Abstract: In one aspect, an example methodology includes, by a computing device, receiving information about a product from another computing device and determining one or more relevant features from the information about the product, the one or more relevant features influencing prediction of a sequence number. The method also includes, by the computing device, generating, using a machine learning (ML) model, a prediction of a sequence number for the product based on the determined one or more relevant features, the sequence number being indicative of a sequence within a product category tree. The method may also include, by the computing device, sending the prediction to the another computing device.

Abstract: In one aspect, an example methodology includes, by a computing device, receiving information about a product from another computing device and determining one or more relevant features from the information about the product, the one or more relevant features influencing prediction of a sequence number. The method also includes, by the computing device, generating, using a machine learning (ML) model, a prediction of a sequence number for the product based on the determined one or more relevant features, the sequence number being indicative of a sequence within a product category tree. The method may also include, by the computing device, sending the prediction to the another computing device.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by an eco fees classification service, receiving information regarding a product to classify and generating a feature vector for the product, the feature vector representing a plurality of relevant features determined from the information regarding the product to classify. The method also includes, by the eco fee classification service, predicting, using an eco fees classification engine, a commodity classification for the product based on the feature vector, and recommending the commodity classification for the product for use in determining an eco fee to apply to a sale of the product. In some aspects, the method may also include computing the eco fee to apply to the sale of the product based on the recommended commodity classification.

Abstract: A method comprising: obtaining an initial system specification for a system; classifying the initial system specification by using a machine learning model that is configured to yield an estimated system throughput for the system; detecting whether the estimated system throughput is greater than or equal to a required system throughput; and when the estimated system throughput is greater than or equal to the required system throughput, outputting one or more recommended system specifications that are based on the initial system specification.

Abstract: A method, comprising: receiving a request to acquire a lock that is associated with a resource; transitioning the lock into an acquired state in response to the request; and releasing the lock.

Abstract: A method comprising: obtaining a first instruction for a first service, the first instruction being associated with a routing preference; generating a second instruction for the first service based on the first instruction; associating the second instruction with the routing preference; and providing the second instruction to the first service, wherein associating the second instruction with the routing preference includes at least one of inserting an indication of the routing preference in the second instruction.

Abstract: A system and method provide customized, logically-sequenced user experiences for configuring products having numbers of components and compatibility rules that have, until now, been too large for individually-customized configurators to be feasible. Embodiments analyze compatibility rules for all possible components to form a directed graph of which components affect the availability of other components in a product. This graph is then analyzed using a community detection algorithm, such as the Louvain algorithm, to identify clusters of closely-related components. Clusters as a whole are given directionality based on the directed graph of components. Then the clusters are sequenced using this directionality, so that customer configuration of components in later clusters may be affected by earlier choices, but not vice versa. Finally, the sequenced clusters are transformed into a product configuration workflow that provides a user experience similar to a custom configurator.

Abstract: A system for updating data content including one or more computer data processors programmed and configured to generate one or more instances of data programming objects, each instance of a data programming object representing a data record and properties of the data record, at least one property corresponding to a scheduled change in the data record. The system includes an object monitor to which one or more status updates of the one or more instances of data programming objects are transmitted to, the status updates including the property corresponding to the time of the scheduled change in the data record.