Abstract: Systems and methods are described for generating efficient iterative electronic recommendation structures. In various aspects, one or more processors aggregate a plurality of ratings vectors, where each ratings vector is associated with a vector type and contains one or more content ranking metrics associated with one or more users. The one or more processors generate similarity pairing values from the plurality of ratings vectors, where each similarity pairing value is based on a similarity mapping between a first ratings vector and a second ratings vector. The one or more processors further generate an electronic recommendation structure based on the similarity pairing values, where the electronic recommendation structure includes a bi-directional look-up interface that is configured to return a bi-directional recommendation value after receiving a lookup request for either the vector type of the first ratings vector or the vector type of the second ratings vector.

Abstract: Systems and methods are described for generating efficient iterative electronic recommendation structures. In various aspects, one or more processors aggregate a plurality of ratings vectors, where each ratings vector is associated with a vector type and contains one or more content ranking metrics associated with one or more users. The one or more processors generate similarity pairing values from the plurality of ratings vectors, where each similarity pairing value is based on a similarity mapping between a first ratings vector and a second ratings vector. The one or more processors further generate an electronic recommendation structure based on the similarity pairing values, where the electronic recommendation structure includes a bi-directional look-up interface that is configured to return a bi-directional recommendation value after receiving a lookup request for either the vector type of the first ratings vector or the vector type of the second ratings vector.

Abstract: A life plan management (LPM) computing device for connecting consumers with producers is provided. The LPM computing device may be in communication with a client computing device and may include a processor. The processor may be programmed to i) receive user input including at least one goal, ii) analyze the at least one goal to determine a plurality of categories, iii) store the plurality of categories, iv) analyze at least one of the plurality of categories to determine a plurality of producers, v) store the plurality of producers, vi) generate an interactive life map including the at least one goal, and enabling the consumer to access the plurality of producers by navigating the life map to select the at least one goal and the at least one category, vii) and cause the interactive life map to be displayed on the client computing device.

Abstract: Systems and methods are described for providing user offers based on efficient iterative recommendation structures. In various aspects, a server invokes a bi-directional look-up interface via a lookup request, where the bi-directional look-up interface is exposed via an electronic recommendation structure. The lookup request causes the bi-directional look-up interface to return a bi-directional recommendation value. The bi-directional recommendation value indicates a likelihood of a first user selecting a first offer or a second offer. The bi-directional recommendation value is transmitted via a computer network to a client device associated with the first user upon a determination that the likelihood meets or exceeds a recommendation threshold. The client device is operative to display at least one of the first offer or the second offer.

Abstract: Systems and methods are described for providing user offers based on efficient iterative recommendation structures. In various aspects, a server invokes a bi-directional look-up interface via a lookup request, where the bi-directional look-up interface is exposed via an electronic recommendation structure. The lookup request causes the bi-directional look-up interface to return a bi-directional recommendation value. The bi-directional recommendation value indicates a likelihood of a first user selecting a first offer or a second offer. The bi-directional recommendation value is transmitted via a computer network to a client device associated with the first user upon a determination that the likelihood meets or exceeds a recommendation threshold. The client device is operative to display at least one of the first offer or the second offer.

Abstract: Systems and methods for determining the effectiveness of vehicle safety features are provided. Vehicle build information (VBI) for vehicles manufactured by a plurality of OEMs may be obtained. The VBI may contain OEM-specific terminology for smart safety features associated with each vehicle. The obtained VBI may be analyzed to generate an ontology model mapping each feature to any OEM-specific terminology associated with the feature. The ontology model may be applied to the VBI to generate translated VBI for each vehicle, such that the OEM-specific terminology associated with each feature is replaced with OEM-agnostic terminology for the feature. Vehicle accident record information may be obtained for each vehicle, including, e.g., the number, frequency, severity, etc. of accidents associated with each vehicle.

Abstract: Systems and methods for determining the effectiveness of vehicle safety features are provided. Vehicle build information (VBI) for vehicles manufactured by a plurality of OEMs may be obtained. The VBI may contain OEM-specific terminology for smart safety features associated with each vehicle. The obtained VBI may be analyzed to generate an ontology model mapping each feature to any OEM-specific terminology associated with the feature. The ontology model may be applied to the VBI to generate translated VBI for each vehicle, such that the OEM-specific terminology associated with each feature is replaced with OEM-agnostic terminology for the feature. Vehicle accident record information may be obtained for each vehicle, including, e.g., the number, frequency, severity, etc. of accidents associated with each vehicle.

Abstract: Systems and methods are described for generating efficient iterative electronic recommendation structures. In various aspects, one or more processors aggregate a plurality of ratings vectors, where each ratings vector is associated with a vector type and contains one or more content ranking metrics associated with one or more users. The one or more processors generate similarity pairing values from the plurality of ratings vectors, where each similarity pairing value is based on a similarity mapping between a first ratings vector and a second ratings vector. The one or more processors further generate an electronic recommendation structure based on the similarity pairing values, where the electronic recommendation structure includes a bi-directional look-up interface that is configured to return a bi-directional recommendation value after receiving a lookup request for either the vector type of the first ratings vector or the vector type of the second ratings vector.

Abstract: A life plan management (LPM) computing device for connecting consumers with producers is provided. The LPM computing device may be in communication with a client computing device and may include a processor. The processor may be programmed to i) receive user input including at least one goal, ii) analyze the at least one goal to determine a plurality of categories, iii) store the plurality of categories, iv) analyze at least one of the plurality of categories to determine a plurality of producers, v) store the plurality of producers, vi) generate an interactive life map including the at least one goal, and enabling the consumer to access the plurality of producers by navigating the life map to select the at least one goal and the at least one category, vii) and cause the interactive life map to be displayed on the client computing device.

Abstract: A life plan management (LPM) computing device for connecting consumers with producers is provided. The LPM computing device may be in communication with a client computing device and may include a processor. The processor may be programmed to i) receive user input including at least one goal, ii) analyze the at least one goal to determine a plurality of categories, iii) store the plurality of categories, iv) analyze at least one of the plurality of categories to determine a plurality of producers, v) store the plurality of producers, vi) generate an interactive life map including the at least one goal, and enabling the consumer to access the plurality of producers by navigating the life map to select the at least one goal and the at least one category, vii) and cause the interactive life map to be displayed on the client computing device.

Abstract: A life plan management (LPM) computing device for connecting consumers with producers is provided. The LPM computing device may be in communication with a client computing device and may include a processor. The processor may be programmed to i) receive user input including at least one goal, ii) analyze the at least one goal to determine a plurality of categories, iii) store the plurality of categories, iv) analyze at least one of the plurality of categories to determine a plurality of producers, v) store the plurality of producers, vi) generate an interactive life map including the at least one goal, and enabling the consumer to access the plurality of producers by navigating the life map to select the at least one goal and the at least one category, vii) and cause the interactive life map to be displayed on the client computing device.

Abstract: Systems and methods are described for generating efficient iterative electronic recommendation structures. In various aspects, one or more processors aggregate a plurality of ratings vectors, where each ratings vector is associated with a vector type and contains one or more content ranking metrics associated with one or more users. The one or more processors generate similarity pairing values from the plurality of ratings vectors, where each similarity pairing value is based on a similarity mapping between a first ratings vector and a second ratings vector. The one or more processors further generate an electronic recommendation structure based on the similarity pairing values, where the electronic recommendation structure includes a bi-directional look-up interface that is configured to return a bi-directional recommendation value after receiving a lookup request for either the vector type of the first ratings vector or the vector type of the second ratings vector.

Abstract: Provided herein is an enhanced engagement and care support platform (“EECSP”) programmed to: (i) store user information for a user associated with the user computer device, wherein the user information also includes user payment account information; (ii) store caregiver information for a plurality of caregivers associated with the user, wherein the caregiver information also includes caregiver payment account information; (iii) determine purchase information for the user, wherein the purchase information includes a cost; (iv) transmit a request to pay a portion of the cost to each caregiver of the plurality of caregivers via the corresponding second computer device; (v) receive a response from one or more of the second computer devices, wherein the response indicates that the corresponding caregiver will pay a portion of the cost; and (vi) transmit the payment account information for each of the caregivers that responded and the cost to a payment processor.

Abstract: Systems and methods are described for providing user offers based on efficient iterative recommendation structures. In various aspects, a server invokes a bi-directional look-up interface via a lookup request, where the bi-directional look-up interface is exposed via an electronic recommendation structure. The lookup request causes the bi-directional look-up interface to return a bi-directional recommendation value. The bi-directional recommendation value indicates a likelihood of a first user selecting a first offer or a second offer. The bi-directional recommendation value is transmitted via a computer network to a client device associated with the first user upon a determination that the likelihood meets or exceeds a recommendation threshold. The client device is operative to display at least one of the first offer or the second offer.

Abstract: A life plan management (LPM) computing device for connecting consumers with producers is provided. The LPM computing device may be in communication with a client computing device and may include a processor. The processor may be programmed to i) receive user input including at least one goal, ii) analyze the at least one goal to determine a plurality of categories, iii) store the plurality of categories, iv) analyze at least one of the plurality of categories to determine a plurality of producers, v) store the plurality of producers, vi) generate an interactive life map including the at least one goal, and enabling the consumer to access the plurality of producers by navigating the life map to select the at least one goal and the at least one category, vii) and cause the interactive life map to be displayed on the client computing device.

Abstract: Systems and methods are described for providing user offers based on efficient iterative recommendation structures. In various aspects, a server invokes a bi-directional look-up interface via a lookup request, where the bi-directional look-up interface is exposed via an electronic recommendation structure. The lookup request causes the bi-directional look-up interface to return a bi-directional recommendation value. The bi-directional recommendation value indicates a likelihood of a first user selecting a first offer or a second offer. The bi-directional recommendation value is transmitted via a computer network to a client device associated with the first user upon a determination that the likelihood meets or exceeds a recommendation threshold. The client device is operative to display at least one of the first offer or the second offer.

Abstract: A life plan management (LPM) computing device for connecting consumers with producers is provided. The LPM computing device may be in communication with a client computing device and may include a processor. The processor may be programmed to i) receive user input including at least one goal, ii) analyze the at least one goal to determine a plurality of categories, iii) store the plurality of categories, iv) analyze at least one of the plurality of categories to determine a plurality of producers, v) store the plurality of producers, vi) generate an interactive life map including the at least one goal, and enabling the consumer to access the plurality of producers by navigating the life map to select the at least one goal and the at least one category, vii) and cause the interactive life map to be displayed on the client computing device.

Abstract: Methods and systems for processing machine accelerated and augmented customer data using a Web-Scale Data Fabric (WSDF). According to embodiments, the data may be received as data transfer objects from a set of business operations client applications. The data transfer objects may be analyzed using complex event processing (CEP) and, based on the analyzing, rules specific to the business operations client application may be applied. The methods and systems may semantically classify text specific to the business operations client application. A federated database (FD) may archive the receive data transfer objects as well as analysis data specific to the business operations client application.

Abstract: Methods and systems for processing data associated with potential damage to a portion of the property. One or more sensors may be disposed in proximity to the portion of the property, where the one or more sensors are configured to sense physical force applied to the portion. A controller module calculates mechanical force data based on the sensor data and estimates an amount of damage to the portion of the property based on the mechanical force data. In some aspects, an insurance provider can use the estimated amount of damage to perform insurance coverage functionalities, estimate damage to additional properties, maintain a damage history for the property, and notify customers of suggested improvements to the property.

Abstract: Methods and systems for displaying a user interface that warns a driver that a vehicle is located within or is near a geographical area (and/or intersection) associated with a higher than average risk of animal-vehicle and/or vehicle-vehicle collisions are provided. According to certain aspects, an electronic device may access a database that identifies a plurality of high risk areas, including areas associated with prior vehicle accidents. The electronic device may display a virtual road map, as well as an icon indicating the vehicle's current location and a plurality of visual indications of high-risk areas. When the electronic device detects that the current location of the vehicle is within and/or approaching a high-risk area, the electronic device may then warn the driver about the higher than average risk of experiencing a vehicle collision.