Patent application number | Description | Published |
20100306167 | ARTICLE AND METHOD FOR FINDING A COMPACT REPRESENTATION TO VISUALIZE COMPLEX DECISION TREES - The invention comprises an article and method for transforming a complex or large decision tree having multiple variables; multiple values for each variable; and, multiple outcomes for each combination of variables and their associated values, into a compact, efficient graphical representation to provided enhanced ease of use and interaction by a human user. More particularly, the invention comprises a computationally efficient method for transforming an input decision tree into an optimal compact representation by computing a particular ordering of variables in the decision tree that first leads to a Directed Acyclic Graph, or “DAG,” with a minimum number of nodes. The method then converts the DAG into an exception-based DAG, or “EDAG,” with exactly one exception, having an optimal, minimum number of nodes with increased comprehensibility for a user. | 12-02-2010 |
20100332514 | DECISION LOGIC COMPARISON AND REVIEW - Techniques are described for logically comparing strategies. In one aspect the strategies can be compared by receiving a request to compare a first strategy to a second strategy, the first strategy graphically represented by a first set of linked nodes, the second strategy graphically represented by a second set of linked nodes, each set of linked nodes linking a root node to at least one action node; identifying a subset of linked nodes from at least one of the first set of linked nodes and the second set of linked nodes based on an equivalence of a first subset of the first set of linked nodes to a second subset of the second set of linked nodes; and, providing a visual depiction of the identified subset of the linked nodes to a user, the visual depiction corresponding to the equivalence of the first subset to the second subset. | 12-30-2010 |
20150185042 | DYNAMIC COMPUTATION OF DISTANCE OF TRAVEL ON WEARABLE DEVICES - Techniques for dynamic computation of distance of travel on wearable devices are described. Disclosed are techniques for receiving motion data over context windows from one or more sensors coupled to a wearable device, determining a number of motion units of each context window, determining a motion unit length of each context window as a function of the number of motion units of each context window and a duration of each context window, determining a distance of travel of each context window, and determining a total distance of travel over all context windows. The motion unit length of each context window is variable from the motion unit length of another context window. In some embodiments, the total distance of travel is presented on an interface coupled to the wearable device. | 07-02-2015 |
20150185045 | DYNAMIC CALIBRATION OF RELATIONSHIPS OF MOTION UNITS - Techniques for dynamic computation of distance of travel on wearable devices are described. Disclosed are techniques for determining a number of motion units over a distance for one or more samples, determining a motion unit length of each sample, and creating a model determining a motion unit length as a function of a number of motion units and a duration of the motion units. Further disclosed are techniques for receiving motion data of a user from one or more sensors coupled to the wearable device, accessing the model to determine a motion unit length of the user, and determining a distance of travel of the user, and initiating execution of an operation of the wearable device based on the distance of travel of the user. In one embodiment, the model may be adjusted or calibrated. In another embodiment, the determination of the distance of travel may be verified or corrected. | 07-02-2015 |
20160066857 | Device-Based Activity Classification Using Predictive Feature Analysis - Device-based activity classification using predictive feature analysis is described, including receiving a signal from a sensor configured to measure a heart rate coupled to a device, the sensor being configured to sense the signal over a time period, evaluating the signal to generate data associated with the heart rate, the data being further evaluated to select a classifier, invoking the classifier, the classifier being configured to evaluate the data to identify a predictive feature, the predictive feature invoking an application configured to determine a state using a feature interpreter, the application also being configured to evaluate other data from another signal, the signal being configured to detect a respiration rate, and processing the data and the other data using the application and the feature interpreter to generate information associated with sleep, the information being configured to display on an interface associated with the device. | 03-10-2016 |
20160066858 | Device-based activity classification using predictive feature analysis - Device-based activity classification using predictive feature analysis is described, including receiving a signal from a sensor coupled to a device, the sensor being configured to sense the signal over a time period, evaluating the signal to generate data, the data being further evaluated to select a classifier, invoking the classifier, the classifier being configured to evaluate a predictive feature, the predictive feature invoking an application configured to determine a state using a feature interpreter, and processing the data using the application and the feature interpreter to generate information associated with a biological state, the information being configured to display on an interface associated with the device. | 03-10-2016 |
20160066859 | Device-based activity classification using predictive feature analysis - Device-based activity classification using predictive feature analysis is described, including receiving a signal from a sensor coupled to a device, the sensor being configured to detect the signal over a time period and to detect motion, evaluating the signal to generate data, the data being used to indicate motion, the data being further evaluated to select a classifier based on whether the motion is detected, activating another sensor coupled to the device, the another sensor being configured to detect another signal that is substantially different than the signal, the another signal being used to generate other data associated with whether the motion is detected, invoking the classifier, the classifier being configured to evaluate a predictive feature to identify a type associated with whether the motion is detected, the predictive feature invoking an application configured to determine the type and a state using a feature interpreter, and processing the data using the application and the feature interpreter to generate information associated with a biological state associated with whether the motion is detected, the information being configured to display on an interface associated with the device. | 03-10-2016 |
20160070339 | Divice-based activity classification using predictive feature analysis - Device-based activity classification using predictive feature analysis is described, including evaluating an indicator associated with a predictive feature, identifying an application, using the name, to be performed, and invoking the application, the application being configured to interpret the indicator to determine an operation to perform at one or more levels of a protocol stack using data generated from evaluating a signal detected by a sensor, the sensor being coupled to a wearable device, and the application being configured to perform the operation using other data generated from evaluating another signal detected by another sensor, the another sensor being substantially different than the sensor. | 03-10-2016 |