PERIOD

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63/000,874, filed March 27, 2020, incorporated by reference herein for all purposes. [0002] International PCT Application No. PCT/US21/18233, titled “System and Methods for Providing Renewing Carbon Offsets” is incorporated by reference herein for all purposes.

[0003] The following five applications, including this one, are being filed concurrently and the other four are hereby incorporated by reference in their entirety for all purposes:

[0004] 1. International PCT Application No. _ , titled “Systems and

Methods for Offering Carbon Offset Rewards that Correspond to Users” (Attorney Docket Number BOL-00007A-PCT);

[0005] 2. International PCT Application No. _ , titled “Systems and

Methods for Providing Multiple Carbon Offset Sources” (Attorney Docket Number BOL-00007B-PCT);

[0006] 3. International PCT Application No. _ , titled “Systems and

Methods for Generating Tree Imagery” (Attorney Docket Number BOL-00007G- PCT);

[0007] 4. International PCT Application No. _ , titled “Systems and

Methods for Validating Planting of Trees” (Attorney Docket Number BOL-00007H- PCT); and

[0008] 5. International PCT Application No. _ , titled “Systems and

Methods for Providing Renewing Carbon Offsets for a User Driving Period” (Attorney Docket Number BOL-00007J-PCT). FIELD OF THE DISCLOSURE

[0009] Some embodiments of the present disclosure are directed to providing renewing carbon offsets. More particularly, certain embodiments of the present disclosure provide methods and systems for offering carbon offsets to compensate for carbon emissions generated during a user’s driving period. Merely by way of example, the present disclosure has been applied to offering carbon offsets through continuous self-funded tree planting during each year of the user’s driving period.

But it would be recognized that the present disclosure has much broader range of applicability.

BACKGROUND OF THE DISCLOSURE

[0010] Carbon emissions from vehicles represent a major contributor to climate change. While new vehicle technologies have been developed to curb carbon emissions, the continued use of vehicles for private transportation will cause the amount of carbon emissions to remain high or even increase. Hence it is highly desirable to develop additional approaches that compensate for the release of these carbon emissions.

BRIEF SUMMARY OF THE DISCLOSURE

[0011] Some embodiments of the present disclosure are directed to providing renewing carbon offsets. More particularly, certain embodiments of the present disclosure provide methods and systems for offering carbon offsets to compensate for carbon emissions generated during a user’s driving period. Merely by way of example, the present disclosure has been applied to offering carbon offsets through continuous self-funded tree planting during each year of the user’s driving period.

But it would be recognized that the present disclosure has much broader range of applicability.

[0012] According to certain embodiments, a method for providing renewing carbon offsets for a user driving period includes collecting driving data for one or more vehicle trips made by a user. The driving data include information related to a mindful driving behavior of the user. Also, the method includes analyzing the driving data to determine a level of mindful driving of the user. Additionally, the method includes determining a level of carbon offset reward based at least in part upon the level of mindful driving of the user. Further, the method includes determining a driving period of the user and estimating an amount of total carbon emission of the user for the driving period. Moreover, the method includes providing an amount of carbon offset reward based at least in part upon the level of carbon offset reward and the amount of total carbon emission. The amount of carbon offset reward includes a first amount for at least planting one or more first trees at a first time and planting one or more second trees at a second time, and a second amount for at least planting one or more third trees at a third time and planting one or more fourth trees at a fourth time. The third time follows the first time by one or more years. The first time precedes the second time by a time duration that is shorter than or equal to a lifespan of each of the one or more first trees. The third time precedes the fourth time by a time duration that is shorter than or equal to a lifespan of each of the one or more third trees.

[0013] According to some embodiments, a computing device for providing renewing carbon offsets for a user driving period 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 collect driving data for one or more vehicle trips made by a user. The driving data include information related to a mindful driving behavior of the user. Also, the instructions, when executed, cause the one or more processors to analyze the driving data to determine a level of mindful driving of the user. Additionally, the instructions, when executed, cause the one or more processors to determine a level of carbon offset reward based at least in part upon the level of mindful driving of the user and an amount of total carbon emission of the user. Further, the instructions, when executed, cause the one or more processors to determine a driving period of the user and estimating an amount of total carbon emission of the user for the driving period. Moreover, the instructions, when executed, cause the one or more processors to provide an amount of carbon offset reward based at least in part upon the level of carbon offset reward and the amount of total carbon emission. The amount of carbon offset reward includes a first amount for at least planting one or more first trees at a first time and planting one or more second trees at a second time, and a second amount for at least planting one or more third trees at a third time and planting one or more fourth trees at a fourth time. The third time follows the first time by one or more years. The first time precedes the second time by a time duration that is shorter than or equal to a lifespan of each of the one or more first trees. The third time precedes the fourth time by a time duration that is shorter than or equal to a lifespan of each of the one or more third trees.

[0014] According to certain embodiments, a non-transitory computer-readable medium stores instructions for providing renewing carbon offsets for a user driving period. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to collect driving data for one or more vehicle trips made by a user. The driving data include information related to a mindful driving behavior of the user. Also, the non-transitory computer-readable medium includes instructions to analyze the driving data to determine a level of mindful driving of the user. Additionally, the non-transitory computer-readable medium includes instructions to determine a level of carbon offset reward based at least in part upon the level of mindful driving of the user and an amount of total carbon emission of the user. Further, the non-transitory computer- readable medium includes instructions to determine a driving period of the user and estimating an amount of total carbon emission of the user for the driving period. Moreover, the non-transitory computer-readable medium includes instructions to provide an amount of carbon offset reward based at least in part upon the level of carbon offset reward and the amount of total carbon emission. The amount of carbon offset reward includes a first amount for at least planting one or more first trees at a first time and planting one or more second trees at a second time, and a second amount for at least planting one or more third trees at a third time and planting one or more fourth trees at a fourth time. The third time follows the first time by one or more years. The first time precedes the second time by a time duration that is shorter than or equal to a lifespan of each of the one or more first trees. The third time precedes the fourth time by a time duration that is shorter than or equal to a lifespan of each of the one or more third trees.

[0015] 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

[0016] Figure 1A, Figure IB and Figure 1C are a simplified method for providing renewing carbon offsets for a user driving period according to certain embodiments of the present disclosure.

[0017] Figure 2 is a simplified system for providing renewing carbon offsets for a user driving period according to certain embodiments of the present disclosure [0018] Figure 3 is a simplified computing device for providing renewing carbon offsets for a user driving period according to certain embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

[0019] Some embodiments of the present disclosure are directed to providing renewing carbon offsets. More particularly, certain embodiments of the present disclosure provide methods and systems for offering carbon offsets to compensate for carbon emissions generated during a user’s driving period. Merely by way of example, the present disclosure has been applied to offering carbon offsets through continuous self-funded tree planting during each year of the user’s driving period.

But it would be recognized that the present disclosure has much broader range of applicability.

I. ONE OR MORE METHODS FOR PROVIDING RENEWING CARBON OFFSETS FOR A USER DRIVING PERIOD ACCORDING TO CERTAIN EMBODIMENTS

[0020] Figure 1A, Figure IB and Figure 1C are a simplified method for providing renewing carbon offsets for a user driving period according to certain embodiments of the present disclosure. The diagrams are merely examples, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. The method 100 includes process 110 for collecting driving data, process 115 for determining a level of mindful driving, process 120 for determining a level of carbon offset reward, process 125 for determining a driving period, process 130 for estimating an amount of total carbon emission, process 135 for providing an amount of carbon offset reward including a first amount and a second amount, process 140 for using a first part of the first amount for planting first trees, process 145 for investing a second part of the first amount to become a third amount, process 150 for using a third part of the third amount for planting second trees, process 155 for investing a fourth part of the third amount, process 160 for investing the second amount to become a fourth amount, process 165 for using a fifth part of the fourth amount to plant third trees, process 170 for investing a sixth part of the fourth amount to become a fifth amount, process 175 for using a seventh part of the fifth amount for planting fourth trees, and process 180 for investing an eighth part of the fifth amount. 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, some or all processes of the method are performed by a 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.

[0021] At the process 110, the driving data are collected for one or more vehicle trips made by a user according to some embodiments. As an example, the driving data include information related to a mindful driving behavior of the user. For example, the driving data indicate how careful the user is in driving a vehicle, such as how frequently the user drives, type of maneuvers that the user makes while driving (e.g., hard cornering, hard braking, sudden acceleration, smooth acceleration, slowing before turning, etc.), types of road that the user drives on (e.g., highways, local roads, off-roads, etc.), number of reported accidents/collisions, types of dangerous driving events (e.g., cell phone usage while driving, eating while driving, falling asleep while driving, etc.), and/or types of safe driving events (e.g., maintaining safe following distance, turning on headlights, observing traffic lights, yielding to pedestrians, obeying speed limits, etc.).

[0022] According to certain embodiments, the driving data are collected from one or more sensors associated with the vehicle operated by the user. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, barometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, brake sensors, airbag deployment sensors, headlight sensors, steering angle sensors, gear position sensors, proximity detectors, and/or any other suitable sensors that measure vehicle state and/or operation. In some embodiments, the one or more sensors are part of or located in the vehicle. In certain embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the user) that is connected to the vehicle while the vehicle is in operation. According to some embodiments, the driving data are collected continuously or at predetermined time intervals. According to certain embodiments, the driving data are collected based on a triggering event. For example, the driving data are collected when each sensor has acquired a threshold amount of sensor measurements.

[0023] At the process 115, the driving data are analyzed to determine the level of mindful driving of the user according to certain embodiments. For example, a high level of mindful driving is determined if analysis of the driving data shows that the user always exercises safe driving with no reported accidents/collisions. As an example, a medium level of mindful driving is determined if analysis of the driving data shows that the user exercises safe driving but has one or two reported accidents/collisions. For example, a low level of mindful driving is determined if analysis of the driving data shows that the user exercises reckless driving with multiple reported accidents/collisions. In some embodiments, the level of mindful driving is represented as a numerical score. For example, a score of 90 and above indicates a high level of mindful driving of the user. In certain embodiments, mindful driving is used as a measure that incorporates collision risk, gas consumption, and/or other factors related to driving. In some embodiments, the level of mindful driving is proxied by claims data, mileage data, and/or other data related to mindful driving behaviors.

[0024] According to certain embodiments, the driving data are provided to a model (e.g., a machine learning model, a statistical model, etc.) to determine the level of mindful driving of the user. In certain embodiments, the model has been trained, and the trained model possesses existing knowledge of which features in the driving data are desirable or useful in determining whether the user exercises safe or unsafe driving. For example, determining the level of mindful driving involves that the trained model analyzes the driving data based upon the existing knowledge. As an example, analyzing the driving data includes various tasks such as performing feature extractions, applying pattern recognition, and/or other suitable tasks.

[0025] According to some embodiments, the model is an artificial neural network (e.g., a convolutional neural network, a recurrent neural network, a modular neural network, etc.) and the driving data are analyzed by the artificial neural network to determine mindful driving features that indicate whether safe or unsafe driving is being exercised. For example, obeying the speed limit is considered safe driving. As an example, slowing down while making a turn is considered safe driving. For example, texting on a cell phone while driving is considered unsafe driving. As an example, maintaining a tight following distance is considered unsafe driving. In some embodiments, the artificial neural network has been trained, and the trained artificial neural network possesses existing knowledge of which mindful driving features are desirable or useful in terms of determining the level of mindful driving. For example, determining the level of mindful driving involves that the trained artificial network analyzes the mindful driving features based upon the existing knowledge.

[0026] At the process 120, the level of carbon offset reward is determined based at least in part upon the level of mindful driving of the user according to some embodiments. For example, a high level of mindful driving produces a high level of carbon offset reward whereas a low level of mindful driving results in a low level of carbon offset reward. In certain embodiments, as long as the user maintains a high level of mindful driving, the level of carbon offset reward will be equally high regardless of how much driving has taken place.

[0027] At the process 125, the driving period of the user is determined according to some embodiments. In various embodiments, the driving period represents past and future times in which the vehicle is operated by the user. In some embodiments, the driving period includes one or more past years that the user has operated the vehicle. In certain embodiments, the driving period includes one or more future years that the user plans to operate the vehicle. For example, the user is 25 years old and started driving at the age of 20. As an example, the user plans to drive until the age of 70. For example, the driving period of the user is 50 years which includes 5 years of prior driving and 45 years of planned driving. In some embodiments, the driving period includes any driving time that the user is operating the vehicle (e.g., commuting to and from work, traveling between cities, road trips, running errands, etc.)· In certain embodiments, the driving period is determined based upon analyzing driving records of other users who share similar characteristics as the user (e.g., age, gender, occupation, hobbies, etc.)· In some embodiments, the driving period is determined based upon information from the user. For example, the user indicates what his/her driving period will be.

[0028] At the process 130, the amount of total carbon emission of the user for the driving period is estimated according to certain embodiments. For example, the amount of total carbon emission for the user’s driving period represents how much carbon pollution (e.g., carbon dioxide) the user has generated during the entire driving period.

[0029] In some embodiments, estimating the amount of total carbon emission of the user’s driving period is based upon fuel-consumption driving data and/or vehicle information collected for the one or more vehicle trips made by the user. For example, the fuel-consumption driving data indicate a quantity of fuel (e.g., gasoline) that has been consumed in operating the vehicle during the one or more vehicle trips. As an example, the fuel-consumption driving data indicate how much fuel has been consumed in view of different driving conditions (e.g., traffic conditions, road conditions, weather conditions, terrain conditions). For example, the vehicle information indicate various specifications of the vehicle operated by the user, such as model/year/make, type (e.g., hybrid), engine size, fuel economy (e.g., miles per gallon) and/or other suitable information.

[0030] In certain embodiments, a first amount of carbon emission for the one or more past years is estimated based at least in part upon analyzing the fuel- consumption driving data and/or the vehicle information collected for the one or more vehicle trips. In some embodiments, a second amount of carbon emission for the one or more future years is estimated based at least in part upon analyzing the fuel- consumption driving data and/or the vehicle information collected for the one or more vehicle trips. According to various embodiments, the fuel-consumption driving data and/or the vehicle information are analyzed using any suitable model (e.g., machine learning model, statistical model, etc.), mathematical formula, algorithm, and/or computational method (e.g., decision tree, Bayesian network, finite-state machine, support vector machine, etc.). [0031] In some embodiments, the one or more vehicle trips represent the user’s driving activity for a current year. For example, carbon emissions determined for the one or more vehicle trips represent carbon emissions of the user for the current year. As an example, the carbon emissions of the user for the current year are analyzed (e.g., extrapolated, interpolated, projected, etc.) to estimate the first amount of carbon emission for the one or more past years and the second amount of carbon emission for the one or more future years. In certain embodiments, the amount of total carbon emission for the user’s driving period is determined based at least in part upon the first amount of carbon emission and the second amount of carbon emission. For example, the first amount of carbon emission and the second amount of carbon emission are combined to determine the amount of total carbon emission for the user’s driving period.

[0032] In certain embodiments, the fuel-consumption driving data are collected from various sensors (e.g., fuel level sensors, exhaust sensors, speedometers, etc.) associated with the vehicle operated by the user. In some embodiments, the vehicle information are identified using a unique identifier of the vehicle (e.g., vehicle identification number (VIN)), which may be supplied by the user or collected from a manufacturer of the vehicle.

[0033] In some embodiments, estimating the amount of total carbon emission of the user’s driving period is based upon fueling data collected for the one or more vehicle trips made by the user. For example, the fueling data indicate how much fuel was consumed by the vehicle during the one or more vehicle trips. In certain embodiments, the fueling data are supplied by the user. As an example, the user manually inputs a certain amount of fuel that was added between a set of dates in which the one or more vehicle trips occurred. In some embodiments, the fueling data are automatically collected from one or more sensors (e.g., a fuel gauge) associated with the vehicle.

[0034] In certain embodiments, the first amount of carbon emission for the one or more past years is estimated based at least in part upon analyzing the fueling data collected for the one or more vehicle trips. In some embodiments, the second amount of carbon emission for the one or more future years is estimated based at least in part upon analyzing the fueling data collected for the one or more vehicle trips. According to various embodiments, the fueling data are analyzed using any suitable model (e.g., machine learning model, statistical model, etc.), mathematical formula, algorithm, and/or computational method (e.g., decision tree, Bayesian network, finite-state machine, support vector machine, etc.). In some embodiments, the amount of total carbon emission for the user’s driving period is determined based at least in part upon the first amount of carbon emission and the second amount of carbon emission. For example, the first amount of carbon emission and the second amount of carbon emission are combined to determine the amount of total carbon emission for the user’s driving period.

[0035] At the process 135, the amount of carbon offset reward is provided based at least in part upon the level of carbon offset reward and the amount of total carbon emission according to some embodiments. In certain embodiments, the amount of carbon offset reward corresponds to an amount of cost (e.g., money) needed for the planting of trees to compensate for the amount of total carbon emission generated by the user during the user’s driving period.

[0036] According to various embodiments, the planting of trees is carried out in a renewable fashion in which new trees are planted when already planted trees die. For example, when a tree dies, the carbon stored in the tree is released back to the atmosphere. As an example, the planting of a new tree will ensure that the carbon is permanently recaptured and stored in a tree. In some embodiments, the planting of trees is performed by a company or entity engaged in carbon emission reduction proj ects/ programs .

[0037] In certain embodiments, the amount of carbon offset reward includes the first amount for at least planting one or more first trees at a first time and planting one or more second trees at a second time, and the second amount for at least planting one or more third trees at a third time and planting one or more fourth trees at a fourth time. For example, the first and second amounts of carbon offset reward enable one or more trees to be planted during each year of the user’s driving period. As an example, the one or more trees are planted during each consecutive year of the user’s driving period.

[0038] In some embodiments, if a tree costs $X. then there is an amount $7 that would enable the planting of a new tree every year. In certain embodiments, the amount $7 is equal to $X plus $A. For example, $X corresponds to the first amount of carbon offset reward for at least planting the one or more first trees and planting the one or more second trees. As an example, $D corresponds to the second amount of carbon offset reward for at least planting the one or more third trees and planting the one or more fourth trees.

[0039] In certain embodiments, if $D is determined based upon a perpetuity formula and the amount $7 is equal to %X l i + $X (where i is an available long-term real interest rate), then the amount $7 would enable the planting of a new tree every year forever. In some embodiments, if $D is determined based upon an annuity formula, then the amount $7 would enable the planting of a new tree every year for a predetermined number of years (e.g., planting a new tree every year for 30 years so that 30 trees will be planted in total).

[0040] In certain embodiments, the user may completely offset the carbon emissions generated during the user’s driving period by planting, for example 20 trees/year, during the user’s driving period (e.g., assuming the driving period is 50 years). In some embodiments, if the user pays 20*$7 at the present time, then the user would become potentially carbon neutral (from driving) by planting 20 trees/year for 50 years for a total of 1000 trees.

[0041] In various embodiments, planting of the one or more third trees at the third time follows planting of the one or more first trees at the first time by one or more years. In some embodiments, the third time follows the first time by only one year. For example, the one or more first trees are planted in year 1 and the one or more third trees are planted in year 2. In certain embodiments, the first time precedes the second time by a first time duration that is shorter than or equal to a first lifespan of each of the one or more first trees. In some embodiments, the third time precedes the fourth time by a second time duration that is shorter than or equal to a second lifespan of each of the one or more third trees. In certain embodiments, the first and second lifespans are the same, and the first and second time durations are the same.

[0042] In various embodiments, the first amount of carbon offset reward is used for planting one or more trees during a current year of the user’s driving period. In some embodiments, the first amount includes the first part and the second part. At the process 140, the first part of the first amount is used for planting the one or more first trees at the first time according to some embodiments. At the process 145, the second part of the first amount is invested (e.g., in stocks, mutual funds, savings account, etc.) during the first time duration according to certain embodiments. For example, the second part of the first amount is invested so that it can grow to become the third amount for the subsequent planting of new trees. In some embodiments, the third amount includes the third part and the fourth part.

[0043] At the process 150, after the first time duration, the third part of the third amount is used to plant the one or more second trees at the second time according to certain embodiments. At the process 155, the fourth part of the third amount is invested for planting one or more fifth trees at a fifth time according to some embodiments. For example, the second time precedes the fifth time by a third time duration that is shorter than or equal to a third lifespan of each of the one or more second trees. In some embodiments, the fourth part of the third amount is invested so that it can grow to become an additional amount, part of which is used to plant the one or more fifth trees at the fifth time and part of which is again invested for the planting of additional trees.

[0044] In various embodiments, the second amount of carbon offset reward is used for planting one or more trees during subsequent years of the user’s driving period. In some embodiments, the second amount is used to plant trees after one or more years of using the first amount to plant trees. At the process 160, the second amount is invested during the one or more years to become the fourth amount according to certain embodiments. In some embodiments, the fourth amount includes the fifth part and the sixth part.

[0045] At the process 165, the fifth part of the fourth amount is used to plant the one or more third trees at the third time according to certain embodiments. At the process 170, the sixth part of the fourth amount is invested during the second time duration according to some embodiments. For example, the sixth part of the fourth amount is invested so that it can grow to become the fifth amount which can be used for planting new trees in succeeding years. In certain embodiments, the fifth amount includes the seventh part and the eighth part.

[0046] At the process 175, after the second time duration, the seventh part of the fifth amount is used to plant the one or more fourth trees at the fourth time according to some embodiments. At the process 180, the eighth part of the fifth amount is invested for planting one or more sixth trees at a sixth time according to certain embodiments. For example, the fourth time precedes the sixth time by a fourth time duration that is shorter than or equal to a fourth lifespan of each of the one or more fourth trees. In some embodiments, the eighth part of the fifth amount is invested so that it can grow to become additional amounts, part of which are used to plant the one or more sixth trees at the sixth time and part of which are again invested for the planting of additional trees in future years.

[0047] According to various embodiments, the process 135, the process 140, the process 145, the process 150, the process 155, the process 160, the process 165, the process 170, the process 175, and/or the process 180 are repeated continuously unless interrupted by external instructions so that carbon emissions generated during the driving period of the user are effectively captured and stored. In some embodiments, each of the first amount and the second amount of carbon offset reward is always divided into two parts, with one part being used for the initial planting of trees and the other part being invested for the subsequent planting of additional trees in the future to replace and/or supplement the initially planted trees.

[0048] In certain embodiments, the process 135, the process 140, the process 145, the process 150, the process 155, the process 160, the process 165, the process 170, the process 175, and/or the process 180 operate to continuously capture, store and recapture carbon emissions generated during the driving period of the user in the form of an eternal tree. As an example, the process 135, the process 140, the process 145, the process 150, the process 155, the process 160, the process 165, the process 170, the process 175, and/or the process 180 are repeated for an infinite number of times. II. ONE OR MORE SYSTEMS FOR PROVIDING RENEWING CARBON OFFSETS FOR A USER DRIVING PERIOD ACCORDING TO CERTAIN EMBODIMENTS

[0049] Figure 2 is a simplified system for providing renewing carbon offsets for a user driving period 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. The system 200 includes a vehicle system 202, a network 204, and a server 206. 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.

[0050] In various embodiments, the system 200 is used to implement the method 100. According to certain embodiments, the vehicle system 202 includes a vehicle 210 and a client device 212 associated with the vehicle 210. For example, the client device 212 is an on-board computer embedded or located in the vehicle 210. As an example, the client device 212 is a mobile device (e.g., a smartphone) that is connected (e.g., via wired or wireless links) to the vehicle 210. As an example, the client device 212 includes a processor 216 (e.g., a central processing unit (CPU), a graphics processing unit (GPU)), a memory 218 (e.g., random-access memory (RAM), read-only memory (ROM), flash memory), a communications unit 220 (e.g., a network transceiver), a display unit 222 (e.g., a touchscreen), and one or more sensors 224 (e.g., an accelerometer, a gyroscope, a magnetometer, a barometer, a GPS sensor).

[0051] In some embodiments, the vehicle 210 is operated by the user. In certain embodiments, multiple vehicles 210 exist in the system 200 which are operated by respective users. As an example, during vehicle trips, the one or more sensors 224 monitor the vehicle 210 by collecting data associated with various operating parameters of the vehicle, such as speed, acceleration, braking, location, engine status, fuel level, as well as other suitable parameters. In certain embodiments, the collected data include vehicle telematics data. According to some embodiments, the 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 various embodiments, the collected data represent the driving data in the method 100.

[0052] According to certain embodiments, the collected data are stored in the memory 218 before being transmitted to the server 206 using the communications unit 220 via the network 204 (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 206 via the network 204. In certain embodiments, the collected data are transmitted to the server 206 via a third party. For example, a data monitoring system stores any and all data collected by the one or more sensors 224 and transmits those data to the server 206 via the network 204 or a different network. [0053] According to certain embodiments, the server 206 includes a processor 230 (e.g., a microprocessor, a microcontroller), a memory 232, a communications unit 234 (e.g., a network transceiver), and a data storage 236 (e.g., one or more databases). In some embodiments, the server 206 is a single server, while in certain embodiments, the server 206 includes a plurality of servers with distributed processing. In Figure 2, the data storage 236 is shown to be part of the server 206. In some embodiments, the data storage 236 is a separate entity coupled to the server 206 via a network such as the network 204. In certain embodiments, the server 206 includes various software applications stored in the memory 232 and executable by the processor 230. For example, these software applications include specific programs, routines, or scripts for performing functions associated with the method 100. 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.

[0054] According to various embodiments, the server 206 receives, via the network 204, the data collected by the one or more sensors 224 using the communications unit 234 and stores the data in the data storage 236. For example, the server 206 then processes the data to perform one or more processes of the method 100

[0055] According to certain embodiments, any related information determined or generated by the method 100 (e.g., mindful driving score, amount of carbon offset reward, planting of trees, etc.) are transmitted back to the client device 212, via the network 204, to be provided (e.g., displayed) to the user via the display unit 222. [0056] In some embodiments, one or more processes of the method 100 are performed by the client device 212. For example, the processor 216 of the client device 212 processes the data collected by the one or more sensors 224 to perform one or more processes of the method 100.

III. ONE OR MORE COMPUTING DEVICES FOR PROVIDING

RENEWING CARBON OFFSETS FOR A USER DRIVING PERIOD ACCORDING TO CERTAIN EMBODIMENTS

[0057] Figure 3 is a simplified computing device for providing renewing carbon offsets for a user driving period 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. The computing device 300 includes a processing unit 304, a memory unit 306, an input unit 308, an output unit 310, a communication unit 312, and a storage unit 314. In various embodiments, the computing device 300 is configured to be in communication with a user 316 and/or a storage device 318. In some embodiments, the computing device 300 is configured to implement the method 100 of Figure 1A, Figure IB, and/or Figure 1C. 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.

[0058] In various embodiments, the processing unit 304 is configured for executing instructions, such as instructions to implement the method 100 of Figure 1A, Figure IB, and/or Figure 1C. In some embodiments, the executable instructions are stored in the memory unit 306. In certain embodiments, the processing unit 304 includes one or more processing units (e.g., in a multi-core configuration). In some embodiments, the processing unit 304 includes and/or is communicatively coupled to one or more modules for implementing the methods and systems described in the present disclosure. In certain embodiments, the processing unit 304 is configured to execute instructions within one or more operating systems. In some embodiments, upon initiation of a computer-implemented method, one or more instructions is executed during initialization. In certain embodiments, one or more operations is executed to perform one or more processes described herein. In some embodiments, an operation may be general or specific to a particular programming language (e.g., C, C++, Java, or other suitable programming languages, etc.).

[0059] In various embodiments, the memory unit 306 includes a device allowing information, such as executable instructions and/or other data to be stored and retrieved. In some embodiments, the memory unit 306 includes one or more computer readable media. In certain embodiments, the memory unit 306 includes computer readable instructions for providing a user interface, such as to the user 316, via the output unit 310. In some embodiments, a user interface includes a web browser and/or a client application. For example, a web browser enables the user 316 to interact with media and/or other information embedded on a web page and/or a website. In certain embodiments, the memory unit 306 includes computer readable instructions for receiving and processing an input via the input unit 308. In some embodiments, the memory unit 306 includes RAM such as dynamic RAM (DRAM) or static RAM (SRAM), ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and/or non volatile RAM (NVRAM).

[0060] In various embodiments, the input unit 308 is configured to receive input (e.g., from the user 316). In some embodiments, the input unit 308 includes a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad or touch screen), a gyroscope, an accelerometer, a position sensor (e.g., GPS sensor), and/or an audio input device. In certain embodiments, the input unit 308 is configured to function as both an input unit and an output unit.

[0061] In various embodiments, the output unit 310 includes a media output unit configured to present information to the user 316. In some embodiments, the output unit 310 includes any component capable of conveying information to the user 316.

In certain embodiments, the output unit 310 includes an output adapter such as a video adapter and/or an audio adapter. For example, the output unit 310 is operatively coupled to the processing unit 304 and/or a visual display device to present information to the user 316 (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, a projected display, etc.). As an example, the output unit 310 is operatively coupled to the processing unit 304 and/or an audio display device to present information to the user 316 (e.g., a speaker arrangement or headphones).

[0062] In various embodiments, the communication unit 312 is configured to be communicatively coupled to a remote device. In some embodiments, the communication unit 312 includes a wired network adapter, a wireless network adapter, a wireless data transceiver for use with a mobile phone network (e.g., 3G, 4G, 5G, Bluetooth, etc.), and/or other mobile data networks. In certain embodiments, other types of short-range or long-range networks may be used. In some embodiments, the communication unit 312 is configured to provide email integration for communicating data between a server and one or more clients. [0063] In various embodiments, the storage unit 314 is configured to enable communication between the computing device 300 and the storage device 318. In some embodiments, the storage unit 314 is a storage interface. For example, the storage interface is any component capable of providing the processing unit 304 with access to the storage device 318. In certain embodiments, the storage unit 314 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 304 with access to the storage device 318.

[0064] In various embodiments, the storage device 318 includes any computer operated hardware suitable for storing and/or retrieving data. In certain embodiments, the storage device 318 is integrated in the computing device 300. In some embodiments, the storage device 318 includes a database such as a local database or a cloud database. In certain embodiments, the storage device 318 includes one or more hard disk drives. In some embodiments, the storage device 318 is external and is configured to be accessed by a plurality of server systems. In certain embodiments, the storage device 318 includes multiple storage units such as hard disks or solid state disks in a redundant array of inexpensive disks configuration. In some embodiments, the storage device 318 includes a storage area network and/or a network attached storage system.

IV. EXAMPLES OF CERTAIN EMBODIMENTS OF THE PRESENT DISCLOSURE

[0065] According to certain embodiments, a method for providing renewing carbon offsets for a user driving period includes collecting driving data for one or more vehicle trips made by a user. The driving data include information related to a mindful driving behavior of the user. Also, the method includes analyzing the driving data to determine a level of mindful driving of the user. Additionally, the method includes determining a level of carbon offset reward based at least in part upon the level of mindful driving of the user. Further, the method includes determining a driving period of the user and estimating an amount of total carbon emission of the user for the driving period. Moreover, the method includes providing an amount of carbon offset reward based at least in part upon the level of carbon offset reward and the amount of total carbon emission. The amount of carbon offset reward includes a first amount for at least planting one or more first trees at a first time and planting one or more second trees at a second time, and a second amount for at least planting one or more third trees at a third time and planting one or more fourth trees at a fourth time. The third time follows the first time by one or more years. The first time precedes the second time by a time duration that is shorter than or equal to a lifespan of each of the one or more first trees. The third time precedes the fourth time by a time duration that is shorter than or equal to a lifespan of each of the one or more third trees. For example, the method is implemented according to at least Figure 1 A,

Figure IB, and/or Figure 1C.

[0066] According to some embodiments, a computing device for providing renewing carbon offsets for a user driving period 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 collect driving data for one or more vehicle trips made by a user. The driving data include information related to a mindful driving behavior of the user. Also, the instructions, when executed, cause the one or more processors to analyze the driving data to determine a level of mindful driving of the user. Additionally, the instructions, when executed, cause the one or more processors to determine a level of carbon offset reward based at least in part upon the level of mindful driving of the user and an amount of total carbon emission of the user. Further, the instructions, when executed, cause the one or more processors to determine a driving period of the user and estimating an amount of total carbon emission of the user for the driving period. Moreover, the instructions, when executed, cause the one or more processors to provide an amount of carbon offset reward based at least in part upon the level of carbon offset reward and the amount of total carbon emission. The amount of carbon offset reward includes a first amount for at least planting one or more first trees at a first time and planting one or more second trees at a second time, and a second amount for at least planting one or more third trees at a third time and planting one or more fourth trees at a fourth time. The third time follows the first time by one or more years. The first time precedes the second time by a time duration that is shorter than or equal to a lifespan of each of the one or more first trees. The third time precedes the fourth time by a time duration that is shorter than or equal to a lifespan of each of the one or more third trees. For example, the computing device is implemented according to at least Figure 2 and/or Figure 3.

[0067] According to certain embodiments, a non-transitory computer-readable medium stores instructions for providing renewing carbon offsets for a user driving period. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to collect driving data for one or more vehicle trips made by a user. The driving data include information related to a mindful driving behavior of the user. Also, the non-transitory computer-readable medium includes instructions to analyze the driving data to determine a level of mindful driving of the user. Additionally, the non-transitory computer-readable medium includes instructions to determine a level of carbon offset reward based at least in part upon the level of mindful driving of the user and an amount of total carbon emission of the user. Further, the non-transitory computer- readable medium includes instructions to determine a driving period of the user and estimating an amount of total carbon emission of the user for the driving period. Moreover, the non-transitory computer-readable medium includes instructions to provide an amount of carbon offset reward based at least in part upon the level of carbon offset reward and the amount of total carbon emission. The amount of carbon offset reward includes a first amount for at least planting one or more first trees at a first time and planting one or more second trees at a second time, and a second amount for at least planting one or more third trees at a third time and planting one or more fourth trees at a fourth time. The third time follows the first time by one or more years. The first time precedes the second time by a time duration that is shorter than or equal to a lifespan of each of the one or more first trees. The third time precedes the fourth time by a time duration that is shorter than or equal to a lifespan of each of the one or more third trees. For example, the non-transitory computer- readable medium is implemented according to at least Figure 1A, Figure IB, Figure 1C, Figure 2, and/or Figure 3.

V. EXAMPLES OF MACHINE LEARNING ACCORDING TO CERTAIN EMBODIMENTS

[0068] 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 leams 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.

[0069] 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.

[0070] 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.

VI. ADDITIONAL CONSIDERATIONS ACCORDING TO CERTAIN EMBODIMENTS

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] The computing system can include client devices and servers. A client device and server are generally remote from each other and typically interact through a communication network. The relationship of client device and server arises by virtue of computer programs running on the respective computers and having a client device-server relationship to each other.

[0076] 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.

[0077] 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.

[0078] 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.