US20230020291A1

US20230020291A1 - Systems and methods for determining amount of carbon emissions produced by vehicles

Info

Publication number: US20230020291A1
Application number: US17/935,416
Authority: US
Inventors: Kenneth Jason Sanchez
Original assignee: BlueOwl LLC
Current assignee: Quanata LLC
Priority date: 2020-03-27
Filing date: 2022-09-26
Publication date: 2023-01-19
Also published as: EP4128117A1; WO2021195043A1; WO2021195077A1; WO2021195059A1; US20230017596A1; US20230013561A1; WO2021195007A1; US20230008123A1; US20230028260A1; EP4128117A4; US20230016022A1; US20230131645A1; WO2021195019A1; US20230016696A1; WO2021195056A1; EP4128108A4; EP4128013A4; US20230016482A1; WO2021195006A1; WO2021195053A1

Abstract

Method and system for determining a historical amount of carbon emissions produced by a vehicle of a driver are disclosed. For example, the method includes receiving, by the computing device, a vehicle information including a number of miles that the driver has driven the vehicle during a first time duration, collecting, by the computing device, telematics data for one or more trips made by the driver with the vehicle during a second time duration, and determining, by the computing device, the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 63/000,874, filed Mar. 27, 2020, incorporated by reference herein in its entirety.
Additionally, this application is related to International Application No. PCT/US2021/018233, filed Feb. 16, 2021, incorporated by reference herein in its entirety.

FIELD OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to determining a historical amount of carbon emissions produced by a vehicle. More particularly, certain embodiments of the present disclosure provide systems and methods for determining a historical amount of carbon emissions produced by a vehicle of a driver based at least in part upon telematics data of the driver. Merely by way of example, the present disclosure has been applied to determining a historical amount of carbon emissions produced by a vehicle during a first time duration based at least in part upon telematic data of the driver collected during a second time duration. But it would be recognized that the present disclosure has much broader range of applicability.

BACKGROUND OF THE DISCLOSURE

Carbon emissions produced by individuals everyday represent a major contributor to climate change. Hence it is highly desirable to develop more accurate techniques for determining an amount of carbon emissions that have been produced by a vehicle of a driver, which may be shared with the driver.

BRIEF SUMMARY OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to determining a historical amount of carbon emissions produced by a vehicle. More particularly, certain embodiments of the present disclosure provide systems and methods for determining a historical amount of carbon emissions produced by a vehicle of a driver based at least in part upon telematics data of the driver. Merely by way of example, the present disclosure has been applied to determining a historical amount of carbon emissions produced by a vehicle during a first time duration based at least in part upon telematic data of the driver collected during a second time duration. But it would be recognized that the present disclosure has much broader range of applicability.
According to some embodiments, a method for determining a historical amount of carbon emissions produced by a vehicle of a driver includes receiving vehicle information including a number of miles that the driver has driven the vehicle during a first time duration. The method further includes collecting telematics data for one or more trips made by the driver with the vehicle during a second time duration. The first time duration ends before the second time duration starts. Additionally, the method includes determining the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration.
According to some embodiments, a computing device for determining a historical amount of carbon emissions produced by a vehicle of a driver includes receiving vehicle information includes one or more processors and a memory that stores instructions for execution by the one or more processors. The instructions, when executed, cause the one or more processors to receive vehicle information including a number of miles that the driver has driven the vehicle during a first time duration. Further, the instructions, when executed, cause the one or more processors to collect telematics data for one or more trips made by the driver with the vehicle during a second time duration. The first time duration ends before the second time duration starts. Additionally, the instructions, when executed, cause the one or more processors to determine the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration.
According to some embodiments, a non-transitory computer-readable medium stores instructions for determining a historical amount of carbon emissions produced by a vehicle of a driver includes receiving vehicle information. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to receive vehicle information including a number of miles that the driver has driven the vehicle during a first time duration. Further, the non-transitory computer-readable medium includes instructions to collect telematics data for one or more trips made by the driver with the vehicle during a second time duration. The first time duration ends before the second time duration starts. Additionally, the non-transitory computer-readable medium includes instructions to determine the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration.
Depending upon the embodiment, one or more benefits may be achieved. These benefits and various additional objects, features and advantages of the present disclosure can be fully appreciated with reference to the detailed description and accompanying drawings that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified method for determining a historical amount of carbon emissions produced by a vehicle of a driver according to certain embodiments of the present disclosure.

FIG. 2 is a simplified method for determining a historical amount of carbon emissions produced by a vehicle of a driver according to some embodiments of the present disclosure.

FIG. 3 is a diagram showing a system for determining a historical amount of carbon emissions produced by a vehicle of a driver according to certain embodiments of the present disclosure.

FIG. 4 is a simplified diagram showing a computing device, according to various embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

I. One or More Methods for Determining a Historical Amount of Carbon Emissions Produced by a Vehicle of a Driver According to Certain Embodiments

FIG. 1 is a simplified diagram showing a method 100 for determining a historical amount of carbon emissions produced by a vehicle of a driver according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the method 104) is performed by a computing device (e.g., a server 306). However, it should be appreciated that, in some embodiments, some of the method 100 is performed by any computing device (e.g., a mobile device 302).
The method 100 includes process 102 for receiving vehicle information including a number of miles that the driver has driven the vehicle during a first time duration, process 104 for collecting telematics data for one or more trips made by the driver with the vehicle during a second time duration, and process 106 for determining the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration.
Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, although the method 100 is described as performed by the computing device above, some or all processes of the method are performed by any computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.
Specifically, at the process 102, the vehicle information may include a duration of which the driver owned the vehicle, one or more odometer readings, and/or one or more gas receipts. The odometer reading indicates a number of miles that the driver has driven the vehicle when the odometer reading was collected. In some embodiments, the computing device may obtain or receive the odometer reading from an application installed on a mobile device of the driver. For example, the application may be the same application that collects the telematics data, as described in the process 104. In such example, the application may transmit the initial odometer reading when the application is first installed and/or may periodically collect and transmit the odometer readings. In other embodiments, the computing device may communicate directly with the vehicle to obtain or receive one or more odometer readings. Additionally or alternatively, the odometer reading may be received manually from the driver. As described further below, the number of miles is used to predict an amount of carbon emissions produced for the number of miles that the driver has driven the vehicle.
According to some embodiments, if the one or more odometer readings are not available, the computing device may use the duration of which the driver owned the vehicle to predict the number of miles that the driver has driven the vehicle prior to collecting the telematics data.
According to certain embodiments, the gas receipts may be collected (e.g., from the driver's online bank statements) to determine how much gas the vehicle consumed during a certain time period that the driver owned the vehicle. An amount of carbon emissions produced by the vehicle may be predicted based at least in part upon the gas consumption.
At the process 104, the telematics data includes information related to one or more driving behaviors of the driver. As an example, the one or more driving behaviors represent a manner in which the driver has operated a vehicle. For example, the driving behaviors indicate the driver's driving habits and/or driving patterns. As discussed below, the telematics data is used to determine a historical amount of carbon emissions produced by the vehicle during a time period when the telematics data was not collected according to some embodiments.
According to some embodiments, the telematics data is received, obtained, or otherwise collected from one or more sensors associated with one or more vehicles of the driver. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, steering angle sensors, brake sensors, proximity detectors, and/or any other suitable sensors that measure vehicle state and/or operation. In certain embodiments, the one or more sensors are part of or located in the one or more vehicles. In some embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the driver) that are communicatively coupled to the one or more vehicles while the driver is operating one of the one or more vehicles. In other embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the driver) that is located inside a vehicle while the driver is operating the vehicle. According to certain embodiments, the telematics data is collected continuously or at predetermined time intervals. According to some embodiments, the telematics data is collected based on a triggering event. For example, the telematics data is collected when each sensor has acquired a threshold amount of sensor measurements. According to other embodiments, the telematics data may be received, obtained, or otherwise collected from a server (e.g., a server 306) associated with an insurance provider.
At the process 106, the first time duration ends before the second time duration starts according to some embodiments. However, according to certain embodiments, a portion of the first time duration precedes the second time duration.
For example, a driver bought a vehicle five years ago and has been driving the vehicle since then. A year ago, the driver chose to install an application on his mobile device that collects telematics data of his vehicle to receive an insurance discount. Based on the driver's telematics data, an amount of carbon emissions produced by the vehicle for the past year may be determined. Additionally, a historical amount of carbon emissions produced by the vehicle for the first four years that the driver has been driving the vehicle may be estimated based on the driver's telematics data of the driver for the last year. In other words, based on the telematics data of the driver collected during a certain period of time is used to predict or estimate an amount of carbon emissions produced during another period of time that the driver was driving the vehicle.
FIG. 2 is a simplified method for determining a historical amount of carbon emissions produced by a vehicle of a driver according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the method 200 is performed by a computing device (e.g., a server 306). However, it should be appreciated that, in some embodiments, some of the method 200 is performed by any computing device (e.g., a mobile device 302).
The method 200 includes process 202 for process 202 for receiving vehicle information including a number of miles that the driver has driven the vehicle during a first time duration, process 204 for collecting telematics data for one or more trips made by the driver with the vehicle during a second time duration, process 206 for determining the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration, process 208 for receiving additional information, process 210 for adjusting the historical amount of carbon emissions based at least in part upon the additional information, and process 212 for transmitting a notification to the driver.
Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, although the method 200 is described as performed by the computing device above, some or all processes of the method are performed by any computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.
Specifically, at the process 202, the vehicle information may include a duration of which the driver owned the vehicle, one or more odometer readings, and/or one or more gas receipts. The odometer reading indicates a number of miles that the driver has driven the vehicle when the odometer reading was collected. In some embodiments, the computing device may obtain or receive the odometer reading from an application installed on a mobile device of the driver. For example, the application may be the same application that collects the telematics data, as described in the process 104. In such example, the application may transmit the initial odometer reading when the application is first installed and/or may periodically collect and transmit the odometer readings. In other embodiments, the computing device may communicate directly with the vehicle to obtain or receive one or more odometer readings. Additionally or alternatively, the odometer reading may be received manually from the driver. As described further below, the number of miles is used to predict an amount of carbon emissions produced for the number of miles that the driver has driven the vehicle.
According to some embodiments, if the one or more odometer readings are not available, the computing device may use the duration of which the driver owned the vehicle to predict the number of miles that the driver has driven the vehicle prior to collecting the telematics data.
According to certain embodiments, the gas receipts may be collected (e.g., from the driver's online bank statements) to determine how much gas the vehicle consumed during a certain time period that the driver owned the vehicle. An amount of carbon emissions produced by the vehicle may be predicted based at least in part upon the gas consumption.
At the process 204, the telematics data includes information related to one or more driving behaviors of the driver. As an example, the one or more driving behaviors represent a manner in which the driver has operated a vehicle. For example, the driving behaviors indicate the driver's driving habits and/or driving patterns. As discussed below, the telematics data is used to determine a historical amount of carbon emissions produced by the vehicle during a time period when the telematics data was not collected according to some embodiments.
According to some embodiments, the telematics data is received, obtained, or otherwise collected from one or more sensors associated with one or more vehicles of the driver. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, steering angle sensors, brake sensors, proximity detectors, and/or any other suitable sensors that measure vehicle state and/or operation. In certain embodiments, the one or more sensors are part of or located in the one or more vehicles. In some embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the driver) that are communicatively coupled to the one or more vehicles while the driver is operating one of the one or more vehicles. In other embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the driver) that is located inside a vehicle while the driver is operating the vehicle. According to certain embodiments, the telematics data is collected continuously or at predetermined time intervals. According to some embodiments, the telematics data is collected based on a triggering event. For example, the telematics data is collected when each sensor has acquired a threshold amount of sensor measurements. According to other embodiments, the telematics data may be received, obtained, or otherwise collected from a server (e.g., a server 306) associated with an insurance provider.
At the process 206, the first time duration ends before the second time duration starts according to some embodiments. However, according to certain embodiments, a portion of the first time duration precedes the second time duration.
For example, a driver bought a vehicle five years ago and has been driving the vehicle since then. A year ago, the driver chose to install an application on his mobile device that collects telematics data of his vehicle to receive an insurance discount. Based on the driver's telematics data, an amount of carbon emissions produced by the vehicle for the past year may be determined. Additionally, a historical amount of carbon emissions produced by the vehicle for the first four years that the driver has been driving the vehicle may be estimated based on the driver's telematics data of the driver for the last year. In other words, based on the telematics data of the driver collected during a certain period of time is used to predict or estimate an amount of carbon emissions produced during another period of time that the driver was driving the vehicle.
At the process 208, the additional information includes one or more changes in environment of the driver from the first time duration to the second time duration according to some embodiments. According to some embodiments, the environment of the driver changes if the driver moves his residence from urban area to rural area or rural area to urban area. Additionally or alternatively, the environment of the driver changes if the driver changes where he works (e.g., different commute routes for work). In other words, the changes in the environment of the driver include any change in the environment that may impact the one or more driving behaviors or driving patterns of the driver.
Additionally or alternatively, the additional information includes other data collected from one or more other drivers whose demographic information (e.g., age, race, ethnicity, gender, marital status, income, education, and/or employment) is similar to the user. Additionally or alternatively, the additional information includes other data collected from one or more other drivers whose one or more driving behaviors are similar to the user.
At the process 210, the historical amount of carbon emissions determined at the process 206 is adjusted based at least in part upon the additional information. As described above, the additional information of the driver may affect the one or more driving behaviors of the driver. Referring back to the example described above, where the driver has been driving the vehicle for five years but the telematics data has been collected for the past year. If the additional information further indicates that the driver moved from a rural environment to an urban environment a year ago, the computing device may adjust the historical amount of carbon emissions. For example, the drivers generally brake more when driving in the urban environment and, therefore, produces more carbon emissions. Such adjustment may be performed using machine learning using other data collected from one or more other drivers whose demographic information is similar to the user and/or from one or more other drivers whose one or more driving behaviors are similar to the user.
At the process 212, the computing device may transmit a notification to the driver on the driver's device based upon the adjusted historical amount of carbon emissions. For example, the notification may include the adjusted historical amount of carbon emissions. The notification may further include one or more recommendations for the driver. As an example, the one or more recommendations may include planting a number of eternal trees to compensate for the historical amount of carbon emissions.
Generally, the planted trees consumes carbon during its lifetime; however, carbon stored by the planted trees are released back into the atmosphere when the trees die. For example, to compensate for this, an eternal tree is planted in which a tree is planted today. As the tree grows, it removes carbon from the air. When the tree eventually dies, another tree is planted. This process repeats indefinitely to ensure that there will always be a tree that exists to capture the carbon according to certain embodiments. According to some embodiments, the driver may become 100% carbon neutral by planting one or more eternal trees.
As an example, the computing device is configured to estimate a number of eternal trees to be planted to compensate for the historical amount of carbon emissions. To do so, the computing device may continuously capture, store, and recapture carbon emissions generated by the driver in the form of an eternal tree. According to some embodiments, the computing device estimates at least a number of first trees to be planted at a first time and the number of second trees to be planted at a second time to compensate for the historical amount of carbon emissions. Each of the first trees corresponds to a lifespan, and the first time precedes the second time by a time duration, which is shorter than or equal to the lifespan.
Additionally or alternatively, the recommendations may include recommending another vehicle to the driver. For example, in response to determining that the adjusted historical amount of carbon emissions exceeds the predetermined amount, a recommended vehicle is determined based at least in part upon the telematics data of the driver. In other example, the computing device may determine an amount of carbon emissions per mile based at least in part upon the determined historical amount of carbon emissions and the number of miles that the driver has driven the vehicle during the first time duration. The computing device may further determine whether the amount of carbon emissions per mile exceeds a predetermined rate. If so, the computing device may determine a recommended vehicle that produces less amount of carbon emissions per mile based at least in part upon the telematics data of the driver

II. One or More Systems for Determining a Historical Amount of Carbon Emissions Produced by a Vehicle of a Driver According to Certain Embodiments

FIG. 3 is a simplified diagram showing a system for determining a historical amount of carbon emissions produced by a vehicle of a driver according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the system 300 includes a mobile device 302, a network 304, and a server 306. Although the above has been shown using a selected group of components for the system, there can be many alternatives, modifications, and variations. For example, some of the components may be expanded and/or combined. Other components may be inserted to those noted above. Depending upon the embodiment, the arrangement of components may be interchanged with others replaced.
In various embodiments, the system 300 is used to implement the method 100 and/or the method 200. According to certain embodiments, the mobile device 302 is communicatively coupled to the server 306 via the network 304. As an example, the mobile device 302 includes one or more processors 316 (e.g., a central processing unit (CPU), a graphics processing unit (GPU)), a memory 318 (e.g., random-access memory (RAM), read-only memory (ROM), flash memory), a communications unit 320 (e.g., a network transceiver), a display unit 322 (e.g., a touchscreen), and one or more sensors 324 (e.g., an accelerometer, a gyroscope, a magnetometer, a location sensor). For example, the one or more sensors 324 are configured to generate the driving data. According to some embodiments, the driving data are collected continuously, at predetermined time intervals, and/or based on a triggering event (e.g., when each sensor has acquired a threshold amount of sensor measurements).
In some embodiments, the mobile device 302 is operated by the driver. For example, the driver installs an application associated with an insurer on the mobile device 302 and allows the application to communicate with the one or more sensors 324 to collect data (e.g., the driving data). According to some embodiments, the application collects the data continuously, at predetermined time intervals, and/or based on a triggering event (e.g., when each sensor has acquired a threshold amount of sensor measurements). In certain embodiments, the data is used to determine an amount of carbon emissions generated by the driver's vehicle in the method 100 and/or the method 200. As an example, the data represents the driver's driving behaviors. According to some embodiments, there may be other drivers that drives the driver's vehicle. In such embodiments, there may be multiple mobile devices (e.g., mobile devices of one or more drivers of the vehicle) that are in communication with the server 306.
According to certain embodiments, the collected data are stored in the memory 318 before being transmitted to the server 306 using the communications unit 322 via the network 304 (e.g., via a local area network (LAN), a wide area network (WAN), the Internet). In some embodiments, the collected data are transmitted directly to the server 306 via the network 304. In certain embodiments, the collected data are transmitted to the server 306 via a third party. For example, a data monitoring system stores any and all data collected by the one or more sensors 324 and transmits those data to the server 306 via the network 304 or a different network.
According to certain embodiments, the server 306 includes a processor 330 (e.g., a microprocessor, a microcontroller), a memory 332, a communications unit 334 (e.g., a network transceiver), and a data storage 336 (e.g., one or more databases). In some embodiments, the server 306 is a single server, while in certain embodiments, the server 306 includes a plurality of servers with distributed processing. As an example, in FIG. 4 , the data storage 336 is shown to be part of the server 306. In some embodiments, the data storage 336 is a separate entity coupled to the server 306 via a network such as the network 304. In certain embodiments, the server 306 includes various software applications stored in the memory 332 and executable by the processor 330. For example, these software applications include specific programs, routines, or scripts for performing functions associated with the method 100 and/or the method 200. As an example, the software applications include general-purpose software applications for data processing, network communication, database management, web server operation, and/or other functions typically performed by a server.
According to various embodiments, the server 306 receives, via the network 304, the driving data collected by the one or more sensors 324 from the application using the communications unit 334 and stores the data in the data storage 336. For example, the server 306 then processes the data to perform one or more processes of the method 100 and/or one or more processes of the method 200.
According to certain embodiments, the notification in the method 200 is transmitted to the mobile device 302, via the network 304, to be provided (e.g., displayed) to the driver via the display unit 322.
In some embodiments, one or more processes of the method 100 and/or one or more processes of the method 200 are performed by the mobile device 302. For example, the processor 316 of the mobile device 302 analyzes the driving data collected by the one or more sensors 324 to perform one or more processes of the method 100 and/or one or more processes of the method 200.

III. One or More Computing Devices According to Various Embodiments

FIG. 4 is a simplified diagram showing a computing device 400, according to various embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In some examples, the computing device 400 includes a processing unit 402, a memory unit 404, an input unit 406, an output unit 408, and a communication unit 410. In various examples, the computing device 400 is configured to be in communication with a driver 420 and/or a storage device 422. In certain examples, the system computing device 400 is configured according to the system 300 of FIG. 3 to implement the method 100 of FIG. 1 and/or the method 200 of FIG. 2 . Although the above has been shown using a selected group of components, there can be many alternatives, modifications, and variations. In some examples, some of the components may be expanded and/or combined. Some components may be removed. Other components may be inserted to those noted above. Depending upon the embodiment, the arrangement of components may be interchanged with others replaced.
In various embodiments, the processing unit 402 is configured for executing instructions, such as instructions to implement the method 100 of FIG. 1 and/or the method 200 of FIG. 2 . In some embodiments, executable instructions may be stored in the memory unit 404. In some examples, the processing unit 402 includes one or more processing units (e.g., in a multi-core configuration). In certain examples, the processing unit 402 includes and/or is communicatively coupled to one or more modules for implementing the systems and methods described in the present disclosure. In some examples, the processing unit 402 is configured to execute instructions within one or more operating systems, such as UNIX, LINUX, Microsoft Windows®, etc. In certain examples, upon initiation of a computer-implemented method, one or more instructions is executed during initialization. In some examples, one or more operations is executed to perform one or more processes described herein. In certain examples, an operation may be general or specific to a particular programming language (e.g., C, C#, C++, Java, or other suitable programming languages, etc.). In various examples, the processing unit 402 is configured to be operatively coupled to the storage device 422, such as via an on-board storage unit 412.
In various embodiments, the memory unit 404 includes a device allowing information, such as executable instructions and/or other data to be stored and retrieved. In some examples, the memory unit 404 includes one or more computer readable media. In some embodiments, data stored in the memory unit 404 include computer readable instructions for providing a user interface, such as to the driver 404, via the output unit 408. In some examples, a user interface includes a web browser and/or a client application. In various examples, a web browser enables one or more users, such as the driver 404, to display and/or interact with media and/or other information embedded on a web page and/or a website. In certain examples, the memory unit 404 include computer readable instructions for receiving and processing an input, such as from the driver 404, via the input unit 406. In certain examples, the memory unit 404 includes random access memory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and/or non-volatile RAM (NVRAN).
In various embodiments, the input unit 406 is configured to receive input, such as from the driver 404. In some examples, the input unit 406 includes a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad or a touch screen), a gyroscope, an accelerometer, a position detector (e.g., a Global Positioning System), and/or an audio input device. In certain examples, the input unit 406, such as a touch screen of the input unit, is configured to function as both the input unit and the output unit
In various embodiments, the output unit 408 includes a media output unit configured to present information to the driver 404. In some embodiments, the output unit 408 includes any component capable of conveying information to the driver 404. In certain embodiments, the output unit 408 includes an output adapter, such as a video adapter and/or an audio adapter. In various examples, the output unit 408, such as an output adapter of the output unit, is operatively coupled to the processing unit 402 and/or operatively coupled to an presenting device configured to present the information to the driver, such as via a visual display device (e.g., a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a cathode ray tube (CRT) display, an “electronic ink” display, a projected display, etc.) or an audio display device (e.g., a speaker arrangement or headphones).
In various embodiments, the communication unit 410 is configured to be communicatively coupled to a remote device. In some examples, the communication unit 410 includes a wired network adapter, a wireless network adapter, a wireless data transceiver for use with a mobile phone network (e.g., Global System for Mobile communications (GSM), 3G, 4G, or Bluetooth), and/or other mobile data networks (e.g., Worldwide Interoperability for Microwave Access (WIMAX)). In certain examples, other types of short-range or long-range networks may be used. In some examples, the communication unit 410 is configured to provide email integration for communicating data between a server and one or more clients.
In various embodiments, the storage unit 412 is configured to enable communication between the computing device 400, such as via the processing unit 402, and an external storage device 422. In some examples, the storage unit 412 is a storage interface. In certain examples, the storage interface is any component capable of providing the processing unit 402 with access to the storage device 422. In various examples, the storage unit 412 includes an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any other component capable of providing the processing unit 402 with access to the storage device 422.
In some examples, the storage device 422 includes any computer-operated hardware suitable for storing and/or retrieving data. In certain examples, the storage device 422 is integrated in the computing device 400. In some examples, the storage device 422 includes a database, such as a local database or a cloud database. In certain examples, the storage device 422 includes one or more hard disk drives. In various examples, the storage device is external and is configured to be accessed by a plurality of serer systems. In certain examples, the storage device includes multiple storage units such as hard disks or solid state disks in a redundant array of inexpensive disks (RAID) configuration. In some examples, the storage device 422 includes a storage area network (SAN) and/or a network attached storage (NAS) system.

IV. Examples of Machine Learning According to Certain Embodiments

According to some embodiments, a processor or a processing element may be trained using supervised machine learning and/or unsupervised machine learning, and the machine learning may employ an artificial neural network, which, for example, may be a convolutional neural network, a recurrent neural network, a deep learning neural network, a reinforcement learning module or program, or a combined learning module or program that learns in two or more fields or areas of interest. Machine learning may involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data. Models may be created based upon example inputs in order to make valid and reliable predictions for novel inputs.
According to certain embodiments, machine learning programs may be trained by inputting sample data sets or certain data into the programs, such as images, object statistics and information, historical estimates, and/or actual repair costs. The machine learning programs may utilize deep learning algorithms that may be primarily focused on pattern recognition and may be trained after processing multiple examples. The machine learning programs may include Bayesian Program Learning (BPL), voice recognition and synthesis, image or object recognition, optical character recognition, and/or natural language processing. The machine learning programs may also include natural language processing, semantic analysis, automatic reasoning, and/or other types of machine learning.
According to some embodiments, supervised machine learning techniques and/or unsupervised machine learning techniques may be used. In supervised machine learning, a processing element may be provided with example inputs and their associated outputs and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output. In unsupervised machine learning, the processing element may need to find its own structure in unlabeled example inputs.

V. Examples of Certain Embodiments of the Present Disclosure

According to some embodiments, a method for determining a historical amount of carbon emissions produced by a vehicle of a driver includes receiving vehicle information including a number of miles that the driver has driven the vehicle during a first time duration. The method further includes collecting telematics data for one or more trips made by the driver with the vehicle during a second time duration. The first time duration ends before the second time duration starts. Additionally, the method includes determining the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration. For example, the method is implemented according to at least FIG. 1 and/or FIG. 2 .
According to some embodiments, a computing device for determining a historical amount of carbon emissions produced by a vehicle of a driver includes receiving vehicle information includes one or more processors and a memory that stores instructions for execution by the one or more processors. The instructions, when executed, cause the one or more processors to receive vehicle information including a number of miles that the driver has driven the vehicle during a first time duration. Further, the instructions, when executed, cause the one or more processors to collect telematics data for one or more trips made by the driver with the vehicle during a second time duration. The first time duration ends before the second time duration starts. Additionally, the instructions, when executed, cause the one or more processors to determine the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration. For example, the computing device (e.g., the server 306) is implemented according to at least FIG. 3 and/or FIG. 4 (e.g. the computing device 400).
According to some embodiments, a non-transitory computer-readable medium stores instructions for determining a historical amount of carbon emissions produced by a vehicle of a driver includes receiving vehicle information. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to receive vehicle information including a number of miles that the driver has driven the vehicle during a first time duration. Further, the non-transitory computer-readable medium includes instructions to collect telematics data for one or more trips made by the driver with the vehicle during a second time duration. The first time duration ends before the second time duration starts. Additionally, the non-transitory computer-readable medium includes instructions to determine the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration. For example, the non-transitory computer-readable medium is implemented according to at least FIG. 1 . FIG. 2 , FIG. 3 , and/or FIG. 4 .

VI. Additional Considerations According to Certain Embodiments

For example, some or all components of various embodiments of the present disclosure each are, individually and/or in combination with at least another component, implemented using one or more software components, one or more hardware components, and/or one or more combinations of software and hardware components. As an example, some or all components of various embodiments of the present disclosure each are, individually and/or in combination with at least another component, implemented in one or more circuits, such as one or more analog circuits and/or one or more digital circuits. For example, while the embodiments described above refer to particular features, the scope of the present disclosure also includes embodiments having different combinations of features and embodiments that do not include all of the described features. As an example, various embodiments and/or examples of the present disclosure can be combined.
Additionally, the methods and systems described herein may be implemented on many different types of processing devices by program code comprising program instructions that are executable by the device processing subsystem. The software program instructions may include source code, object code, machine code, or any other stored data that is operable to cause a processing system to perform the methods and operations described herein. Certain implementations may also be used, however, such as firmware or even appropriately designed hardware configured to perform the methods and systems described herein.
The systems' and methods' data (e.g., associations, mappings, data input, data output, intermediate data results, final data results) may be stored and implemented in one or more different types of computer-implemented data stores, such as different types of storage devices and programming constructs (e.g., RAM, ROM, EEPROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, application programming interface). It is noted that data structures describe formats for use in organizing and storing data in databases, programs, memory, or other computer-readable media for use by a computer program.
The systems and methods may be provided on many different types of computer-readable media including computer storage mechanisms (e.g., CD-ROM, diskette, RAM, flash memory, computer's hard drive, DVD) that contain instructions (e.g., software) for use in execution by a processor to perform the methods' operations and implement the systems described herein. The computer components, software modules, functions, data stores and data structures described herein may be connected directly or indirectly to each other in order to allow the flow of data needed for their operations. It is also noted that a module or processor includes a unit of code that performs a software operation, and can be implemented for example as a subroutine unit of code, or as a software function unit of code, or as an object (as in an object-oriented paradigm), or as an applet, or in a computer script language, or as another type of computer code. The software components and/or functionality may be located on a single computer or distributed across multiple computers depending upon the situation at hand.
The computing system can include mobile devices and servers. A mobile device and server are generally remote from each other and typically interact through a communication network. The relationship of mobile device and server arises by virtue of computer programs running on the respective computers and having a mobile device-server relationship to each other.
This specification contains many specifics for particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations, one or more features from a combination can in some cases be removed from the combination, and a combination may, for example, be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Although specific embodiments of the present disclosure have been described, it will be understood by those of skill in the art that there are other embodiments that are equivalent to the described embodiments. Accordingly, it is to be understood that the present disclosure is not to be limited by the specific illustrated embodiments.

Claims

What is claimed is:

1. A computer-implemented method for determining a historical amount of carbon emissions produced by a vehicle of a driver, the method comprising:

receiving, by the computing device, vehicle information including a number of miles that the driver has driven the vehicle during a first time duration;

collecting, by the computing device, telematics data for one or more trips made by the driver with the vehicle during a second time duration; and

determining, by the computing device, the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration,

wherein the first time duration ends before the second time duration starts.

2. The method of claim 1, wherein the vehicle information includes at least one selected from a group consisting of how long the driver owned the vehicle, odometer readings, and gas receipts.

3. The method of claim 1, wherein the telematics data includes information related to one or more driving behaviors of the driver.

4. The method of claim 1, further comprising:

receiving, by the computing device, additional information;

wherein the additional information includes at least one selected from a group consisting of: one or more changes in environment of the driver from the first time duration to the second time duration and other data collected from one or more other drivers.

5. The method of claim 4, further comprising adjusting the historical amount of carbon emissions based at least in part upon the additional information.

6. The method of claim 1, further comprising:

estimating, by the computing device, at least a number of first trees to be planted at a first time and the number of second trees to be planted at a second time to compensate for the historical amount of carbon emissions; and

presenting, by the computing device, the estimated number to the driver,

wherein:

each of the first trees corresponds to a lifespan;

the first time precedes the second time by a time duration; and

the time duration is shorter than or equal to the lifespan.

7. The method of claim 1, further comprising:

determining, by the computing device, whether the determined historical amount of carbon emissions exceeds a predetermined amount of carbon emissions;

determining, in response to determining that the determined historical amount of carbon emissions exceeds the predetermined amount and by the computing device, a recommended vehicle based at least in part upon the telematics data of the driver; and

presenting, by the computing device, the recommended vehicle to the driver.

8. The method of claim 1, further comprising:

determining, by the computing device, a first amount of carbon emissions per mile based at least in part upon the determined historical amount of carbon emissions and the number of miles that the driver has driven the vehicle during the first time duration;

determining, by the computing device, whether the first amount of carbon emissions per mile exceeds a predetermined rate;

determining, in response to determining that the first amount of carbon emissions per mile exceeds the predetermined rate and by the computing device, a recommended vehicle based at least in part upon the telematics data of the driver, the recommended vehicle adapted to produce a second amount of carbon emissions per mile, the second amount of carbon emissions per mile being less than the first amount of carbon emissions per mile; and

presenting, by the computing device, the recommended vehicle to the driver.

9. A computing device for determining a historical amount of carbon emissions produced by a vehicle of a driver, the computing device comprising:

a processor; and

a memory having a plurality of instructions stored thereon that, when executed by the processor, causes the computing device to:

receive vehicle information including a number of miles that the driver has driven the vehicle during a first time duration;

collect telematics data for one or more trips made by the driver with the vehicle during a second time duration; and

determine the historical amount of carbon emissions produced by the vehicle during the first time duration based at least in part upon the number of miles during the first time duration and the telematics data during the second time duration,

wherein the first time duration ends before the second time duration starts.

10. The computing device of claim 9, wherein the vehicle information includes at least one selected from a group consisting of how long the driver owned the vehicle, odometer readings, and gas receipts.

11. The computing device of claim 9, wherein the telematics data includes information related to one or more driving behaviors of the driver.

12. The computing device of claim 9, wherein the plurality of instructions, when executed, further cause the computing device to:

receive additional information;

13. The computing device of claim 12, wherein the plurality of instructions, when executed, further cause the computing device to adjust the historical amount of carbon emissions based at least in part upon the additional information.

14. The computing device of claim 9, wherein the plurality of instructions, when executed, further cause the computing device to:

estimate at least a number of first trees to be planted at a first time and the number of second trees to be planted at a second time to compensate for the historical amount of carbon emissions; and

present the estimated number to the driver,

wherein:

each of the first trees corresponds to a lifespan;

the first time precedes the second time by a time duration; and

the time duration is shorter than or equal to the lifespan.

15. The computing device of claim 9, wherein the plurality of instructions, when executed, further cause the computing device to:

determine whether the determined historical amount of carbon emissions exceeds a predetermined amount of carbon emissions;

determine, in response to determining that the determined historical amount of carbon emissions exceeds the predetermined amount, a recommended vehicle based at least in part upon the telematics data of the driver; and

present the recommended vehicle to the driver.

16. The computing device of claim 9, wherein the plurality of instructions, when executed, further cause the computing device to:

determine a first amount of carbon emissions per mile based at least in part upon the determined historical amount of carbon emissions and the number of miles that the driver has driven the vehicle during the first time duration;

determine whether the first amount of carbon emissions per mile exceeds a predetermined rate;

determine, in response to determining that the first amount of carbon emissions per mile exceeds the predetermined rate, a recommended vehicle based at least in part upon the telematics data of the driver, the recommended vehicle adapted to produce a second amount of carbon emissions per mile, the second amount of carbon emissions per mile being less than the first amount of carbon emissions per mile; and

present the recommended vehicle to the driver.

17. A non-transitory computer-readable medium storing instructions for determining a historical amount of carbon emissions produced by a vehicle of a driver, the instructions when executed by one or more processors of a computing device, cause the computing device to:

wherein the first time duration ends before the second time duration starts.

18. The non-transitory computer-readable medium of claim 17,

wherein the vehicle information includes at least one selected from a group consisting of how long the driver owned the vehicle, odometer readings, and gas receipts, and

wherein the telematics data includes information related to one or more driving behaviors of the driver.

19. The non-transitory computer-readable medium of claim 17, wherein the instructions when executed by the one or more processors further cause the computing device to:

receive additional information;

20. The non-transitory computer-readable medium of claim 17, wherein the instructions when executed by the one or more processors further cause the computing device to adjust the historical amount of carbon emissions based at least in part upon the additional information.