Abstract: One example method includes obtaining data from one or more databases of information about one or more technologies, selecting a set of features for extraction from the data, extracting the features from the data, and using a convolutional neural network to generate a forecast for the features, and the forecast is made with respect to a defined time period. The forecast may indicate the expected lifecycle changes of the features over the defined period of time.

Abstract: One example method includes identifying companies involved in development and/or use of a technology, and identifying the companies includes identifying datasets from which features are to be extracted, wherein each feature comprises an aspect of one of the companies that relates to the technology, and extracting the features from the datasets. Next, the method includes selecting a subset of the identified companies, for each feature assigned to a company in the subset, normalizing a value of the feature relative to respective values of all other features assigned to that company, assigning a weight to each of the features whose value has been normalized, calculating, for each company in the subset, an index value based on the features assigned to that company and the weights assigned to those features, and generating an index that ranks the companies in the subset by their respective index values.

Abstract: One example method includes identifying emerging hybrid technologies. Datasets are data mined to identify transactions that are related to technology. Associations are generated based on the transactions and the associations are processed to identify emerging hybrid technologies from the transactions.

Abstract: One example method includes identifying datasets known to contain data relating to different technologies, extracting the data from the data sources, performing preprocessing on the data, clustering the data after the data has been preprocessed, and the clustering comprises generating data clusters, and mapping each of the data clusters to a phase of a Wardley Map. The mapping may be used to make a prediction about which phase a particular technology would fall under in a future time period.

Abstract: One example method includes a pipeline for a distributed neural network. The pipeline includes a first phase that identifies intersecting identifiers across datasets of multiple clients in a privacy preserving manner. The second phase includes a distributed neural network that includes a data receiving portion at each of the clients and an orchestrator portion at an orchestrator. The data receiving portions and the orchestrator portions communicate forward and backward passes to perform training without revealing the raw training data.