Abstract: Systems and methods for deep technology innovation management by cross-pollinating innovations dataset are disclosed. A system extracts context-based keyword from an innovation dataset by transforming the innovation dataset to a vector. Further, the system searches semantically relevant keywords for the extracted context-based keyword, by extracting an entity and a key phrase from the extracted a context-based keyword. Furthermore, system clusters the vector, by identifying frequent keywords in the semantically relevant keywords to obtain cluster centroids of the frequent keywords. Thereafter, the system determines weighted keywords in each cluster using the obtained cluster centroids, and classifies the weighted keywords to identify emerging innovation trends relevant to the innovation in the innovation dataset. The system forms cohorts of innovators to explore the reuse of innovations, assets, code, and build focused monetization model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a win prediction for an opportunity. In some implementations, a server receives data representing an opportunity. The server obtains historical data from a data repository based on the received data representing the opportunity. The server generates standardized feature vectors based on non-standardized data, the non-standardized data comprising (i) the received data and (ii) the obtained historical data. The server filters the standardized feature vectors. The server generates a win percentage based on the filtered feature vectors using classifiers. In response to generating the win percentage for the opportunity, the server generates a plurality of data insights using the classifiers and the generated win percentage, wherein the plurality of data insights describe one or more data predictions for pursuing the opportunity.

Abstract: Systems and methods for profile service management are disclosed. A system generates user data corresponding to plurality of users in an electronic meeting (e-meeting), based on meta data received from one or more data sources. The generated user data is stored in data lake. The system retrieves, from data lake, the user data. Each of plurality of users has corresponding user profile. The system determines, from user data, factual user information for each user of plurality of users from corresponding user profile. Further, the system compares factual user information of plurality of users, to determine set of commonalities between plurality of users. Furthermore, system generates, for each user, integration data comprising factual user information and set of commonalities. Further, system displays, for each user, plurality of meeting objects in a meeting interface associated with the e-meeting, based on the integration data, to provide one or more profile services.

Abstract: In some implementations, a system may receive a submission associated with an innovation associated with an entity. The system may analyze, using a first machine learning model, the submission to identify a classification associated with the innovation. The system may analyze, using a second machine learning model in association with the classification, content of the submission to identify a characteristic of the innovation. The system may determine, using a third machine learning model and based on the characteristic, a ranking of the innovation relative to individual innovations in the subset of innovations. The system may determine, using a fourth machine learning model, an impact score associated with the innovation. The system may perform, based on the impact score satisfying a threshold, an action associated with a project involving the innovation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a win prediction for an opportunity. In some implementations, a server receives data representing an opportunity. The server obtains historical data from a data repository based on the received data representing the opportunity. The server generates standardized feature vectors based on non-standardized data, the non-standardized data comprising (i) the received data and (ii) the obtained historical data. The server filters the standardized feature vectors. The server generates a win percentage based on the filtered feature vectors using classifiers. In response to generating the win percentage for the opportunity, the server generates a plurality of data insights using the classifiers and the generated win percentage, wherein the plurality of data insights describe one or more data predictions for pursuing the opportunity.

Abstract: Systems, methods, and devices for presenting and managing interrelated sets of skills are provided. In some embodiments, a map interface is provided that presents the interrelated skills to a user, and provides the user an opportunity to strategize regarding how best the skills may be acquired to advance on a career path. In some embodiments, an administrative user may use the interface to view an aggregate skill set of employees in an organization.

Abstract: Systems, methods, and devices for presenting and managing interrelated sets of skills are provided. In some embodiments, a map interface is provided that presents the interrelated skills to a user, and provides the user an opportunity to strategize regarding how best the skills may be acquired to advance on a career path. In some embodiments, an administrative user may use the interface to view an aggregate skill set of employees in an organization.