Salesforce Com, Inc.

Fair Isaac Corporation

Open Invention Network LLC

Jari Koister

Andrew Flint

Arash Nourian

Longfei Fan

Feier Lian

Kevin Griest

Nan Gong

Chong Huang

Brian William Hayes

Charles Boyle

Mudita Jain

Richard Spjut

Mike Micucci

Parama Dutta

Computer-implemented machines, systems and methods for managing missing values in a dataset for a machine learning model. The method may comprise importing a dataset with missing values; computing data statistics and identifying the missing values; verifying the missing values; updating the missing values; imputing missing values; encoding reasons for why values are missing; combining imputed missing values and the encoded reasons; and recommending models and hyperparameters to handle special or missing values.

Managing missing values in datasets for machine learning models

Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. Analyzing one or more features of the machine learning model, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. Displaying one or more visual indicators based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy.

Meaningfully explaining black-box machine learning models

Providing insights about a dynamic machine learning model

An aspect of the present invention includes a protocol for conveying data during an e-commerce session with a polymorphic response, comprising initiating a session with a message from a buyer application to a broker application and a session identifier assigned by the broker application; conducting the session between the buyer application and a supplier application; and concluding the session with a additional message which includes a schema identifier for the additional message, resolvable in a context of a system identifier; and a polymorphic response comprising a type and a version, wherein the polymorphic response includes additional data elements corresponding to values assigned to the type and version.

Method and device utilizing polymorphic data in E-commerce

Embodiments are described for a method for processing graph data by executing a Markov Clustering algorithm (MCL) to find clusters of vertices of the graph data, organizing the graph data by column by calculating a probability percentage for each column of a similarity matrix of the graph data to produce column data, generating a probability matrix of states of the column data, performing an expansion of the probability matrix by computing a power of the matrix using a Map-Reduce model executed in a processor-based computing device; and organizing the probability matrix into a set of sub-matrices to find the least amount of data needed for the Map-Reduce model given that two lines of data in the matrix are required to compute a single value for the power of the matrix. One of at least two strategies may be used to computing the power of the matrix (matrix square, M) based on simplicity of execution or improved memory usage.

Methods and systems for using map-reduce for large-scale analysis of graph-based data

Methods and systems for automatically determining, from a body of emails, blogs, and other documents, authors of the documents who are authorities on certain subjects, and what those subjects are. An intersection of the semantic footprints of documents by an author are deemed to be the derived skills footprint of the author. The derived skills footprints of many authors are compared with a user's query to determine who is the best person that could respond to the user.

Method and system for performing an authority analysis

A compositional recommender framework using modular recommendation functions is described. Each modular recommendation function can use a discrete technology, such as using clustering, a database lookup, or other means. A first recommendation function can recommend to a user items, such as books to check out, automobiles to purchase, people to date, etc. Another modular recommendation function can be daisy chained with the first to recommend items that are similar or related to the first recommended items, such as users who have also checked out the same recommended book, trailers that can be towed by the recommended automobiles, or vacations booked by people that were recommended as people to date. The modular recommendation functions can be used to build customized recommendation engines for different industries.

Methods and systems for implementing a compositional recommender framework

Embodiments are directed to a density-based clustering algorithm that decomposes and reformulates the DBSCAN algorithm to facilitate its performance on the Map-Reduce model. The DBSCAN algorithm is reformulated into connectivity problem using a density filter method and a partial connectivity detector. The density-based clustering algorithm uses message passing and edge adding to increase the speed of result merging, it also uses message mining techniques to further decrease the number of iterations to process the input graph. The algorithm is scalable, and can be accelerated by using more machines in a distributed computer network implementing the Map-Reduce program.

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Menlo Park, CA, United States of America

Jari Koister