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2017

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Using Imagery To Identify Abandoned Property In Public Spaces, Charles Armstrong Dec 2017

Using Imagery To Identify Abandoned Property In Public Spaces, Charles Armstrong

Defensive Publications Series

Generally, the present disclosure is directed to identifying private property that has been illegally dumped or stored for a prolonged period without use. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict whether an item has been abandoned in a public right-of-way based on imagery of the item and record of public right-of-way.

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System Volume Compensating For Environmental Noise, Nick Felker Dec 2017

System Volume Compensating For Environmental Noise, Nick Felker

Defensive Publications Series

Generally, the present disclosure is directed to an audio system for compensating for ambient environmental noise. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a comfortable volume level based on an intensity of ambient noise.

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Using Music To Affect Mood Based On Sentiment Analysis, Nick Felker Dec 2017

Using Music To Affect Mood Based On Sentiment Analysis, Nick Felker

Defensive Publications Series

Generally, the present disclosure is directed to influencing the mood of a person listening to music while operating a system or device. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to select music to play to influence a user’s mood based on user input into a system or device.

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Planning Group Meals Based On Preferences Of Attendees, Nick Felker Dec 2017

Planning Group Meals Based On Preferences Of Attendees, Nick Felker

Defensive Publications Series

Generally, the present disclosure is directed to determining an optimal place and time for a meeting based on the preferences of the people attending the meeting. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an optimal restaurant and time for a group meal based on personal preferences and/or time availability of members of the group.

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Font Optimization Based On User Reading Habits, Nick Felker Dec 2017

Font Optimization Based On User Reading Habits, Nick Felker

Defensive Publications Series

Generally, the present disclosure is directed to improving accessibility to electronic documents by adjusting font characteristics based on user behavior. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an optimal font to display an electronic document in based on user interaction with the electronic document.

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Detecting Interesting Events In A Home Security Camera System, N/A Dec 2017

Detecting Interesting Events In A Home Security Camera System, N/A

Defensive Publications Series

Generally, the present disclosure is directed to a system for predicting whether the subject of a camera needs to be recorded and/or transmitted. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict whether the view of the camera contains a noteworthy change in semantic meaning based on labels describing the semantic meaning of the view.

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Machine-Learning For Optimization Of Software Parameters, Etienne J. Membrives Dec 2017

Machine-Learning For Optimization Of Software Parameters, Etienne J. Membrives

Defensive Publications Series

Generally, the present disclosure is directed to optimizing tuning parameters in a computing system and/or software application using machine learning. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an optimal value for tuning parameters based on metrics provided by a developer. As examples, such metrics may be related to an amount of user engagement, latency associated with the application, or efficiency of executing the software application.

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Providing Relevant Advertisements Based On Item-Specific Purchase History, Nick Felker Dec 2017

Providing Relevant Advertisements Based On Item-Specific Purchase History, Nick Felker

Defensive Publications Series

Generally, the present disclosure is directed to providing relevant advertisements to a shopper based on purchase history. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict how receptive a shopper will be to advertisements for a similar item to an item recently purchased based on purchase history patterns for the item.

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Machine-Learned Caching Of Datasets, Etienne J. Membrives Dec 2017

Machine-Learned Caching Of Datasets, Etienne J. Membrives

Defensive Publications Series

Generally, the present disclosure is directed to creating and/or modifying a pre-cache for a client device connected to a remote server containing a dataset. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict the likelihood a particular piece of data will be used (e.g. opened, edited, saved, etc.) within a time frame based on information about the data, the user’s interaction with the data, and/or the user’s schedule.

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Identifying Listed Properties Based On Imagery, Charles Armstrong Dec 2017

Identifying Listed Properties Based On Imagery, Charles Armstrong

Defensive Publications Series

Generally, the present disclosure is directed to identifying properties that are listed for sale or similarly available based on imagery of the properties. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict that a property is listed for sale or similarly available and/or characteristics about the listing based on imagery of the property, including imagery containing listing signage.

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Machine-Learning Predicted User Interfaces, David Huffaker Dec 2017

Machine-Learning Predicted User Interfaces, David Huffaker

Defensive Publications Series

Generally, the present disclosure is directed to customizing user interfaces to a particular user. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict the likelihood a user will interact with a user interface element and/or the importance of the content of the user interface element based on user data such as, for example, user interaction with the user interface.

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Reinforcement Learning For Fuzzing Testing Techniques, Neil Dhillon, Tanmay Wadhwa Dec 2017

Reinforcement Learning For Fuzzing Testing Techniques, Neil Dhillon, Tanmay Wadhwa

Defensive Publications Series

Generally, the present disclosure is directed to using machine learning to manage a trade-off between exploration and exploitation in a fuzzer system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict inputs that will cause a system being tested to malfunction or crash based on input data provided to the system and output from the system.

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Improving Appeal Of An Advertisement Based On Linguistic Trends, Donny Greenberg Dec 2017

Improving Appeal Of An Advertisement Based On Linguistic Trends, Donny Greenberg

Defensive Publications Series

Generally, the present disclosure is directed to improving appeal of an advertisement based on linguistic trends. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to generate fashionable advertisements based on a candidate advertisement and linguistic trends determined from sample material from one or more advertising platforms.

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A Modeling System For Integral Simulation Of Propagation Of Ocean Surge And Wave And Their Impinging On Coastal Structure (Extended Abstract), Ke Qu, Hansong Tang, Anil Agrawal Dec 2017

A Modeling System For Integral Simulation Of Propagation Of Ocean Surge And Wave And Their Impinging On Coastal Structure (Extended Abstract), Ke Qu, Hansong Tang, Anil Agrawal

Ocean Waves Workshop

Now it has become necessary to develop our capability to directly simulate many emerging coastal ocean flow and wave problems. These flow problems present a common challenge to our modeling capability; they involve multiphysics phenomena spanning a vast range of spatial and temporal scales, however, so far essentially we have no methods and computer software to directly and integrally simulate these phenomena. Now it has become necessary for us to develop new capability to model them in efficient ways and high-fidelity. Towards this goal, we have developed a brand new, unprecedented modeling system that is able to directly evaluate many …

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Session 1 Paper - Contemporary Expeditionary Warfare For Scientists And Engineers, C. Reid Nichols Dec 2017

Session 1 Paper - Contemporary Expeditionary Warfare For Scientists And Engineers, C. Reid Nichols

Ocean Waves Workshop

Expeditionary forces from many countries have deployed to mitigate the effects of natural disasters and human conflicts. Approaches are usually linked to the nation’s maritime strategy. Expeditionary forces provide rapid response to man-made disruptions such as mass casualties by terrorists, vessel hijackings by pirates, murders by drug cartels, and nuclear accidents. Recent examples include Humanitarian Assistance/Disaster Response (HA/DR) and Non Combatant Evacuation Operations (NEO). HA/DR type missions have been planned and executed to mitigate the effects of catastrophic tropical cyclones, flooding, wild fires, and tsunami. NEO missions are focused on the evacuation of a nation’s citizens and other foreigners from …

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Configuring Alarm System Based On Time To Arrive At Appointment, Nick Felker Dec 2017

Configuring Alarm System Based On Time To Arrive At Appointment, Nick Felker

Defensive Publications Series

Generally, the present disclosure is directed to setting an alarm to alert a user based on an appointment. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a time to alert a user based on a user’s schedule and location.

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Predictive Cryptocurrency Mining And Staking, Thomas Price Dec 2017

Predictive Cryptocurrency Mining And Staking, Thomas Price

Defensive Publications Series

Generally, the present disclosure is directed to determining the validity of a chain within a blockchain system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict the likelihood that a block within a blockchain system will be verified based on characteristics of the block.

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Predicting Computing Prices Dynamically Using Machine Learning, Thomas Price Dec 2017

Predicting Computing Prices Dynamically Using Machine Learning, Thomas Price

Defensive Publications Series

Generally, the present disclosure is directed to determining the cheapest datacenter for a computing task to be computed in. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict the cost of computing at a datacenter over a time interval based on time data, weather data, and/or grid statistics.

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Predicting Delivery Time Of Components In A Supply Chain, Venki Raman, Sunil Reddy Dec 2017

Predicting Delivery Time Of Components In A Supply Chain, Venki Raman, Sunil Reddy

Defensive Publications Series

Generally, the present disclosure is directed to predicting a delivery time of components in a supply chain. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a delivery time of a component based on enterprise data relating to the component.

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Generating Travel Itineraries Based On User Interests, David Parkinson Dec 2017

Generating Travel Itineraries Based On User Interests, David Parkinson

Defensive Publications Series

Generally, the present disclosure is directed to generating a travel itinerary for a user based on the user’s interests. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict interest of a user and generate a travel itinerary based on user preferences and interests.

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Identifying Hold State In An Automated Calling System, Thomas Price Dec 2017

Identifying Hold State In An Automated Calling System, Thomas Price

Defensive Publications Series

Generally, the present disclosure is directed to identifying that a caller is on hold or has been removed from hold when calling a calling system such as, for example, an automated calling system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict that a hold state has changed (e.g. a caller has been placed on hold or has been taken off hold) based on call data from a call.

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Session 1 Presentation - Use Of Offshore Energy Facilities As Deepwater Ocean Observing Platforms, Jeffrey Morin, Pak Leung, Ruth Mullins Perry, Eoin Howlett, Armen-Sjur Minassian, Rudy Poulos Dec 2017

Session 1 Presentation - Use Of Offshore Energy Facilities As Deepwater Ocean Observing Platforms, Jeffrey Morin, Pak Leung, Ruth Mullins Perry, Eoin Howlett, Armen-Sjur Minassian, Rudy Poulos

Ocean Waves Workshop

Wave measurement is one of the major components for any ocean observation program, however, it is also the most challenging one due to its complexity. For Oil & Gas community, wave data is often used for platform stability calculation, operational decision making, structure fatigue analysis, and engineering design criteria.

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Wavemaker Improvements At The University Of New Orleans Towing Tank, Ryan D. Thiel Dec 2017

Wavemaker Improvements At The University Of New Orleans Towing Tank, Ryan D. Thiel

Ocean Waves Workshop

No abstract provided.

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Machine Learning To Determine Type Of Vehicle, Thomas Price Dec 2017

Machine Learning To Determine Type Of Vehicle, Thomas Price

Defensive Publications Series

Generally, the present disclosure is directed to determining the vehicle type of a vehicle (e.g. for use in vehicle navigation). In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a vehicle type of a vehicle based on vehicle data relating to the operation and/or capacity of a vehicle.

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Machine Learning To Identify Vehicle Maintenance Needs, Thomas Price Dec 2017

Machine Learning To Identify Vehicle Maintenance Needs, Thomas Price

Defensive Publications Series

Generally, the present disclosure is directed to identifying maintenance needs for a vehicle. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict vehicle maintenance needs for a vehicle based on vehicle data from the vehicle.

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Identifying Energy Overconsumption Based On Ambient Noise Audio Signatures, Thomas Price Dec 2017

Identifying Energy Overconsumption Based On Ambient Noise Audio Signatures, Thomas Price

Defensive Publications Series

Generally, the present disclosure is directed to identifying energy overconsumption in a device. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a device is in a state of energy overconsumption based on ambient noise data and electricity throughput.

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Image Moderation Using Machine Learning, Dave Feltenberger, Rob Neuhaus Dec 2017

Image Moderation Using Machine Learning, Dave Feltenberger, Rob Neuhaus

Defensive Publications Series

Generally, the present disclosure is directed to moderating images using machine learning. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a probability that an image will be accepted and/or rejected according to one or more rules of a submission service based on image data from the image.

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Machine Learning For Accurate Battery Run Time Prediction, Liang Jia Dec 2017

Machine Learning For Accurate Battery Run Time Prediction, Liang Jia

Defensive Publications Series

Generally, the present disclosure is directed to using machine learning to manage state of charge of a battery. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict future energy consumption and optimal charging rate of a battery based on battery characteristics and expected user routine.

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Removing Breathing Artifacts From Audio, N/A Dec 2017

Removing Breathing Artifacts From Audio, N/A

Defensive Publications Series

Generally, the present disclosure is directed to removing breathing artifacts from audio. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to generate a clean audio sample based on audio data from the audio sample.

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Configuring Music Variation In A Music Compilation, Thomas Price Dec 2017

Configuring Music Variation In A Music Compilation, Thomas Price

Defensive Publications Series

Generally, the present disclosure is directed to determining an optimal level of music variation in an assortment of music such as, for example, a music streaming service. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an optimal level of music variation based on historical level of music variation preferences and/or listening data.

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