US20230016022A1

US20230016022A1 - Systems and methods for offering carbon offset rewards that correspond to users

Info

Publication number: US20230016022A1
Application number: US17/935,246
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; US20230131645A1; WO2021195019A1; US20230016696A1; US20230020291A1; WO2021195056A1; EP4128108A4; EP4128013A4; US20230016482A1; WO2021195006A1; WO2021195053A1

Abstract

Method and system for offering carbon offset rewards corresponding to users. For example, the method includes detecting actions attributable to a user, determining a level of mindful driving and additional characteristics of the user based upon the attributable actions, determining the user's quality based upon the level of mindful driving and additional characteristics, determining a level of carbon offset reward based upon the user's quality, and offering an amount of carbon offset reward to the user based upon the level of carbon offset reward, where the amount of carbon offset reward includes a first amount for planting a first set of trees at a first time and a second amount for planting a second set of trees at a second time with the first time preceding the second time by a time duration that is shorter than or equal to the lifespan of each of the first set of trees.

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 for all purposes.
International PCT Application No. PCT/US21/18233, titled “System and Methods for Providing Renewing Carbon Offsets” is incorporated by reference herein for all purposes.
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:
1. International PCT application Ser. No.______, titled “Systems and Methods for Offering Carbon Offset Rewards that Correspond to Users” (Attorney Docket Number BOL-00007A-PCT);
2. International PCT application Ser. No.______, titled “Systems and Methods for Providing Multiple Carbon Offset Sources” (Attorney Docket Number BOL-00007B-PCT);
3. International PCT application Ser. No.______, titled “Systems and Methods for Generating Tree Imagery” (Attorney Docket Number BOL-00007G-PCT);
4. International PCT application Ser. No.______, titled “Systems and Methods for Validating Planting of Trees” (Attorney Docket Number BOL-00007H-PCT); and
5. International PCT application Ser. 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

Some embodiments of the present disclosure are directed to offering carbon offset rewards that correspond to a user. More particularly, certain embodiments of the present disclosure provide methods and systems for offering the carbon offset rewards to the user based upon the user's driving behavior and other user characteristics. Merely by way of example, the present disclosure has been applied to offering carbon offset rewards in view of a perceived quality of the user as a potential or existing customer to a business. But it would be recognized that the present disclosure has much broader range of applicability.

BACKGROUND OF THE DISCLOSURE

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 such as offering carbon offset rewards to compensate for the release of these carbon emissions.

BRIEF SUMMARY OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to offering carbon offset rewards that correspond to a user. More particularly, certain embodiments of the present disclosure provide methods and systems for offering the carbon offset rewards to the user based upon the user's driving behavior and other user characteristics. Merely by way of example, the present disclosure has been applied to offering carbon offset rewards in view of a perceived quality of the user as a potential or existing customer to a business. But it would be recognized that the present disclosure has much broader range of applicability.
According to certain embodiments, a method for offering a carbon offset reward corresponding to a user includes detecting one or more actions attributable to the user. Also, the method includes determining a plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user including a level of mindful driving of the user and one or more additional characteristics of the user. Additionally, the method includes processing information associated with the level of mindful driving of the user and the one or more additional characteristics of the user, and determining a quality of the user as a perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user. Further, the method includes determining a level of carbon offset reward based at least in part upon the quality of the user as the perspective or existing customer. Moreover, the method includes offering an amount of carbon offset reward to the user based at least in part upon the level of carbon offset reward. The amount of carbon offset reward includes a first amount for planting one or more first trees at a first time and a second amount for planting one or more second trees at a second time. 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.
According to some embodiments, a computing device for offering a carbon offset reward corresponding to a user 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 detect one or more actions attributable to the user. Also, the instructions, when executed, cause the one or more processors to analyze the driving data to determine a plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user including a level of mindful driving of the user and one or more additional characteristics of the user. Additionally, the instructions, when executed, cause the one or more processors to process information associated with the level of mindful driving of the user and the one or more additional characteristics of the user, and determine a quality of the user as a perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user. Further, 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 quality of the user as the perspective or existing customer. Moreover, the instructions, when executed, cause the one or more processors to offer an amount of carbon offset reward to the user based at least in part upon the level of carbon offset reward. The amount of carbon offset reward includes a first amount for planting one or more first trees at a first time and a second amount for planting one or more second trees at a second time. 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.
According to certain embodiments, a non-transitory computer-readable medium stores instructions for offering a carbon offset reward corresponding to a user. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to detect one or more actions attributable to the user. Also, the non-transitory computer-readable medium includes instructions to determine a plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user including a level of mindful driving of the user and one or more additional characteristics of the user. Additionally, the non-transitory computer-readable medium includes instructions to process information associated with the level of mindful driving of the user and the one or more additional characteristics of the user, and to determine a quality of the user as a perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user. Further, the non-transitory computer-readable medium includes instructions to determine a level of carbon offset reward based at least in part upon the quality of the user as the perspective or existing customer. Moreover, the non-transitory computer-readable medium includes instructions to offer an amount of carbon offset reward to the user based at least in part upon the level of carbon offset reward. The amount of carbon offset reward includes a first amount for planting one or more first trees at a first time and a second amount for planting one or more second trees at a second time. 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.
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. 1A and FIG. 1B are a simplified method for offering carbon offset rewards that correspond to a user according to certain embodiments of the present disclosure.

FIG. 2 is a simplified system for offering carbon offset rewards that correspond to a user according to some embodiments of the present disclosure.

FIG. 3 is a simplified computing device for offering carbon offset rewards that correspond to a user according to certain embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

I. One or More Methods for Offering Carbon Offset Rewards that Correspond to a User According to Certain Embodiments

FIG. 1A and FIG. 1B are a simplified method for offering carbon offset rewards that correspond to a user 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 detecting actions attributable to the user, process 115 for determining a plurality of characteristics of the user, process 120 for processing information associated with the plurality of characteristics, process 125 for determining a quality of the user, process 130 for determining a level of carbon offset reward, process 135 for offering an amount of carbon offset reward including a first amount for planting first trees at a first time and a second amount for planting second trees at a second time, process 140 for using the first amount for planting the first trees at the first time, process 145 for investing the second amount to become a third amount, process 150 for using a first part of the third amount for planting the second trees at the second time, and process 155 for investing a second part of the third amount for planting third trees at a third time. 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.
At the process 110, one or more actions attributable to the user are detected according to some embodiments. In various embodiments, the one or more actions relate to any number of everyday activities carried out by the user. In some embodiments, the one or more actions are automatically logged by a computing device (e.g., mobile device) that the user is carrying or using. In certain embodiments, the one or more actions are manually recorded by the user in the computing device. For example, the one or more actions relate to the user making a vehicle trip. As an example, the one or more actions relate to the user interacting with a device. For example, the one or more actions relate to the user completing a certain business task such as completing a quote from an insurance company.
At the process 115, the plurality of characteristics related to the user are determined based at least in part upon the one or more actions attributable to the user according to certain embodiments. For example, the plurality of characteristics include a level of mindful driving of the user and one or more additional characteristics of the user.
According to various embodiments, the one or more actions attributable to the user include the user making one or more vehicle trips. In some embodiments, driving data associated with the one or more vehicle trips are analyzed to determine the level of mindful driving of the user and/or the one or more additional characteristics of the user. For example, the driving data indicate how the user drives, such as how frequently the user drives, type of vehicle that the user drives (e.g., model/year/make), 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 dangerous driving events (e.g., cell phone usage while driving, eating while driving, falling asleep while driving, etc.), types of safe driving events (e.g., maintaining safe following distance, turning on headlights, observing traffic lights, yielding to pedestrians, obeying speed limits, etc.), and/or number of reported accidents/collisions.
In certain embodiments, the driving data are collected from one or more sensors associated with a 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 mobile device 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.
In some embodiments, the level of mindful driving of the user is determined based at least in part upon the analyzed driving data. 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 0-40 represents a low level of mindful driving, a score of 40-85 represents a medium level of mindful driving, and a score of 85+ represents a high level of mindful driving. 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.
In certain embodiments, the one or more additional characteristics of the user are determined based at least in part upon the analyzed driving data. For example, the one or more additional characteristics may indicate that the user has reduced a number of the one or more vehicle trips when analysis of the driving data shows less driving on the part of the user. As an example, the one or more additional characteristics may indicate that the user has improved his/her fuel consumption when analysis of the driving data shows the user has switched to driving a more fuel-efficient vehicle.
According to some embodiments, the one or more actions attributable to the user include the user making a trip between a particular pair of origination and destination points. For example, the trip may be a commute between the user's home and the user's workplace. In certain embodiments, trip data associated with the trip are analyzed to determine the one or more additional characteristics of the user. For example, the one or more additional characteristics may indicate that the user is using an alternate form of transportation to make the trip when analysis of the trip data shows the user walking, biking, and/or riding public transit to travel between the particular pair of origination and destination points.
According to certain embodiments, the one or more actions attributable to the user include the user interacting with a mobile device. In some embodiments, mobile device data associated with the user interacting with the mobile device are analyzed to determine the one or more additional characteristics of the user. For example, the one or more additional characteristics may indicate that the user is using an application installed on the mobile device for a certain number of times when analysis of the mobile device data shows the user repeatedly accessing the application on the mobile device during a specified period. As an example, the one or more additional characteristics may indicate that the user is referring a friend to use the application when analysis of the mobile device data shows the user texting a link to the friend and receiving a notice that the friend has downloaded the application on the friend's mobile device using the link.
In various embodiments, relevant data (e.g., driving data, trip data, mobile device data) are provided to a model (e.g., a machine learning model, a statistical model, etc.) to determine the plurality of characteristics related to the user such as the level of mindful driving of the user and the one or more additional characteristics of the user. In certain embodiments, the model is an artificial neural network (e.g., a convolutional neural network, a recurrent neural network, a modular neural network, etc.). In some embodiments, the model has been trained, and the trained model possesses existing knowledge of which features in the relevant data are desirable or useful in determining the plurality of characteristics. For example, determining the plurality of characteristics involves that the trained model analyzes the relevant data based upon the existing knowledge. As an example, analyzing the relevant data includes various tasks such as performing feature extractions, applying pattern recognition, and/or other suitable tasks. In certain embodiments, other suitable computational methods (e.g., decision tree, Bayesian network, finite-state machine, support vector machine, etc.) may be used to analyze the relevant data and determine the plurality of characteristics.
At the process 120, information associated with the level of mindful driving of the user and the one or more additional characteristics of the user are processed according to some embodiments. For example, the information associated with the level of mindful driving and the one or more additional characteristics are formatted to be stored in a database. As an example, the information the information associated with the level of mindful driving and the one or more additional characteristics are used to create a profile of the user along with other user identification data. For example, the information associated with the level of mindful driving and the one or more additional characteristics are formatted to be displayed on the user's mobile device (e.g., display the level of mindful driving as a score).
At the process 125, the quality of the user as a perspective or existing customer is determined based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user according to certain embodiments. In some embodiments, the level of mindful driving indicates the user's propensity to be a safe driver. For example, the user will be afforded a greater amount insurance discount (e.g., reduction in insurance premium, reduction in premium renewal, etc.) from an insurance company if the level of mindful driving is high. As an example, the user will be incentivized to engage in business with the insurance company (e.g., become a new customer or continue as a current customer). For example, the quality of the user as a customer to the insurance company will be high as a result.
In certain embodiments, the one or more additional characteristics indicate the user's propensity to interact with a business (e.g., insurance company). For example, the quality of the user can be represented by an expected business value of the user. In various embodiments, the expected business value of the user is determined based at least in part upon a probability that the user will become a customer of the business and an estimated revenue from the user if the user becomes the customer of the business. For example, determining the probability that the user will become the customer is based at least in part upon a probability that the user will initiate a quote (e.g., insurance quote), a probability that the user will complete the quote, and a probability that the user will submit the completed quote. As an example, determining the estimated revenue from the user if the user becomes the customer is based at least in part upon an amount of money that the user will bring to or spend on the business. For example, the expected business value of the user is determined based at least in part upon the estimated revenue from the user multiplied by the probability that the user will submit the quote multiplied by the probability that the user will complete the quote multiplied by the probability that the user will even initiate the quote in the first place. According to some embodiments, the estimated revenue from the user and the probabilities of initiating, completing, and/or submitting the quote are based at least in part upon the business needs and/or personalities of the user.
At the process 130, the level of carbon offset reward is determined based at least in part upon the quality of the user as the perspective or existing customer according to certain embodiments. For example, a high value for the quality of the user (e.g., user is perceived to be an exceptional customer) produces a high level of carbon offset reward. As an example, a lower value for the quality of the user (e.g., user is perceived to be an ordinary customer) results in a low level of carbon offset reward.
At the process 135, the amount of carbon offset reward is offered to the user based at least in part upon the level of carbon offset reward according to some embodiments. For example, the amount of carbon offset reward is used to incentivize the user to engage with a business. In certain embodiments, the amount of carbon offset reward corresponds to an amount of cost (e.g., money) needed for planting of trees. As an example, the amount of cost needed for the planting of trees may be waived for the user if a high level of carbon offset reward is determined for the user.
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 an organization engaged in carbon emission reduction projects/programs.
In some embodiments, the amount of carbon offset reward includes the first amount for planting one or more first trees at the first time, and the second amount for planting one or more second trees at the second time. For example, 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 examples, if a tree has a lifespan of 25 years, then a new tree is planted at the 15-year mark to ensure that there will always be a tree to store the carbon in the original tree.
At the process 140, the first amount of carbon offset reward is used to plant the one or more first trees at the first time according to certain embodiments. At the process 145, the second amount of carbon offset reward is invested (e.g., in stocks, mutual funds, savings account, etc.) during the first time duration according to some embodiments. For example, the second amount is invested so that it can grow to become a third amount needed for the subsequent planting of new trees at later times. In some embodiments, the third amount includes a first part and a second part.
At the process 150, after the first time duration, the first 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 second part of the third amount is invested for planting one or more third trees at a third time according to some embodiments. For example, the second time precedes the third time by a second time duration that is shorter than or equal to a second lifespan of each of the one or more second trees. In certain embodiments, the second part is invested so that it can grow to become a fourth amount that includes a third part and a fourth part. For example, the third part is used to plant the one or more third trees at the third time, and the fourth part is again invested for the planting of additional or future trees (e.g., planting of one or more fourth trees at a fourth time).
According to various embodiments, the process 135, the process 140, the process 145, the process 150, and/or the process 155 are repeated continuously unless interrupted by external instructions so that carbon emissions by the user are effectively captured and stored for a predetermined period of time. For example, the predetermined period of time is longer than one lifespan of a tree. In some embodiments, the amount of carbon offset reward is always divided into two parts, with one part being used to plant one or more present trees and the other part being invested such that additional trees are planted in the future to replace and/or supplement the one or more present trees. In certain embodiments, the process 135, the process 140, the process 145, the process 150, and/or the process 155 are repeated for an infinite number of times.

II. One or More Systems for Offering Carbon Offset Rewards that Correspond to a User According to Certain Embodiments

FIG. 2 is a simplified system for offering carbon offset rewards that correspond to a user 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.
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).
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.
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.
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 FIG. 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.
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.
According to certain embodiments, any related information determined or generated by the method 100 (e.g., level of mindful driving, amount of carbon offset reward, planting of current and/or future 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.
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 Offering Carbon Offset Rewards that Correspond to a User According to Certain Embodiments

FIG. 3 is a simplified computing device for offering carbon offset rewards that correspond to a user 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 FIG. 1A and/or FIG. 1B. 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 processing unit 304 is configured for executing instructions, such as instructions to implement the method 100 of FIG. 1A and/or FIG. 1B. 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.).
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).
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.
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).
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.
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.
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

According to certain embodiments, a method for offering a carbon offset reward corresponding to a user includes detecting one or more actions attributable to the user. Also, the method includes determining a plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user including a level of mindful driving of the user and one or more additional characteristics of the user. Additionally, the method includes processing information associated with the level of mindful driving of the user and the one or more additional characteristics of the user, and determining a quality of the user as a perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user. Further, the method includes determining a level of carbon offset reward based at least in part upon the quality of the user as the perspective or existing customer. Moreover, the method includes offering an amount of carbon offset reward to the user based at least in part upon the level of carbon offset reward. The amount of carbon offset reward includes a first amount for planting one or more first trees at a first time and a second amount for planting one or more second trees at a second time. 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. For example, the method is implemented according to at least FIG. 1A and/or FIG. 1B.
As an example, the method for offering the carbon offset reward further includes using the first amount for planting the one or more first trees at the first time. During the first time duration, the method includes investing the second amount to become a third amount including a first part and a second part. After the first time duration, the method includes using the first part of the third amount for planting the one or more second trees at the second time. Moreover, the method includes investing the second part of the third amount for planting one or more third trees at a third time. The second time precedes the third time by a second time duration that is shorter than or equal to a second lifespan corresponding to each of the one or more second trees. For example, the method is implemented according to at least FIG. 1A and/or FIG. 1B.
According to some embodiments, a computing device for offering a carbon offset reward corresponding to a user 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 detect one or more actions attributable to the user. Also, the instructions, when executed, cause the one or more processors to analyze the driving data to determine a plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user including a level of mindful driving of the user and one or more additional characteristics of the user. Additionally, the instructions, when executed, cause the one or more processors to process information associated with the level of mindful driving of the user and the one or more additional characteristics of the user, and determine a quality of the user as a perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user. Further, 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 quality of the user as the perspective or existing customer. Moreover, the instructions, when executed, cause the one or more processors to offer an amount of carbon offset reward to the user based at least in part upon the level of carbon offset reward. The amount of carbon offset reward includes a first amount for planting one or more first trees at a first time and a second amount for planting one or more second trees at a second time. 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. For example, the computing device is implemented according to at least FIG. 2 and/or FIG. 3 .
According to some embodiments, a non-transitory computer-readable medium stores instructions for offering a carbon offset reward corresponding to a user. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to detect one or more actions attributable to the user. Also, the non-transitory computer-readable medium includes instructions to determine a plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user including a level of mindful driving of the user and one or more additional characteristics of the user. Additionally, the non-transitory computer-readable medium includes instructions to process information associated with the level of mindful driving of the user and the one or more additional characteristics of the user, and to determine a quality of the user as a perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user. Further, the non-transitory computer-readable medium includes instructions to determine a level of carbon offset reward based at least in part upon the quality of the user as the perspective or existing customer. Moreover, the non-transitory computer-readable medium includes instructions to offer an amount of carbon offset reward to the user based at least in part upon the level of carbon offset reward. The amount of carbon offset reward includes a first amount for planting one or more first trees at a first time and a second amount for planting one or more second trees at a second time. 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. For example, the non-transitory computer-readable medium is implemented according to at least FIG. 1A, FIG. 1B, FIG. 2 , and/or FIG. 3 .

V. 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.

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 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.
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 method for offering a carbon offset reward corresponding to a user, the method comprising:

detecting, by a computing device, one or more actions attributable to the user;

determining, by the computing device; a plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user, the plurality of characteristics including a level of mindful driving of the user and one or more additional characteristics of the user;

processing; by the computing device, information associated with the level of mindful driving of the user and the one or more additional characteristics of the user;

determining, by the computing device, a quality of the user as a perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user;

determining, by the computing device, a level of carbon offset reward based at least in part upon the quality of the user as the perspective or existing customer; and

offering, by the computing device, an amount of carbon offset reward to the user based at least in part upon the level of carbon offset reward;

wherein:

the amount of carbon offset reward includes a first amount for planting one or more first trees at a first time and a second amount for planting one or more second trees at a second time;

each of the one or more 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.

2. The method of claim 1, wherein:

the one or more actions attributable to the user include the user making one or more vehicle trips; and

the determining, by the computing device, the plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user includes:

analyzing driving data associated with the one or more vehicle trips; and

determining the level of mindful driving of the user based at least in part upon the driving data.

3. The method of claim 1, wherein:

analyzing driving data associated with the one or more vehicle trips; and

determining the one or more additional characteristics of the user based at least in part upon the driving data, the one or more additional characteristics being the user reducing a number of the one or more vehicle trips.

4. The method of claim 1, wherein:

the one or more actions attributable to the user include the user making a trip between a particular pair of origination and destination points; and

analyzing trip data associated with the trip between the particular pair of origination and destination points; and

determining the one or more additional characteristics of the user based at least in part upon the trip data, the one or more additional characteristics being the user using an alternate form of transportation to make the trip between the particular pair of origination and destination points.

5. The method of claim 1, wherein:

the one or more actions attributable to the user include the user interacting with a mobile device; and

analyzing mobile device data associated with the user interacting with the mobile device; and

determining the one or more additional characteristics of the user based at least in part upon the mobile device data, the one or more additional characteristics being the user using an application installed on the mobile device for a certain number of times.

6. The method of claim 1, wherein

determining the one or more additional characteristics of the user based at least in part upon the mobile device data, the one or more additional characteristics being the user referring a friend to use an application installed on the mobile device.

7. The method of claim 1, wherein the determining, by the computing device, the quality of the user as the perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user includes:

determining a probability that the user will become a customer and an estimated revenue from the user if the user becomes the customer; and

determining an expected business value of the user based at least in part upon the probability that the user will become the customer and the estimated revenue from the user if the user becomes the customer, the expected business value of the user indicating the quality of the user.

8. A computing device for offering a carbon offset reward corresponding to a user, the computing device comprising:

one or more processors; and

a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:

detect one or more actions attributable to the user;

determine a plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user, the plurality of characteristics including a level of mindful driving of the user and one or more additional characteristics of the user;

process information associated with the level of mindful driving of the user and the one or more additional characteristics of the user;

determine a quality of the user as a perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user;

determine a level of carbon offset reward based at least in part upon the quality of the user as the perspective or existing customer; and

offer an amount of carbon offset reward to the user based at least in part upon the level of carbon offset reward;

wherein:

each of the one or more 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.

9. The computing device of claim 8, wherein:

the instructions that cause the one or more processors to determine the plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user further comprise instructions that cause the one or more processors to:

analyze driving data associated with the one or more vehicle trips; and

determine the level of mindful driving of the user based at least in part upon the driving data.

10. The computing device of claim 8, wherein:

analyze driving data associated with the one or more vehicle trips; and

determine the one or more additional characteristics of the user based at least in part upon the driving data, the one or more additional characteristics being the user reducing a number of the one or more vehicle trips.

11. The computing device of claim 8, wherein:

analyze trip data associated with the trip between the particular pair of origination and destination points; and

determine the one or more additional characteristics of the user based at least in part upon the trip data, the one or more additional characteristics being the user using an alternate form of transportation to make the trip between the particular pair of origination and destination points.

12. The computing device of claim 8, wherein:

analyze mobile device data associated with the user interacting with the mobile device; and

determine the one or more additional characteristics of the user based at least in part upon the mobile device data, the one or more additional characteristics being the user using an application installed on the mobile device for a certain number of times.

13. The computing device of claim 8, wherein:

determine the one or more additional characteristics of the user based at least in part upon the mobile device data, the one or more additional characteristics being the user referring a friend to use an application installed on the mobile device.

14. The computing device of claim 8, wherein the instructions that cause the one or more processors to determine the quality of the user as the perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user further comprise instructions that cause the one or more processors to:

determine a probability that the user will become a customer and an estimated revenue from the user if the user becomes the customer; and

determine an expected business value of the user based at least in part upon the probability that the user will become the customer and the estimated revenue from the user if the user becomes the customer, the expected business value of the user indicating the quality of the user.

15. A non-transitory computer-readable medium storing instructions for offering a carbon offset reward corresponding to a user, the instructions when executed by one or more processors of a computing device, cause the computing device to:

detect one or more actions attributable to the user;

wherein:

each of the one or more 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.

16. The non-transitory computer-readable medium of claim 15, wherein:

the instructions when executed by the one or more processors that cause the computing device to determine the plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user further cause the computing device to:

analyze driving data associated with the one or more vehicle trips; and

17. The non-transitory computer-readable medium of claim 15, wherein:

analyze driving data associated with the one or more vehicle trips; and

18. The non-transitory computer-readable medium of claim 15, wherein:

instructions when executed by the one or more processors that cause the computing device to determine the plurality of characteristics related to the user based at least in part upon the one or more actions attributable to the user further cause the computing device to:

19. The non-transitory computer-readable medium of claim 15, wherein:

20. The non-transitory computer-readable medium of claim 15, wherein the instructions when executed by the one or more processors that cause the computing device to determine the quality of the user as the perspective or existing customer based at least in part upon the level of mindful driving of the user and the one or more additional characteristics of the user further cause the computing device to: