A computer-aided design system enables physical articles to be customized via printing or embroidering and enables digital content to be customized and electronically shared. A user interface may be generated that includes an image of a model of an article of manufacture and user customizable design areas that are graphically indicated on the image corresponding to the model. A design area selection may be received. In response to an add design element instruction and design element specification, the specified design element is rendered in the selected design area on the model image. Customization permissions associated with the selected design area are accessed, and using the customization permissions, a first set of design element edit tools are selected and rendered. User edits to the design element may be received and rendered in real time. Manufacturing instructions may be transmitted to a printing system.

A computer-aided design system enables physical articles to be customized via printing or embroidering and enables digital content to be customized and electronically shared. A user interface may be generated that includes an image of a model of an article of manufacture and user customizable design areas that are graphically indicated on the image corresponding to the model. A design area selection may be received. In response to an add design element instruction and design element specification, the specified design element is rendered in the selected design area on the model image. Customization permissions associated with the selected design area are accessed, and using the customization permissions, a first set of design element edit tools are selected and rendered. User edits to the design element may be received and rendered in real time. Manufacturing instructions may be transmitted to a printing system.

A hybrid entity recognition system and accompanying method identify composite entities based on machine learning. An input sentence is received and is preprocessed to remove extraneous information, perform spelling correction, and perform grammar correction to generate a cleaned input sentence. A POS tagger tags parts of speech of the cleaned input sentence. A rules based entity recognizer module identifies first level entities in the cleaned input sentence. The cleaned input sentence is converted and translated into numeric vectors. Basic and composite entities are extracted from the cleaned input sentence using the numeric vectors.

A cleanup support system that supports a cleanup behavior includes: a first obtaining unit configured to obtain first information indicating a level of interest of a target person in cleanup; a second obtaining unit configured to obtain second information indicating a level of achievement of the cleanup performed by the target person; a determination unit configured to determine a content of control corresponding to the first information obtained and the second information obtained, with reference to a rule which associates the level of interest in the cleanup and the level of achievement of the cleanup with a content of control performed on a device; and a control unit configured to control the device according to the content of control determined.

Systems and methods for rules-based decisioning of events are disclosed. In one embodiment, a method may include: creating an in-memory cache by parsing stored checkpoints, signals, and rules definitions; receiving a checkpoint request; prioritizing the checkpoint request; preparing a basic context, comprising a limited set of objects, for the checkpoint request; using the in-memory cached definitions, generating at least one of a raw signal, an engineered signal, and a secondary signal for the checkpoint request based on the basic context; using the in-memory cached definitions, executing rules on at least one of the basic context, the raw signal, the engineered signal, and the secondary signal to generate a list of potential decisions; reducing the list of potential decisions to a list of final decisions; publishing the final decisions and supporting data rules and signals execution details; and executing the final decisions.

A hardware architecture for implementing a convolutional neural network.

A hardware architecture for implementing a convolutional neural network.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

A method for making dynamic risk predictions is provided. The method includes receiving a dataset with a first data field and a second data field. The first data field is populated with a measured value. The method also includes imputing a first predicted value to the second data field, generating a first risk score and a first set of associated metrics based on the measured value and the first predicted value, and imputing a second predicted value to the second data field. The method also includes calculating a statistically derived metric and determining whether the statistically derived metric exceeds a predetermined threshold, wherein a predetermined action is recommended if the statistically derived metric exceeds the predetermined threshold. A system and a non-transitory, computer readable medium storing instructions to cause the system to perform the above method are also provided.

A computer implemented method includes obtaining deep learning model embedding for each instance present in a dataset, the embedding incorporating a measure of concept similarity. An identifier of a first instance of the dataset is received. A similarity distance is determined based on the respective embeddings of the first instance and a second instance. Similarity distances between embeddings, represented as points, imply a graph, where each instance's embedding is connected by an edge to a set of similar instances' embeddings. Sequences of connected points, referred to as walks, provide valuable information about the dataset and the deep learning model.