Systems and methods are described for performing contactless gesture recognition for a computing device, such as a mobile computing device. An example technique for managing a gesture-based input mechanism for a computing device described herein includes identifying parameters of the computing device relating to accuracy of gesture classification performed by the gesture-based input mechanism and managing a power consumption level of at least an infrared (IR) light emitting diode (LED) or an IR proximity sensor of the gesture-based input mechanism based on the parameters of the computing device.

Methods and apparatus for contactless gesture recognition

Competitors are classified in terms of products the competitors offer. A product set is generated from product information received from a user. Also, a competitor set is generated, where the competitor set comprises at least one competitor determined to be relevant to one or more products in the product set. A target price rule is generated that is operative to change a price offered by the user for the at least one product. A competitors relevancy can be determined by considering factors such as: (1) unique visitors to the competitor's website, (2) reviews on the competitor's website (3), ratings on the competitor's website, (4) absolute number of products common to the user's website and the competitor's website, (5) percentage number of products common to the user's website and the competitor's website, and (6) number of products offered by the competitor that comprise the product set.

System and method for classifying relevant competitors

Disclosed are a system for, and method of, determining whether records correspond to the same individual. The system and method provide such a determination with a known minimum level of confidence. That is, the system and method provide an indication that records correspond to the same individual along with an associated confidence level. The system and method may be used to link records in a database that correspond to the same individuals, creating entity representations in the database.

Database systems and methods for linking records and entity representations with sufficiently high confidence

A method to support efficient, interactive, and fuzzy search on text data includes an interactive, fuzzy search on structured data used in applications such as query relaxation, autocomplete, and spell checking, where inconsistencies and errors exist in user queries as well as data. It utilizes techniques to efficiently and interactively answer fuzzy queries on structured data to allow users to efficiently search for information interactively, and they can find records and documents even if these records and documents are slightly different from the user keywords.

Method for efficiently supporting interactive, fuzzy search on structured data

A method for clustering data elements stored in a data storage system includes reading data elements from the data storage system. Clusters of data elements are formed with each data element being a member of at least one cluster. At least one data element is associated with two or more clusters. Membership of the data element belonging to respective ones of the two or more clusters is represented by a measure of ambiguity. Information is stored in the data storage system to represent the formed clusters.

Fuzzy data operations

An interoperable platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have few well known plug types. The building blocks can be composed to build complex compositions. Interoperability between data formats, metadata schema and interfaces to machine learning (ML) functions and trained machine learning models can be provided with no loss of information. A cloud runtime environment can be provided in which the composed workflows can be hosted as REST API to run in production.

Interoperable machine learning platform

A computer-implemented technique for fuzzy matching. This works quickly yet accurately to determine if a given computer-readable record is represented, by exact match or pretty close match, in a large collection of computer-readable records. Further tools may be provided to assess the character of the match.

Fast accurate fuzzy matching

A machine learning model deployment tool can receive a trained machine learning model and driven by a series of user interfaces and by received user input from the user interfaces, can automatically generate machine learning model software and deploy it to a hosting environment. The deployment of a machine learning model can be automated so that custom code does not have to be written by a human. Deployment can be to a single computing device, to a small scale service, to a small scale web service or to “the cloud”, e.g., as a high-scale, fault-tolerant web service utilizing hundreds of computers. Deployment can be guided by a series of user interfaces.

Easy deployment of machine learning models

Recent advances in high throughput data collection and storage technologies have led to a dramatic increase in the availability of high-resolution time series data sets in various domains. These time series reflect the dynamics of the underlying physical processes in these domains. Detecting changes in a time series over time or changes in the relationships among the time series in a data set containing multiple contemporaneous time series can be useful to detect changes in these physical processes. Contextual events detection algorithms detect changes in the relationships between multiple related time series. In this work, we introduce a new type of contextual events, called group level contextual change events. In contrast to individual contextual change events that reflect the change in behavior of one target time series against a context, group level events reflect the change in behavior of a target group of time series relative to a context group of time series. We propose an online framework to detect two types of group level contextual change events: (i) group formation (i.e., detecting when a set of multiple unrelated timeseries or groups of time series with little prior relationship in their behavior forms a new group of related time series) and (ii) group disbanding (i.e., detecting when one stable set of related time series disbands into two or more subgroups with little relationship in their behavior). We demonstrate this framework using 2 real world datasets and show that the framework detects group level contextual change events that can be explained by plausible causes.

Online Discovery of Group Level Events in Time Series

A computer-implemented method for matching a display advertisement to a user within a large-scale, non-destructive user modeling and experimentation environment using real-time traffic. The method commences by populating a user profile object (containing demographics, history, and behaviors of the user) for use during concurrent operation of a production platform and an experimentation platform. To implement non-destructive testing, the method continues by cloning a portion of the real-time traffic for use by the experimentation platform while concurrently delivering the real-time traffic to the production platform. The production platform and the experimentation platform operate concurrently, scoring matches between the user profile objects and a plurality of display advertisements for selecting among the best-scored advertisements. At the conclusion of the experiment, a new user profile object is constructed by selecting a first portion of the experimentation user profile object for use during continued operation of the production platform. Any undesired data is discarded.

Vijay Narayanan

Papers

Interoperable machine learning platform

Fast accurate fuzzy matching

Easy deployment of machine learning models

Online Discovery of Group Level Events in Time Series

Large-Scale User Modeling Experiments Using Real-Time Traffic