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Recognizing Semantic Formatting Information In A Document, N/A
Recognizing Semantic Formatting Information In A Document, N/A
Defensive Publications Series
Generally, the present disclosure is directed to recognizing semantic intent from formatting information. 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 intent of an author for a section (e.g., a word, sentence, line, column and/or paragraph) of a document based on the formatting information of that section of the document.
Suggesting Deletion Of Blurry Photos, Charles Lai, Nicolas Miranda
Suggesting Deletion Of Blurry Photos, Charles Lai, Nicolas Miranda
Defensive Publications Series
Generally, the present disclosure is directed to identifying and suggesting deletion of blurry photos. 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 blurriness characteristic of an image based on image data. For example, the blurriness characteristic can describe a percentage of the image that is blurry.
Learning Better Font Slicing Strategies From Data, Quan Wang, Yiran Mao
Learning Better Font Slicing Strategies From Data, Quan Wang, Yiran Mao
Defensive Publications Series
Generally, the present disclosure is directed to serving font files in topical subsets. 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 topic labels of characters in a font based on a corpus of characters or glyphs.
Using Imagery Of Vegetation And Rooftops To Predict Solar Roof Candidacy, Charles Armstrong
Using Imagery Of Vegetation And Rooftops To Predict Solar Roof Candidacy, Charles Armstrong
Defensive Publications Series
Generally, the present disclosure is directed to predicting whether a property can be conducive to solar energy system installation with minimal adjustment to peripheral vegetation. 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 a property can be conducive to solar energy installation with minimal adjustment to peripheral vegetation based on imagery and/or publicly available or user-submitted information about the property or area surrounding the property.
Using Imagery To Identify Abandoned Property In Public Spaces, Charles Armstrong
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.
System Volume Compensating For Environmental Noise, Nick Felker
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.
Using Music To Affect Mood Based On Sentiment Analysis, Nick Felker
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.
Planning Group Meals Based On Preferences Of Attendees, Nick Felker
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.
Font Optimization Based On User Reading Habits, Nick Felker
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.
Detecting Interesting Events In A Home Security Camera System, N/A
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.
Machine-Learning For Optimization Of Software Parameters, Etienne J. Membrives
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.
Providing Relevant Advertisements Based On Item-Specific Purchase History, Nick Felker
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.
Machine-Learned Caching Of Datasets, Etienne J. Membrives
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.
Identifying Listed Properties Based On Imagery, Charles Armstrong
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.
Human Movement Detection In Wi-Fi Mesh Networks, Zhifeng Cai, Yu Wen
Human Movement Detection In Wi-Fi Mesh Networks, Zhifeng Cai, Yu Wen
Defensive Publications Series
Wireless networks, including mesh networks, can not only transmit information among various network devices but can also monitor changes or variations in the transmission signals used to communicate the information. Monitoring changes or variations in transmission signals can indicate presence, motion, lack of motion, or other characteristics about the physical space in which the wireless network operates without the need for additional equipment or dedicated devices.
Machine-Learning Predicted User Interfaces, David Huffaker
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.
Reinforcement Learning For Fuzzing Testing Techniques, Neil Dhillon, Tanmay Wadhwa
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.
Improving Appeal Of An Advertisement Based On Linguistic Trends, Donny Greenberg
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.
Configuring Alarm System Based On Time To Arrive At Appointment, Nick Felker
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.
Predictive Cryptocurrency Mining And Staking, Thomas Price
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.
Predicting Computing Prices Dynamically Using Machine Learning, Thomas Price
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.
Predicting Delivery Time Of Components In A Supply Chain, Venki Raman, Sunil Reddy
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.
Generating Travel Itineraries Based On User Interests, David Parkinson
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.
Identifying Hold State In An Automated Calling System, Thomas Price
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.
Machine Learning To Determine Type Of Vehicle, Thomas Price
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.
Machine Learning To Identify Vehicle Maintenance Needs, Thomas Price
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.
Input Combination To Activate Different Input Modes Utilizing A Presence-Sensitive Screen, Thomas Price, Justin Lewis
Input Combination To Activate Different Input Modes Utilizing A Presence-Sensitive Screen, Thomas Price, Justin Lewis
Defensive Publications Series
A computing device is described that changes input modes associated with a presence-sensitive screen, based on tactile user inputs detected by touch and/or pressure sensors that are distinct from the presence-sensitive screen. Input modes define the actions taken by a computing device in response to receiving indications of user input at the presence-sensitive screen, such as navigating through a user interface of the computing device, adjusting a setting of the computing device, or altering a state of the computing device. The computing device interprets inputs detected at the sensors to change which functions are performed when a user provides subsequent …
Identifying Energy Overconsumption Based On Ambient Noise Audio Signatures, Thomas Price
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.
Image Moderation Using Machine Learning, Dave Feltenberger, Rob Neuhaus
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.
Timeout Mechanism For Latency-Critical Systems, Shu-Yi Yu
Timeout Mechanism For Latency-Critical Systems, Shu-Yi Yu
Defensive Publications Series
A system and method are disclosed for managing timeout in latency-critical systems. The method uses a global running timer by which each transaction is given a deadline when it enters the queue, calculated as: deadline = current time + timeout threshold of its QoS (quality of service). The deadline is stored with the corresponding transaction. The total trackable time is divided into 4 quadrants by its 2 most significant bits (MSB). Time wrapping beyond the saturation point is tracked separately from total trackable time. For transactions with deadlines behind the current time but ahead of the saturation point, their deadlines …