and on uphill roads, the output is higher. The output is the calculated motor output. In concrete terms, the server uses a specific algorithm to calculate the output value and record it.

[1263] Step 6:

[1264] The server sends output instructions to the device. The input is the calculated motor output. The server sends this instruction to the device, which receives it. The output is the sent output instruction. Specifically, the server converts the calculation result into JSON format and sends it to the device using the HTTP protocol, etc.

[1265] Step 7:

[1266] The terminal transmits instructions to the motor control unit. The input is the motor output instruction received from the server. The terminal transmits the instruction to the motor control unit to adjust the motor output. The output is the adjusted motor output. In specific operations, the terminal sends the received instruction to the motor control unit, and the motor output is adjusted accordingly.

[1267] Step 8:

[1268] When the battery level is low, the server proposes a route. The input is the remaining battery level data. The server searches for the optimal route and sends information about the nearest charging station to the terminal. The output is the proposed route and information about the charging station. Specifically, the server refers to a map database to search for the optimal route and charging station.

[1269] Step 9:

[1270] The terminal notifies the user. The input is the route and charging station information sent from the server. The terminal notifies the user of this information. The output is the information provided to the user. As a specific operation, the terminal conveys information to the user using a notification mechanism (display, audio output, etc.).

[1271] The above steps will enable real-time motor output optimization and efficient energy utilization for autonomous vehicles.

[1272] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1273] The embodiment of the present invention is a system that realizes optimal motor output according to the riding condition and the user's emotions by having the sensors of the electric bicycle and the emotion engine work in coordination. A specific implementation method thereof will be described in detail below.

[1274] System Overview

[1275] This system consists of a pedal force sensor, a speed sensor, a location information sensor (GPS), an emotion engine, a server, and a terminal (the control unit of the electric bicycle). These components collect and analyze data in real time, and optimize motor output according to the user's riding condition and emotions.

[1276] Program processing

[1277] Initialize

[1278] First, the server starts the system and initializes all sensors, control units, and emotion engines. This confirms that each sensor and emotion engine is operating normally and completes the initial setup.

[1279] Data collection

[1280] The device then begins collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the GPS sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[1281] Data transmission

[1282] The device sends the collected data (pedaling force, speed, location information, and emotional state) to the server. The data is packaged in JSON format at regular intervals and sent to the server in real time.

[1283] Data analysis

[1284] The server analyzes the received data. The server takes each piece of data and performs analysis to identify the current driving condition (flat road, uphill, downhill, etc.) and the user's emotional state (excited, tired, etc.). Analysis methods include data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms.

[1285] Output control decision

[1286] Based on the analysis results, the server determines the motor output. If fatigue is detected on flat roads, the motor output is set low to minimize battery consumption and reduce the user's burden. If the user becomes excited on an uphill slope, the motor output is increased to provide more pedaling support.

[1287] Output Transmission

[1288] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time to provide optimal assistance according to the user's emotional state and driving conditions.

[1289] Specific examples

[1290] Scenario 1: Flat road and fatigue

[1291] When the user is driving on a flat road, if the emotion engine recognizes the user's state of fatigue from their facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[1292] Scenario 2: Uphill driving and excitement

[1293] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[1294] Battery level notifications and emotional state

[1295] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[1296] By using the present invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided according to the user's emotional state, allowing the electric bicycle to be used comfortably and efficiently for a long period of time.

[1297] The processing flow will be explained below.

[1298] Step 1:

[1299] The server initializes the system. The server checks that all sensors (pedal force sensor, speed sensor, location information sensor), control unit, and emotion engine are operating normally, and completes the initial setup. This calibrates each sensor and emotion engine, and prepares for data collection.

[1300] Step 2:

[1301] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, the current speed from the speed sensor, and the current location from the location sensor (GPS). At the same time, the emotion engine analyzes the user's facial recognition data and voice data to acquire the user's emotional state.

[1302] Step 3:

[1303] The device sends the collected data to the server. The collected data is packaged in JSON format at regular intervals (e.g., every second) and sent to the server in real time. This allows the server to grasp the latest driving and emotional state.

[1304] Step 4:

[1305] The server analyzes the received data. The server takes in each piece of data and determines the current riding condition (flat road, uphill, downhill, etc.) from pedaling force, speed, and location information. It also determines the user's emotional state (excited, tired, etc.) based on data sent from the emotion engine.

[1306] Step 5:

[1307] The server determines the motor output based on the analysis results. For example, if the server determines that the user is fatigued on a flat road, it will set the motor output low to continue supporting the user while minimizing battery consumption. On the other hand, if the server determines that the user is excited on an uphill slope, it will increase the motor output to provide stronger assistance.

[1308] Step 6:

[1309] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit, which adjusts the motor output in real time to provide optimal assistance according to the user's emotional state and driving conditions.

[1310] Step 7:

[1311] The device notifies the user of the necessary information. Specifically, the remaining battery level, estimated remaining distance, and current power consumption status are displayed in real time on the screen. When the battery level is low, the device also notifies the user of the nearest charging station, helping the user take appropriate action.

[1312] Step 8:

[1313] The server continues to analyze the data and readjusts the motor output as needed. It periodically collects new data, monitors changes in the driving situation and the user's emotional state, and dynamically reconfigures the motor output. This allows for efficient battery management and a comfortable ride even over long periods of use.

[1314] Specific examples

[1315] Scenario 1: Flat road and fatigue

[1316] When the user is driving on a flat road, if the emotion engine recognizes the user's state of fatigue from their facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[1317] Scenario 2: Uphill driving and excitement

[1318] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[1319] Battery level notifications and emotional state

[1320] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[1321] By using the present invention, the battery life of an electric bicycle can be extended and a comfortable ride can be provided according to the user's emotional state, allowing the electric bicycle to be used comfortably and efficiently for a long period of time.

[1322] Example 2

[1323] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1324] Conventional electric bicycle systems do not take into account the user's emotional state, and therefore are unable to provide optimal motor output according to the user's fatigue or excitement level, resulting in insufficient improvements in riding comfort and battery efficiency. In particular, there is a need to reduce user fatigue and battery consumption during long periods of use.

[1325] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current position information from a position information sensor, means for acquiring the user's emotional state from an emotion engine, means for analyzing the acquired pedaling force, speed, position information, and emotional state to identify the current riding state and the user's emotional state, means for controlling the output of the electric motor based on the riding state and the emotional state, means for sending an instruction to control the motor output to an electric motor control unit, and means for notifying the user of information related to the output control of the electric motor. This enables optimal motor output according to the user's riding state and emotional state, thereby improving ride comfort and optimizing battery efficiency.

[1326] A "pedal force sensor" is a sensor that measures the force with which a user pedals a bicycle.

[1327] A "speed sensor" is a sensor for measuring the current speed of an electric bicycle.

[1328] A "location sensor" is a sensor used to measure the current location of an electric bicycle, and typically uses GPS.

[1329] An "emotion engine" is software or hardware that analyzes a user's facial recognition data and voice data to identify the user's emotional state.

[1330] The "server" is a centralized processing device that analyzes data and controls the electric bicycle system.

[1331] The "terminal" is a device that functions as a control unit for the electric bicycle, collects data from various sensors, and communicates with the server.

[1332] The "means for controlling the electric motor output" is a function in which the server generates instructions for adjusting the motor output of the electric bicycle.

[1333] An "electric motor control unit" is the hardware that actually controls the output of the electric bicycle's motor.

[1334] "Means for notifying the user of information related to motor output control" refers to a function that enables a server or terminal to notify the user of the motor output state and changes thereto.

[1335] MODE FOR CARRYING OUT THE INVENTION

[1336] The present invention is a system that realizes optimal motor output according to the riding condition and emotional state of the user by having various sensors mounted on the electric bicycle and an emotion engine work in coordination. A specific implementation method for this system is described in detail below.

[1337] System Configuration

[1338] This system consists of the following hardware and software:

[1339] 1. Pedal force sensor: Measures the force with which the user presses the pedal.

[1340] 2. Speed ​​sensor: Measures the current speed.

[1341] 3. Location sensor (GPS): Measures current location information.

[1342] 4. Emotion Engine: Analyzes the user's facial recognition and voice data to identify their emotional state.

[1343] 5. Server: Analyzes data and generates control instructions.

[1344] 6. Terminal (electric bicycle control unit): Collects data from various sensors and communicates with the server.

[1345] Data collection and transmission

[1346] First, the device starts collecting data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location from the location sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state (e.g., fatigue, excitement, etc.). The collected data is packaged in JSON format at regular intervals and sent from the device to the server in real time.

[1347] Data analysis and output control

[1348] The server analyzes the received data. Specifically, the analysis is performed according to the following procedure.

[1349] 1. Filter each data to remove noise.

[1350] 2. Identify anomalous data using an anomaly detection algorithm.

[1351] 3. The location data is compared with a terrain database to determine the current driving conditions (flat, uphill, downhill, etc.).

[1352] 4. Identify the user's emotional state using emotion recognition algorithms.

[1353] Based on the analysis results, the server determines the motor output and sends the instructions to the device. For example, if the server determines that the user is tired on a flat road, it will set the motor output low. If the server determines that the user is excited on an uphill slope, it will increase the motor output.

[1354] Specific examples

[1355] Specific examples are shown below.

[1356] Scenario 1: Flat road and fatigue

[1357] When the user is driving on a flat road, if the emotion engine recognizes a state of fatigue from the user's facial expression, the server will reduce motor output based on this, providing minimal assistance while saving battery power.

[1358] Scenario 2: Uphill driving and excitement

[1359] When the user is riding uphill, if the emotion engine detects excitement from the user's tone of voice, the server will increase the motor output and provide stronger assistance, allowing the user to climb uphill comfortably.

[1360] Battery level notifications and emotional state

[1361] If the user is tired and the battery is low, the server detects this information and suggests the optimal route, while also notifying the user via the device of the nearest charging station, allowing the user to ride the electric bicycle with peace of mind.

[1362] Prompt Sentence Examples

[1363] How does the server detect anomalies when receiving data from the pedal sensor?

[1364]

[1365] How does the server determine motor output when fatigue is detected while driving on a flat road?

[1366]

[1367] How does the server adjust motor power if the user gets excited while going uphill?

[1368] By using this invention, users can extend the battery life of their electric bicycles and ride comfortably with optimal support according to their emotional state.

[1369] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1370] The flow of this system's program processing

[1371] Step 1: Initialize the system

[1372] The server starts the system, initializes all sensors (pedal force sensor, speed sensor, location sensor), control unit, and emotion engine, and checks whether initialization is completed successfully.

[1373] Input: System startup instructions

[1374] Output: Initialization completion status of each sensor and emotion engine

[1375] Step 2: Start collecting data

[1376] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the location sensor. At the same time, the emotion engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[1377] Input: Start data acquisition for each sensor

[1378] Output: Pedaling force, speed, position information, emotional state

[1379] Step 3: Packaging the data

[1380] The data collected by the device (pedaling force, speed, location information, and emotional state) is packaged in JSON format at regular intervals.

[1381] Input: Various collected data

[1382] Output: Data packaged in JSON format

[1383] Step 4: Sending data

[1384] The device sends the packaged data to the server, which transmits the data in real time.

[1385] Input: JSON format data package

[1386] Output: Data package sent

[1387] Step 5: Data filtering

[1388] The server takes the received data and removes noise. It uses anomaly detection algorithms to identify and filter out abnormal data.

[1389] Input: Data package sent

[1390] Output: Filtered data

[1391] Step 6: Identify driving conditions

[1392] The server compares the location information with a terrain database to determine the current driving conditions (flat, uphill, downhill, etc.).

[1393] Input: Filtered data

[1394] Output: Driving status information

[1395] Step 7: Identify your emotional state

[1396] The server uses an emotion recognition algorithm to identify the user's emotional state.

[1397] Input: Filtered data

[1398] Output: Emotional state information

[1399] Step 8: Determine the motor power

[1400] The server determines the motor output based on the analysis results. If fatigue is detected on a flat road, the motor output is set low. If excitement is detected on an uphill road, the motor output is increased.

[1401] Input: Driving state and emotional state information

[1402] Output: Motor output indication

[1403] Step 9: Sending motor power instructions

[1404] The server sends the determined motor output instructions to the terminal.

[1405] Input: Motor output instruction

[1406] Output: Motor power output instructions sent

[1407] Step 10: Control and check motor output

[1408] The terminal transmits the motor output instructions received from the server to the motor control unit, adjusting the motor output in real time. The terminal checks whether the motor control unit is operating according to the instructions.

[1409] Input: Motor output instruction

[1410] Output: Regulated motor output, operation check status

[1411] Specific actions

[1412] Scenario 1: While the user is driving on a flat road, the emotion engine recognizes the user's level of fatigue from their facial expression. The device sends the collected data to the server, which analyzes the user's level of fatigue and sends instructions to the device to reduce motor output. The motor control unit follows the instructions and sets the motor output low, providing minimal assistance while saving battery power.

[1413] Scenario 2: When a user is riding uphill, the emotion engine recognizes the user's excitement level from the tone of their voice. The device sends the collected data to the server, which analyzes the excitement level and sends an instruction to the device to increase motor output. The motor control unit increases motor output as instructed, enhancing pedaling assistance.

[1414] (Application example 2)

[1415] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1416] Conventional electric bicycles offer a function to control motor output based on the user's pedaling force, speed, and location information, but they do not optimize the output based on the user's emotional state, which means they are unable to fully realize a comfortable ride. They also lack a system to improve the shopping experience, such as providing recommended product information or discount information based on the user's emotional state. Furthermore, they do not provide appropriate responses when the battery is low. These issues need to be resolved.

[1417] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's pedaling force from a pedal force sensor, means for acquiring the current speed from a speed sensor, means for acquiring current location information from a location information sensor, means for acquiring the user's emotional state using an emotion recognition engine for recognizing the emotional state, means for analyzing the acquired pedaling force, speed, location information, and emotional state to identify the current riding state and the user's emotional state, means for controlling the output of the electric motor based on the riding state and the emotional state, means for sending an instruction to control the motor output to an electric motor control unit, means for notifying the user of information related to the output control of the electric motor, means for notifying the user of specific product information or discount information according to the emotional state, and means for notifying the user of information about the nearest charging station when the remaining battery power is low. This improves the user's riding experience, optimizes the shopping experience, and enables efficient battery management.

[1418] A "pedal force sensor" is a device that measures a user's pedaling force in real time.

[1419] A "speed sensor" is a device that acquires the current traveling speed of an electric bicycle.

[1420] A "location information sensor" is a device that uses GPS or other devices to obtain current location information.

[1421] An "emotion recognition engine" is a combination of software and hardware for recognizing a user's emotional state through facial expression analysis and voice analysis.

[1422] The "analysis means" is a system that analyzes the acquired pedaling force, speed, position information, and emotional state, and identifies the riding condition and the emotional state of the user.

[1423] The "electric motor output control means" is a system for appropriately controlling the output of the electric motor based on the driving state and emotional state.

[1424] The "notification means" is a function for notifying the user of information relating to the output control of the electric motor, specific product information, and discount information.

[1425] The "battery remaining amount notification means" is a system for notifying the user of information about the nearest charging station when the battery remaining amount is low.

[1426] The embodiment of the present invention utilizes an emotion recognition engine to obtain the user's emotional state and optimize the electric bicycle and virtual shopping experience. Specific implementation methods are described in detail below.

[1427] The system components include a pedal force sensor, a speed sensor, a location information sensor, an emotion recognition engine, a server, and a terminal (smart glasses or smartphone).

[1428] Program processing overview

[1429] 1. Data Collection

[1430] The device starts collecting data in real time. Specifically, it acquires the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location information from the location sensor. In addition, the emotion recognition engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state.

[1431] 2. Data Transmission

[1432] The device sends the collected data to the server. The data is packaged at regular intervals and sent to the server in real time in JSON format.

[1433] 3. Data Analysis

[1434] The server analyzes the received data, specifically using pedaling force, speed, location information, and emotional state to identify the current riding condition and the user's emotional state, using data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms.

[1435] 4. Output control decision

[1436] Based on the analysis results, the server determines the motor output. For example, if the user is fatigued on a flat road, the motor output is set low to minimize battery consumption. On the other hand, if the user is excited on an uphill slope, the motor output is increased to provide more pedaling support.

[1437] 5. Output Transmission and Notification

[1438] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit. The device also notifies the user of specific product information and discount information based on their emotional state. Furthermore, when the battery level is low, it notifies the user of information about the nearest charging station.

[1439] Hardware and software used

[1440] Emotion Recognition Engine: Uses facial recognition camera and voice recognition technology.

[1441] Pedal force sensor, speed sensor, location sensor: Uses standard sensor modules built into the bicycle.

[1442] Server: A high-performance server for large-scale data analysis.

[1443] Terminal (smart glasses, smartphone): A device for collecting data and notifying the user.

[1444] Specific examples

[1445] 1. Driving scenario

[1446] If the emotion engine recognizes a user's fatigue from their facial expression while driving on a flat road, the server will set the motor output low to provide minimal assistance while saving battery power.

[1447] 2. Shopping Scenario

[1448] If the emotion engine recognizes an excited state from the tone of a user's voice while they are shopping in a virtual store, it will notify the smart glasses of specific product information or discount information, helping the user continue shopping comfortably.

[1449] Generative AI model prompt example

[1450] "Generate a program for a smart glasses app that detects the user's emotional state while shopping and provides relaxing content or special discounts accordingly. As a concrete example, please explain the processing flow when the user is feeling stressed, including the definitions of related classes and methods."

[1451] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1452] Step 1:

[1453] The server starts the system and initializes all sensors and emotion recognition engines. This confirms that each sensor and emotion engine is operating normally and completes the initial setup. The input is the system startup command, and the output is the normal operating status of the sensors and emotion recognition engines.

[1454] Step 2:

[1455] The device starts collecting data in real time. Specifically, it obtains the user's pedaling force from the pedal force sensor, current speed from the speed sensor, and current location from the location sensor. At the same time, the emotion recognition engine analyzes the user's facial recognition data and voice data to obtain the user's emotional state. The input is data from each sensor and the emotion recognition engine, and the output is a dataset of pedaling force, speed, location, and emotional state.

[1456] Step 3:

[1457] The terminal sends the data collected to the server. The data is packaged at regular intervals and sent to the server in JSON format. The input is the dataset collected by the terminal, and the output is the JSON data sent to the server.

[1458] Step 4:

[1459] The server analyzes the received data. Specifically, it uses pedaling force, speed, location information, and emotional state to determine the current riding state and the user's emotional state. The server uses data filtering, anomaly detection, matching with a terrain database, and emotion recognition algorithms. The input is the JSON data sent to the server, and the output is the analyzed riding state and emotional state.

[1460] Step 5:

[1461] The server determines the motor output based on the analysis results. For example, if fatigue is detected on a flat road, the motor output is set low to minimize battery consumption. If the user becomes excited while going uphill, the motor output is increased to provide stronger pedaling support. The input is the analysis result of the riding state and emotional state, and the output is the motor control command.

[1462] Step 6:

[1463] The server sends the determined motor output instructions to the terminal, which then transmits the instructions to the motor control unit. It also notifies the user of specific product information and discount information depending on their emotional state. Furthermore, if the battery level is low, it notifies the user of information about the nearest charging station. The inputs are motor control instructions and notification information, and the outputs are control signals to the motor control unit and notification content to the user.

[1464] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1465] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1466] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1467] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1468] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1469] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1470] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1471] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1472] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1473] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1474] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1475] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1476] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1477] 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.

[1478] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1479] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1480] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1481] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1482] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1483] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1484] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1485] The following is further disclosed regarding the above embodiment.

[1486] (Claim 1)

[1487] means for acquiring a user's pedaling force from a pedal force sensor;

[1488] means for obtaining a current speed from a speed sensor;

[1489] A means for acquiring current location information from a location information sensor;

[1490] means for analyzing the acquired pedaling force, speed, and position information to identify a current riding state;

[1491] a means for controlling the output of the electric motor based on the running state;

[1492] means for sending instructions to an electric motor control unit to control the motor output;

[1493] The system includes a means for notifying a user of information relating to the output control of the electric motor.

[1494] (Claim 2)

[1495] 2. The system of claim 1, further comprising means for transmitting to the electric motor control unit an instruction to reduce the output of the motor when the driving condition is determined to be a flat road.

[1496] (Claim 3)

[1497] 10. The system of claim 1, further comprising means for sending an instruction to the electric motor control unit to increase the power output of the motor when the driving condition is determined to be uphill.

[1498] "Example 1"

[1499] (Claim 1)

[1500] means for acquiring a user's pedaling force from a pedal force sensor;

[1501] means for obtaining a current speed from a speed sensor;

[1502] A means for acquiring current location information from a location information sensor;

[1503] means for analyzing the acquired pedaling force, speed, and position information to identify a current riding state;

[1504] a means for controlling the output of the electric motor based on the running state;

[1505] means for sending instructions to an electric motor control unit to control the motor output;

[1506] a means for notifying a user of information relating to the output control of the electric motor;

[1507] means for the server to initialize each sensor and control unit at system startup;

[1508] The system includes means for the terminal to collect data from the sensors and transmit the data to a server.

[1509] (Claim 2)

[1510] 2. The system of claim 1, further comprising means for transmitting to the electric motor control unit an instruction to reduce the output of the motor when the driving condition is determined to be a flat road.

[1511] (Claim 3)

[1512] 10. The system of claim 1, further comprising means for sending an instruction to the electric motor control unit to increase the power output of the motor when the driving condition is determined to be uphill.

[1513] "Application Example 1"

[1514] (Claim 1)

[1515] means for acquiring a user's pedaling force from a pedal force sensor;

[1516] means for obtaining a current speed from a speed sensor;

[1517] A means for acquiring current location information from a location information sensor;

[1518] means for analyzing the acquired pedaling force, speed, and position information to identify a current riding state;

[1519] a means for controlling the output of the electric motor based on the running state;

[1520] means for sending instructions to an electric motor control unit to control the motor output;

[1521] a means for notifying a user of information relating to the output control of the electric motor;

[1522] means for instructing the motor to reduce power output to minimize energy consumption when the vehicle is traveling on a flat road;

[1523] means for calculating the next optimum motor output when the user's pedal force and running speed fall below a set standard, and adjusting the motor output based on the calculated next optimum motor output;

[1524] A means to suggest the optimal route when the battery is low and notify the user of the nearest charging station;

[1525] The system includes means for transmitting information regarding optimization of motor output to a server and adjusting the output of the motor based on instructions from the server.

[1526] (Claim 2)

[1527] 2. The system of claim 1, further comprising means for transmitting to the electric motor control unit an instruction to reduce the output of the motor when the driving condition is determined to be a flat road.

[1528] (Claim 3)

[1529] 10. The system of claim 1, further comprising means for sending an instruction to the electric motor control unit to increase the power output of the motor when the driving condition is determined to be uphill.

[1530] "Example 2: Combining Emotion Engines"

[1531] (Claim 1)

[1532] means for acquiring a user's pedaling force from a pedal force sensor;

[1533] means for obtaining a current speed from a speed sensor;

[1534] A means for acquiring current location information from a location information sensor;

[1535] means for obtaining an emotional state of a user from an emotion engine;

[1536] means for analyzing the acquired pedaling force, speed, position information and emotional state to identify the current riding state and the emotional state of the user;

[1537] a means for controlling the output of an electric motor based on the driving state and the emotional state;

[1538] means for sending instructions to an electric motor control unit to control the motor output;

[1539] The system includes a means for notifying a user of information relating to the output control of the electric motor.

[1540] (Claim 2)

[1541] 2. The system of claim 1, further comprising: means for sending an instruction to the electric motor control unit to reduce the output of the motor when the driving condition is determined to be a flat road and the emotional state is determined to be fatigue.

[1542] (Claim 3)

[1543] 10. The system of claim 1, further comprising: means for sending an instruction to the electric motor control unit to increase motor power when the driving condition is determined to be uphill and the emotional state is determined to be excited.

[1544] "Application example 2 when combining emotion engines"

[1545] (Claim 1)

[1546] means for acquiring a user's pedaling force from a pedal force sensor;

[1547] means for obtaining a current speed from a speed sensor;

[1548] A means for acquiring current location information from a location information sensor;

[1549] means for acquiring an emotional state of a user using an emotion recognition engine for recognizing the emotional state;

[1550] means for analyzing the acquired pedaling force, speed, position information, and emotional state to identify a current riding state and an emotional state of the user;

[1551] a means for controlling the output of an electric motor based on the driving state and the emotional state;

[1552] means for sending instructions to an electric motor control unit to control the motor output;

[1553] The system includes a means for notifying a user of information relating to the output control of the electric motor.

[1554] (Claim 2)

[1555] 2. The system of claim 1, further comprising means for transmitting to the electric motor control unit an instruction to reduce the output of the motor when the driving condition is determined to be a flat road and the user is determined to be fatigued.

[1556] (Claim 3)

[1557] 2. The system of claim 1, further comprising means for sending an instruction to the electric motor control unit to increase the output of the motor when the driving state is determined to be uphill and the user is determined to be excited.

[1558] (Claim 4)

[1559] 10. The system of claim 1, further comprising means for notifying the user of specific product information or discount information depending on the emotional state.

[1560] (Claim 5)

[1561] 10. The system of claim 1, further comprising means for notifying a user of information about the nearest charging station when the battery is low. [Explanation of symbols]

[1562] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring a user's pedaling force from a pedal force sensor; means for obtaining a current speed from a speed sensor; A means for acquiring current location information from a location information sensor; means for analyzing the acquired pedaling force, speed, and position information to identify a current riding state; a means for controlling the output of the electric motor based on the running state; means for sending instructions to an electric motor control unit to control the motor output; The system includes a means for notifying a user of information relating to the output control of the electric motor.

2. The system according to claim 1 , further comprising means for transmitting to the electric motor control unit an instruction to reduce the output of the motor when the driving condition is determined to be a flat road.

3. 2. The system of claim 1, further comprising means for sending an instruction to the electric motor control unit to increase motor power when the driving condition is determined to be uphill.

Images