Abstract: Systems and methods are provided herein for temporary digital content sharing. The systems and methods may involve receiving, from a first device, a selection of a first element associated with a first URL. The systems and methods may also involve determining first content associated with the first URL. The systems and methods may also involve receiving, from the first device, a selection of a second device at which to display the first content associated with the first URL. The systems and methods may also involve providing the first URL to the second device, wherein the URL allows access to the content by the second device.

Abstract: Systems and methods are provided herein for temporary digital content sharing. The systems and methods may involve receiving, from a first device, a selection of a first element associated with a first URL. The systems and methods may also involve determining first content associated with the first URL. The systems and methods may also involve receiving, from the first device, a selection of a second device at which to display the first content associated with the first URL. The systems and methods may also involve providing the first URL to the second device, wherein the URL allows access to the content by the second device.

Abstract: This disclosure describes systems, methods, and devices related to predictive modeling for evaluating vehicles. A device may receive a customer identifier (e.g., a user identification number, a social security number, driver license number, etc.). The device may retrieve credit data associated with the user identifier and vehicle data associated with a vehicle. The device may determine a first weight for the credit data and a second weight for the vehicle data. The device may determine, based on the first weight and the second weight, a value. The device may determine whether or not the value exceeds a profitability threshold. The device may determine a loan information associated with the customer identifier. The device may send a first indication of the product or service to a user device for presentation. The device may send a second indication of the loan information to the second device for presentation.

Abstract: The present disclosure relates to systems and methods for determining vehicle battery health. In some embodiments, an example method may include receiving, from a first vehicle, a first value for a first data type and a second value for a second data type, the first data type and the second data type relating to a battery of the first vehicle. The example method may also include determining, for the first data type and the second data type, a minimum possible value, a maximum possible value, and an offset indicator, wherein the offset indicator provides an indication of a significance of the first value and the second values relative to the minimum possible value and maximum possible value for the first data type and the second data type respectively. The example method may also include determining a weight for the first data type and a weight for the second data type.

Abstract: This disclosure describes systems and methods for improved transaction platforms. An example method may include receiving, by a processor and from a user, a selection of a first vehicle from a user interface. The example method may also include determining, by the processor and based on the first vehicle, a second vehicle. The example method may also include sending, by the processor, data associated with the first vehicle and data associated with the second vehicle to one or more entities. The example method may also include receiving, by the processor, first payment information for the first vehicle and second payment information for the second vehicle. The example method may also include determining, by the processor and based on the first payment information and the second payment information, third payment information for a third vehicle.

Abstract: The present disclosure relates to systems and methods for determining vehicle battery health. In some embodiments, an example method may include receiving, from a first vehicle, a first value for a first data type and a second value for a second data type, the first data type and the second data type relating to a battery of the first vehicle. The example method may also include determining, for the first data type and the second data type, a minimum possible value, a maximum possible value, and an offset indicator, wherein the offset indicator provides an indication of a significance of the first value and the second values relative to the minimum possible value and maximum possible value for the first data type and the second data type respectively. The example method may also include determining a weight for the first data type and a weight for the second data type.

Abstract: This disclosure describes systems, methods, and devices related to predictive modeling for evaluating vehicles. A device may receive a customer identifier. The system may determine financial data associated with the customer identifier and vehicle data associated with a vehicle. The system may determine loan information associated with the customer identifier and the vehicle. The system may determine a product or service associated with the vehicle. The system may determine a first value indicative of a probability that the customer will purchase the vehicle and the product or service based on the loan information. The system may determine a second value indicative of a profitability of a purchase of the vehicle and the product or service based on the loan information. The system may determine respective indications of the vehicle, the product or service, and the loan information to a user device for presentation.

Abstract: The disclosure is directed to, among other things, systems and methods for associating website visitors with a sticky dynamic internet protocol (IP) address. An example method may include identifying one or more first web-based cookies associated with a first IP address or one or more first user agents associated with the first IP address as being received by a web server associated with a website. The example method may also include determining a density measurement for the first IP address, the density measurement being a maximum number of the one or more first web-based cookies or the one or more first user agents that are identified as being received at the web server at a single time. The example method may also include determining that the density measurement is less than or equal to a threshold value. The example method may also include determining, based on the density measurement being less than or equal to the threshold value, that the first IP address is a first sticky dynamic IP address.

Abstract: Systems, devices, methods, and computer-readable media are disclosed for utilizing group theoretic techniques to enable data exchange between a supervisory central processing unit (CPU) and a group of graphical processing units (GPUs). The CPU may be configured to utilize a tabu search metaheuristic to explore a solution space to determine an optimal solution to an optimization problem. More specifically, the CPU may determine a fragmentation of a solution space that yields multiple partitions of the solution space and may assign each partition to a respective GPU configured to calculate a computational result. The CPU may then determine a new fragmentation of the solution space based on the computational results received from the GPUs that yields new partitions of the solution space and may assign each new partition to a respective GPU configured to again generate a computational result based on its assigned new partition.

Abstract: This disclosure describes systems, methods, and devices related to linear regression models and machine learning models for generating vehicle prices. A device may receive information associated with a vehicle, information associated with a dealer selling the vehicle, and market information indicative of previous sales of similar vehicles. The device may generate probabilities of sale of the vehicle at respective price positions based on the information. The device may generate a price elasticity curve for the vehicle based on the probabilities of sale. The device may generate a vehicle score represented by a supply line that is associated with the vehicle based on the information. The device may identify a first intersection of the supply line and the price elasticity curve, generate a market rank based on the intersection, and generate a recommended price for the vehicle based on the market rank as an input to a machine learning model.

Abstract: The present disclosure relates to systems and method for improved vehicle transaction platforms. An example method may include receiving, by a vehicle transaction platform comprising an inventory of vehicles for purchase and from a device associated with a first user accessing the vehicle transaction platform, digital identity information associated with the first user, wherein the digital identity information includes identifying information associated with the device. The method may also include determining the digital identity information matches a record of financial data associated with the first user. The method may also include determining, based on the record of financial data, transaction pricing data for a first vehicle, the transaction pricing data including a total purchase price and a periodic payment amount. The method may also include sending, to the device, a user interface configured to present the vehicle and transaction pricing.

Abstract: The present disclosure relates to systems and methods for improved vehicle transaction platforms. An example method may include receiving, by a vehicle transaction platform, a search request for vehicles from a user device associated with a first user account. The example method may also include receiving identifying information associated with the user account, wherein at least a portion of the identifying information is received from a third party. The example method may also include identifying, based on the identifying information, a predetermined search pattern, wherein the predetermined search pattern was generated based on a prior search request associated with a second user account and identifying information associated with the second user account.

Abstract: This disclosure describes systems, methods, and devices related to predictive modeling for evaluating vehicles. A device may receive a customer identifier (e.g., a user identification number, a social security number, driver license number, etc.). The device may retrieve credit data associated with the user identifier and vehicle data associated with a vehicle. The device may determine a first weight for the credit data and a second weight for the vehicle data. The device may determine, based on the first weight and the second weight, a value. The device may determine whether or not the value exceeds a profitability threshold. The device may determine a loan information associated with the customer identifier. The device may send a first indication of the product or service to a user device for presentation. The device may send a second indication of the loan information to the second device for presentation.

Abstract: This disclosure describes systems, methods, and devices related to customizing the presentation of data indicative of disruptive events relative to workplaces. A device may determine a geographical area comprising one or more geographical sub-areas, and a disruptive event is taking place at the geographical area. The device may determine one or more metrics associated with the disruptive event. The device may determine, based on the one or more metrics, a first disruption score indicative of a future severity condition caused by the disruptive event that will take place at a first geographical sub-area of the one or more geographical sub-areas. The device may generate a first user interface to present a geographical map associated with the geographical area, and the first geographical area is marked by the first disruption score.

Abstract: The disclosure is directed to, among other things, systems and methods for associating website visitors with a sticky dynamic internet protocol (IP) address. An example method may include identifying one or more first web-based cookies associated with a first IP address or one or more first user agents associated with the first IP address as being received by a web server associated with a website. The example method may also include determining a density measurement for the first IP address, the density measurement being a maximum number of the one or more first web-based cookies or the one or more first user agents that are identified as being received at the web server at a single time. The example method may also include determining that the density measurement is less than or equal to a threshold value. The example method may also include determining, based on the density measurement being less than or equal to the threshold value, that the first IP address is a first sticky dynamic IP address.

Abstract: This disclosure describes systems, methods, and devices related to predictive modeling for evaluating vehicles. A device may receive a customer identifier (e.g., a user identification number, a social security number, driver license number, etc.). The device may retrieve credit data associated with the user identifier and vehicle data associated with a vehicle. The device may determine a first weight for the credit data and a second weight for the vehicle data. The device may determine, based on the first weight and the second weight, a value. The device may determine whether or not the value exceeds a profitability threshold. The device may determine a loan information associated with the customer identifier. The device may send a first indication of the product or service to a user device for presentation. The device may send a second indication of the loan information to the second device for presentation.

Abstract: This disclosure describes systems, methods, and devices related to predictive modeling for evaluating vehicles. A device may receive a customer identifier (e.g., a user identification number, a social security number, driver license number, etc.). The device may retrieve credit data associated with the user identifier and vehicle data associated with a vehicle. The device may determine a first weight for the credit data and a second weight for the vehicle data. The device may determine, based on the first weight and the second weight, the value. The device may determine whether or not the value and a profitability threshold. The device may determine a loan information associated with the customer identifier. The device may send a first indication of the product or service to a user device for presentation. The device may send a second indication of the loan information to the second device for presentation.

Abstract: Systems and methods are provided herein for temporary digital content sharing. The systems and methods may involve receiving, from a first device, a selection of a first element associated with a first URL. The systems and methods may also involve determining first content associated with the first URL. The systems and methods may also involve receiving, from the first device, a selection of a second device at which to display the first content associated with the first URL. The systems and methods may also involve providing the first URL to the second device, wherein the URL allows access to the content by the second device.

Abstract: The present disclosure relates to systems and methods for determining vehicle battery health. In some embodiments, an example method may include receiving, from a first vehicle, a first value for a first data type and a second value for a second data type, the first data type and the second data type relating to a battery of the first vehicle. The example method may also include determining, for the first data type and the second data type, a minimum possible value, a maximum possible value, and an offset indicator, wherein the offset indicator provides an indication of a significance of the first value and the second values relative to the minimum possible value and maximum possible value for the first data type and the second data type respectively. The example method may also include determining a weight for the first data type and a weight for the second data type.

Abstract: Example embodiments of automatic vehicle imaging systems may include cameras configured to capture images and/or video. Cameras may be coupled to booms and/or unmanned vehicles, such as unmanned aerial vehicles and unmanned ground vehicles. Example methods may include determining a camera path for a vehicle, wherein the camera path is a path to be followed by a camera while the camera captures images or video of the vehicle, positioning the camera at the starting position, and capturing a first image or video segment of the vehicle using the camera. The example method may include associating the first image or video segment with a first description, moving the camera to a second position along the camera path, capturing a second image or video segment of the vehicle, and associating the second image or video segment with a second description.