A system and method for gathering and analyzing the value chain relationships between legal entities, people, systems, and real and intangible assets using a temporospatial knowledge graph of the integrated value chain. The system provides the ability to layer private data from paid vendors with end-user owned and public records data to enable more comprehensive, contextualized and complete representations of the underlying value chain. Data analysis techniques, such as deep learning and machine learning, are performed on the knowledge graph and its underlying data set, in conjunction with simulation and modeling, to analyze the value chain, including generation of a risk profile for an entity's value chain and potential optimization options to remediate the identified risks.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bidding-based ridesharing are described. One exemplary method includes: obtaining a trip request for a rider, the trip request comprising a price; identifying a list of driver candidates to match with the trip request; determining a list of dispatch waiting times corresponding to the list of driver candidates; determining a list of acceptance probabilities corresponding to the list of driver candidates based on a machine-learning classifier; and determining an estimated waiting time or an estimated matching probability for the trip request based on the list of dispatch waiting times and the list of acceptance probabilities.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bidding-based ridesharing are described. One exemplary method includes: obtaining a price range of a trip request for a rider; determining a plurality of trip setting candidates based on the trip request, and a plurality of price candidates based on the price range; generating a plurality of bidding bundle option candidates based on a plurality of combinations of the plurality of trip setting candidates and the plurality of price candidates; determining, based on a trained machine-learning classifier, a selection probability for the rider to select each of the plurality of bidding bundle option candidates; and ranking the plurality of bidding bundle option candidates based on corresponding probabilities; and transmitting one or more of the plurality of bidding bundle option candidates with top selection probabilities to a terminal device associated with the rider.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bidding-based ridesharing are described. One exemplary method includes: obtaining, by a computing device of a ridesharing platform from a terminal device, a trip request for a rider, the trip request comprising an origin, a destination, and a price; identifying, by the computing device, a plurality of driver candidates to match with the trip request; determining, by the computing device, an acceptance probability for each of the plurality of driver candidates to accept the trip request; determining, by the computing device based on the plurality of determined acceptance probabilities, an estimated waiting time and an estimated matching probability for the trip request; and transmitting at least one of the estimated waiting time and the estimated matching probability to the terminal device.

A method is disclosed. A data set including: (a) identifiers of a set of incidents occurring within a defined geographic region to which at least one service provider responded during a first time period and (b) address data identifying a location within the geographic region of each said incident of the set is retrieved over a network. An instruction to generate a heat map of the incidents occurring within the geographic region during the first time period is received from a user via a user interface generated to a display device. In response to the instruction to generate the heat map, the address data is converted to GPS data. A heat map of an aerial view of the geographic region based on the GPS data is generated. The heat map is displayed to the display device in a user interface.

A virtual local presence display apparatus, system and method is disclosed. Included are an extraction engine capable of automatically disassociating the virtual local presence from its production background; a first data feed of an actual background in which the displaying device resides; a background data feed of a plurality of optional backgrounds including at least the production background of the virtual local presence; a stream subject data feed of an extracted one of the virtual local presence extracted from the production background; an integrated data feed in which the virtual local presence and a background are integrated; a first processing having a receiver capable of receiving the data feeds and assessing a plurality of focal lengths to focal planes for the virtual presence in the production background, and in a selected one of the other backgrounds; and a second processing for causing displaying, on the display device, the virtual presence at a suitable location with regard to the focal planes in a selected one of the other backgrounds.

Methods, systems, and computer-readable media for generating a virtual based on location. The method acquires a request for a service based on a type of service and is associated with a user, the user's location, and user preferences. The method then acquires a set of service providers based on the type of service and the user's location who are filtered from a larger set of service providers using user preferences. The method in the next step acquires a machine learning model that is based on stored information associated with other users travel patterns and with service providers providing the service and the geographical information associated with the user's location. The method executed the identified machine learning model to aggregate a subset of service providers based on output from the machine learning model. The machine learning model is inputted the set of service providers, the user's location, the user's preferences, and the geographical information.

Systems and methods enable the discovery of new relationships between diseases and genes by prioritizing the selection of gene targets for a disease using an embedding space generated from a knowledge graph by mapping datasets collected from various data sources using a graph schema, modeling disease and gene associations with link weightings, analyzing the data with several machine learning models, and scoring predictions.

One embodiment provides a method, including: receiving a clothing design, wherein the clothing design identifies attributes of an article of clothing; accessing a plurality of information sources having sentiments regarding attributes of pieces of clothing, wherein each of the plurality of information sources is assigned to one of a plurality of geographical regions; identifying location-dependent attributes of the pieces of clothing, wherein the location-dependent attributes comprise clothing attributes whose corresponding user sentiments vary across at least a subset of the geographical regions; and recommending, based upon the location-dependent attributes and a targeted geographical region in which the article of clothing is to be sold, parameter values for the attributes of the article of clothing, wherein the recommending comprises correlating (i) the targeted geographical region with (ii) location-dependent attributes having a positive sentiment within the targeted geographical region.

A computer-implemented method is disclosed. The method includes using reverse geo-coding to determine user transaction locations for a user, determining a number of user transactions for the user that correspond to each of a plurality of statistical area levels, determining a subdivision of each of the plurality of statistical area levels that has the highest number of domestic card present transactions for the user, identifying an effective area of influence (EAI) for the user, based on a determination of a statistical area level that has the highest number of domestic card present transactions for the user, and accessing geographically classified statistics from public data sources related to one or more of the plurality of the statistical area levels. A location based market recommendation is generated based on the geographically classified statistics and the effective area of influence.