Predicting infection risk by generating a first temporal graph of a first set of disease progression data, generating a second temporal graph of a second set of disease progression data, combining a first temporal graph node embedding and a second temporal graph node embedding, and generating a predicted infection risk according to the first temporal graph node embedding and the second temporal graph node embedding.

Techniques for generating an item transmission portal are described herein. First information for a plurality of carriers may be maintained which includes a capacity for each carrier. Historical capacity requirements for geographic locations may be maintained. For each geographic location a forecast of future capacity requirements, for each week of a plurality of weeks, may be generated based on the historical capacity requirements. A plurality of documents may be generated for each geographic location based on the forecast and the first information. A value may be determined for each document based on a time and capacity commitment for each document, the historical capacity requirements, and the forecast. A user interface may be generated that includes the plurality of documents and corresponding determined values. The user interface may be updated to reflect acceptance of a specific document by a carrier in response to input received via the user interface.

An apparatus that receives service provider location data of one or more service provider mobile devices comprising first global positioning system (GPS) data from the one or more service provider mobile devices, and retrieves, from a wireless carrier system, consumer location data based at least in part on second GPS data associated with a plurality of consumer mobile devices, wherein the one or more geographic regions are associated with the plurality of consumer mobile devices that are located at a same given location or within a threshold distance of the same given location. The apparatus further transmits push notifications alerting at least a subset of the plurality of consumer mobile devices based on at least in part on the determination that the service provider location data is within the one or more geographic regions.

Computationally efficient management of location-dependent values, such as rate cards, is provided, for example in the context of transport and market systems. A ball tree is traversed. The ball tree comprises a plurality of nodes, each node of the ball tree comprising a pivot geographic location and a radius, each node corresponding to at least one local value having a location within the radius of the pivot. Traversing the ball tree comprises: computing a bound on the location-dependent value for at least one node of the ball tree based on its corresponding at least one local value, its pivot geographic location, and a first geographic location, selectively traversing at least one child of the at least one node according to the bound, computing the location-dependent value for the at least one child based on its corresponding at least one local value, its pivot geographic location, and the first geographic location, and inserting the location dependent value of the at least one child to a sorted collection having a predetermined size.

An information processing method which is performed by an information processing apparatus for forecasting demand for a service provided by a mobile vehicle, comprises a classification step of classifying a plurality of unit regions included in a first area into a plurality of categories, respectively, by using a first classification model; a first building step of building a first demand forecasting model by determining a first set of parameters to be applied to each of the plurality of categories based on the classified categories and track record data in the first area; a second building step of building a second classification model by using classification criteria possessed by the first classification model, and building a second demand forecasting model by using the first set of parameters; and a forecasting step of performing demand forecasting in a second area by using the second classification model and the second demand forecasting model.

A method of selecting and presenting content based on learned user preferences is provided. The method includes providing a content system including a set of content items organized by genre characterizing the content items, and wherein the set of content items contains microgenre metadata further characterizing the content items. The method also includes receiving search input from the user for identifying desired content items and, in response, presenting a subset of content items to the user. The method further includes receiving content item selection actions from the user and analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user. The method includes, in response to receiving subsequent user search input, selecting and presenting content items in an order that portrays as relatively more relevant those content items containing microgenre metadata that more closely match the learned microgenre preferences of the user.

Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, generating predictions based at least in part on recognizing significant words in unstructured text. In an embodiment, a method is provided. The method comprises: generating a plurality of word-level tokens for an input unstructured textual data object; and for each word-level token: determining a significance type and a significance subtype for the word-level token by using a significance recognition machine learning model, and assigning a significance token label or an insignificance token label to the word-level token. The method further comprises: generating a label-based feature data object based at least in part on a subset of word-level tokens associated with the significance token label; generating a prediction data object for the input unstructured textual data object by providing the label-based feature data object to a first prediction machine learning model; and performing one or more automated prediction-based actions.

Techniques are described for automatic creation of optimal auditable entities (AEs) using a machine learning (ML)-based clustering model. The clustering model, when executed on one or more computing devices within an audit system of a company, is configured to automatically cluster the company's business processes into AEs based on similarity analyses of business process attributes. More specifically, in some examples, the clustering model ingests business processes and their corresponding attributes from a database, automatically clusters together business processes to achieve maximum intra-cluster similarity scores, and outputs the final clusters as model AEs. The resulting model AEs may be used as functional units for internal audits of the company's business processes. The resulting model AEs may improve audit efficiency due to the model AEs including only highly similar business processes. In addition, the resulting model AEs may enable more accurate assignment of audits based upon auditor experience and technical skills.

A method and apparatus for performing economic nexus determination by a transaction processing system. The method may include accessing a plurality of transactions associated with a plurality of merchant systems processed by the transaction processing system over a period of time. The method may also include inferring a geographic location, from among a plurality of geographic locations, for each of the plurality of transactions based on one or more transaction parameters. Then, the method may include aggregating a total revenue per geographic location per merchant system based on the inference of geographic location determined from the one or more transaction parameters. For a merchant and for a geographic location for which said merchant has location based revenue within the geographic location, the method may include applying an economic nexus rule corresponding to the geographic location, and determining when the economic nexus rule corresponding to the geographic location is satisfied.

An automated vending system includes a physical item storage unit, and a plurality of land-based transportation mechanisms, or vehicles, arranged to store a predicted number and assortment of items to vend. A processing resource can communicate with the plurality of transportation vehicles, and is arranged to support a machine learning module. A plurality of data sources is arranged to provide data to the machine learning module to determine a plurality of respective actions for the plurality of transportation vehicles. The processing resource can communicate respective control instructions to the plurality of transportation vehicles. A selected transportation mechanism which receives the control instruction can operate in response to the control instruction in order to convey the item to a determined vending location.