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Using Imagery Of Property Improvements To Direct Marketing, Charles Armstrong
Using Imagery Of Property Improvements To Direct Marketing, Charles Armstrong
Defensive Publications Series
Generally, the present disclosure is directed to using imagery of properties containing property improvements to direct advertising for such improvements. 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 has a property improvement based on imagery of the property.
Generating Punctuation For Spoken Text, Sourish Chaudhuri, Andrew Gallagher
Generating Punctuation For Spoken Text, Sourish Chaudhuri, Andrew Gallagher
Defensive Publications Series
Generally, the present disclosure is directed to generating punctuation for spoken text from a speech-to-text 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 generate punctuation data for spoken text based on speech data for the spoken text
Anticipatory Product Development Using Design Suggestions, Nicole Gibson, Kevin D. Brune
Anticipatory Product Development Using Design Suggestions, Nicole Gibson, Kevin D. Brune
Defensive Publications Series
Generally, the present disclosure is directed to determining an optimal solution for building and/or designing a multi-dimensional product. 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 design solution for building and/or designing a product based on design characteristics and information relating to existing designs.
Compressed Sensing For Monitoring Of An Application, Roberto Lupi
Compressed Sensing For Monitoring Of An Application, Roberto Lupi
Defensive Publications Series
Generally, the present disclosure is directed to monitoring behavior of an application or other computing system or computing task. 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 behavior of an application or other computing system or computing task based on monitoring data.
Machine-Learned Wireless Propagation Model, Yi-Fan Chen, Georgios Roumpos, Boon-Lock Yeo, Peter Yung Sen Chen
Machine-Learned Wireless Propagation Model, Yi-Fan Chen, Georgios Roumpos, Boon-Lock Yeo, Peter Yung Sen Chen
Defensive Publications Series
Generally, the present disclosure is directed to predicting wireless propagation in an area and/or at a wavelength. 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 propagation data (e.g. radio signal attenuation and/or Bit Error Rate (BER)) based on connection data and terrain data.
Determining Optimal Wireless Propagation Model For A Region And Wavelength, Yi-Fan Chen, Georgios Roumpos, Boon-Lock Yeo, Peter Yung Sen Chen
Determining Optimal Wireless Propagation Model For A Region And Wavelength, Yi-Fan Chen, Georgios Roumpos, Boon-Lock Yeo, Peter Yung Sen Chen
Defensive Publications Series
Generally, the present disclosure is directed to determining an optimal wireless propagation model for a given region and wavelength. 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 wireless propagation model for a region based on terrain data for the region.
Intelligent Ordering Of Repeated Fields, Gang Wang, Haskell Garon
Intelligent Ordering Of Repeated Fields, Gang Wang, Haskell Garon
Defensive Publications Series
Generally, the present disclosure is directed to choosing an optimal order for options within a repeated field. 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 order for options within a repeated field based on interaction data with the repeated field.
Identifying Sources Of A Change In Metrics Of A Stack Of Servers, Kyle Loveless
Identifying Sources Of A Change In Metrics Of A Stack Of Servers, Kyle Loveless
Defensive Publications Series
Generally, the present disclosure is directed to identifying a source of a change in one or more metrics of one or more servers. 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 contribution from a component within a stack of one or more servers based on one or more states of the component.
Automatically Scaling Multi-Tenant Machine Learning, Steven J. Ross, Christopher Olston, Noah Fiedel
Automatically Scaling Multi-Tenant Machine Learning, Steven J. Ross, Christopher Olston, Noah Fiedel
Defensive Publications Series
Generally, the present disclosure is directed to optimizing use of computing resources in a 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 task allocation for a job serving a plurality of machine-learned models based on current system state and queries per second (QPS) data for the plurality of models. Alternatively, the tasks can be allocated according to one or more rules (e.g., a new task is allocated to a job until the compute usage for the job falls below a scaling threshold). …
Legal Precedent Mining With Machine Learning, Alexandros Salazar
Legal Precedent Mining With Machine Learning, Alexandros Salazar
Defensive Publications Series
Generally, the present disclosure is directed to gathering legal precedent from a corpus of law. 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 decision data from a court decision based on the court decision (e.g., a textual copy of the court decision).
Determining Validity Of A Point Of Interest Based On Existing Data, Melanie L. Donny-Clark, Jingyu Zhou, Shuang Qu
Determining Validity Of A Point Of Interest Based On Existing Data, Melanie L. Donny-Clark, Jingyu Zhou, Shuang Qu
Defensive Publications Series
Generally, the present disclosure is directed to determining whether or not a point of interest is valid based on existing data. 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 likelihood of a point of interest being valid based on ‘clues’ gathered from existing data.
Extracting Point Of Interest Information From Query Logs, Melanie L. Donny-Clark, Jing Dai
Extracting Point Of Interest Information From Query Logs, Melanie L. Donny-Clark, Jing Dai
Defensive Publications Series
Generally, the present disclosure is directed to extracting information about a point of interest from one or more query logs. 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 local intent for a query based on content of the query.
Correcting Image Anomalies Using Machine Learning, Nick Felker
Correcting Image Anomalies Using Machine Learning, Nick Felker
Defensive Publications Series
Generally, the present disclosure is directed to correcting anomalies in an image. 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 correct one or more anomalies in an image based on image data from an image.
Fitness Program For User-Defined Goals, Nick Felker
Fitness Program For User-Defined Goals, Nick Felker
Defensive Publications Series
Generally, the present disclosure is directed to a fitness program tailored to a 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 generate a goal-oriented fitness program to a user based on user physiological and or fitness data.
User Interface And Application Programming Interface For Explaining Personalized Machine-Learned Model Outputs, N/A
Defensive Publications Series
Generally, the present disclosure is directed to systems and methods that enable explanation of personalized machine-learned model outputs. For example, the systems and methods of the present disclosure can include or leverage a user interface and/or an application programming interface (API) to enable explanation of model outputs. According to one aspect, the present disclosure provides a principled way of discovering which actions or range of actions that the user took contributed to a particular output that the model predicted at later time. This allows, for example, an application that employs the personalized model to describe to the user which actions …
Providing Reader Assistance Based On Reader Interaction, Nick Felker
Providing Reader Assistance Based On Reader Interaction, Nick Felker
Defensive Publications Series
Generally, the present disclosure is directed to assisting users of an electronic document. 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 provide assistance for one or more content items in an electronic document to a user of the electronic document based on the user’s interaction with the electronic document.
Machine Learning To Predict Advertisement Targeting Solutions, Zachary Garrett
Machine Learning To Predict Advertisement Targeting Solutions, Zachary Garrett
Defensive Publications Series
Generally, the present disclosure is directed to using machine learning to predict advertisement targeting solutions. 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 optimal advertisement target solutions such as, for example, keyword word sets, negative word sets, location restrictions, bid adjustments, and/or schedules based on product data such as, for example, advertisement content (e.g., ad creatives text), seed keywords, images of the product, and/or advertiser metadata.
Using Image Recognition To Control Devices, Johan Ulrich Lewin Jessen, Morten Just Petersen
Using Image Recognition To Control Devices, Johan Ulrich Lewin Jessen, Morten Just Petersen
Defensive Publications Series
Generally, the present disclosure is directed to using image recognition to control devices. 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 appropriate user interface to control one or more remotely controllable appliances based on image data.
Generating Icons For Applications In An Applications Marketplace, Jun Hong
Generating Icons For Applications In An Applications Marketplace, Jun Hong
Defensive Publications Series
Generally, the present disclosure is directed to generating one or more icons for one or more applications in an applications marketplace. 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 an icon for an application based on one or more icons for existing applications.
Machine Learning To Disable Applications From Using Background Resources Except At Appropriate Times, Thomas Price, Eytan Lerba
Machine Learning To Disable Applications From Using Background Resources Except At Appropriate Times, Thomas Price, Eytan Lerba
Defensive Publications Series
Generally, the present disclosure is directed to disabling computing applications from using background resources (e.g., processing or data transmission) except at appropriate times (e.g., times when the user is likely to use the electronic 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 an appropriate time for an application to use resources based on device usage data.
Machine Learning To Select Best Network Access Point, Thomas Price, Eytan Lerba
Machine Learning To Select Best Network Access Point, Thomas Price, Eytan Lerba
Defensive Publications Series
Generally, the present disclosure is directed to selecting a best network access point when multiple are available (e.g., including cell towers, WiFi access points, etc.). 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 best network access point based on device usage data.
Configuring Alarm Setting Using Machine Learning, Thomas Price, Eytan Lerba
Configuring Alarm Setting Using Machine Learning, Thomas Price, Eytan Lerba
Defensive Publications Series
Generally, the present disclosure is directed to configuring the initial time set in an alarm 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 a time to set an alarm based on historical alarm records and/or other contextual data.
Machine Learning To Select Media Playback Volume, Thomas Price, Eytan Lerba
Machine Learning To Select Media Playback Volume, Thomas Price, Eytan Lerba
Defensive Publications Series
Generally, the present disclosure is directed to setting a media playback volume of an electronic 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 an appropriate media playback volume based on device usage data.
Machine Learning To Select Input Language On A Software Keyboard, Thomas Price, Eytan Lerba
Machine Learning To Select Input Language On A Software Keyboard, Thomas Price, Eytan Lerba
Defensive Publications Series
Generally, the present disclosure is directed to selecting an input language on a software keyboard. 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 input language for a keyboard based on device usage data.
Machine Learning To Select Screen Brightness Level, Thomas Price, Eytan Lerba
Machine Learning To Select Screen Brightness Level, Thomas Price, Eytan Lerba
Defensive Publications Series
Generally, the present disclosure is directed to setting a brightness of a screen of an electronic device (e.g., during media viewing or media playback). 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 appropriate screen brightness level based on device usage data.
Machine Learning To Automatically Lock Device Screen At Opportune Time, Thomas Price, Eytan Lerba
Machine Learning To Automatically Lock Device Screen At Opportune Time, Thomas Price, Eytan Lerba
Defensive Publications Series
Generally, the present disclosure is directed to determining when to lock a screen of an electronic device such as, for example, a smartphone. 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 appropriate lock screen behavior based on device usage data.
Generating Travel Itineraries Based On Travel History Of Similar Users, Alexandre Chinien
Generating Travel Itineraries Based On Travel History Of Similar Users, Alexandre Chinien
Defensive Publications Series
Generally, the present disclosure is directed to generating a travel itinerary for a user based on travel history of similar users. 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 travel itinerary for a user based on travel history data from one or more users.
User Assistant Device, Cristian Cibils Bernardes
User Assistant Device, Cristian Cibils Bernardes
Defensive Publications Series
The present disclosure provides a device that enables a user to automatically trigger an action to be performed by one or more computing devices with the push of a button. In particular, the device can be a wireless button coupled to a user assistant enabled computing device. Existing user assistant enabled computing devices can be configured to accept user text or voice commands or queries to perform certain actions. To repeat a common action the user must repeatedly enter the same text or voice commands or queries. Despite the frequency of a particular command or query, user assistant enabled devices …
Generation Of Meeting Transcript Using Speech Recognition, Eric Bellamy
Generation Of Meeting Transcript Using Speech Recognition, Eric Bellamy
Defensive Publications Series
Participants in meetings conducted via audio or videoconferencing often engage in notetaking. This can cause participants to be less engaged. This disclosure describes techniques to automatically generate a meeting transcript. At the beginning of a meeting, participant identities and voices are learned as participants introduce themselves. During the meeting, participants can enable transcript generation with voice commands. Speech-to-text techniques are used to generate a text transcript of the meeting that associates speech with corresponding participants.
Virtual Reality Re-Centering, Chris Mckenzie
Virtual Reality Re-Centering, Chris Mckenzie
Defensive Publications Series
A virtual reality player often views a virtual reality scene of a virtual world while the player is located in a physical world via a head-mounted display. If the physical world is a virtual reality room, the scene can be re-centered within a known region, where stationary objects in the physical world (furniture, walls, etc.) may be accounted for via data such as maps or dimensions of the virtual reality room. However, in instances where the physical world is not a virtual reality room, such as a random standing space or a couch of a player’s house, a virtual reality …