Microsoft Technology Licensing, LLC

Parmjeet Singh

Lakshmi Narayana Mummidi

Sanjib Saha

Janet Lynn Schneider

Frank Gorgenyi

Fernando Gonzalez

Ashish Vijay Gadre

Mark A Inderhees

Stuart J Harper

Shaun C Hedrick

Sudharssun Subramanian

Kristofer Noel Iverson

Siddhartha Cingh Arora

Aman Arneja

Louis Tzu Leng Hong

Shahar Marom

James Michael Wierzba

A license for software distributed to multiple users can be validated using a peer-to-peer network, asymmetrical cryptography and an essentially hack-proof public data store of licensing information, avoiding the need for a licensing server or central coordination by servers or host computers. Instances of the licensed software are implemented on the nodes of the peer-to-peer network. A distributed, immutable data store where each segment of data of the series of segments in the data store include a hash of the previous segment. Licensing transactions can be recorded and validated using the distributed, immutable data store. Licensing transactions can include but are not limited to provisioning, de-provisioning, activation and deactivation transactions. The license processing logic can be integrated into the licensed application software, so that once the licensed software is implemented on a user's computer, license generation, license revocation, license validation and license invalidation proceed automatically without the need for any intervention.

Software license distribution and validation using a distributed immutable data store

The system includes a predictive model trained to estimate an amount of material to be used to fabricate three-dimensional objects. The system further includes an estimation component that receives information regarding the three-dimensional object. The estimation component, using the predictive model, estimates the amount of material to be used to fabricate the three-dimensional object based upon the information regarding the three-dimensional object. The estimation component compares the estimated amount of material with an available amount to determine whether the material available is less than the estimated amount of material to fabricate the three-dimensional object. When it is determined the material available is less than the estimated amount of material, the estimation component can perform an action such as preventing commencement of a fabrication process and/or providing information to a user. The predictive model can be adaptively updated based upon an actual amount of material used during the fabrication process.

Material estimate for fabrication of three-dimensional object

Intent-based reminders are provided. A user is enabled to initiate a reminder request based on an intent to enter or leave a given location. In a geofence training process, a plurality of geofences are created for plotting a path and subsequently tracking the user's traversal of the path for inferring the user's intent to depart or enter the location. A signal strength of a WLAN is recorded at each geofence. As the user traverses the path, a determination is made as to whether a predetermined percentage of the geofences is triggered in a sequential order by comparing the signal strength of the WLAN against the recorded WLAN signal strengths at the geofences. In some examples, signal strengths of neighboring WLANs are recorded and used to filter out false triggers. When a determination is made that the user's intent is to depart or enter the location, a reminder is provided.

Departure or entry intent-based reminders

Methods, systems, and devices are described herein for estimating a three dimensional (3D) print-time for generating a 3D object relative to one or more 3D model or printer parameters. In one aspect, at least one 3D model or printer parameter may be received for generating a 3D object by a 3D printer. Historical print-time data associated with at least one 3D model or printer parameter for generating other 3D objects may be obtained. Based on the received 3D model and printer parameters and the obtained historical print-time data, an estimated print-time for generating the 3D object by the 3D printer may be determined before slicing the 3D model by the 3D printer. In another aspect, a desired print-time for generating a 3D object may be received. One or more 3D model/print parameters may then be configured to achieve or exceed the desired print-time.

Determining print-time for generation of 3D objects based on 3D printing parameters

The methods described herein are configured to collect profile data on a device, scan for access points based on the profile data, and update a machine learning (ML) component based on feedback from the scan. Profile data is collected on a device as input to the ML component and a scan pattern is generated by the ML component based on the collected profile data, the scan pattern including a scan frequency, a scan iteration count, and a channel hint. A scan for access points is run in accordance with the generated scan pattern and the ML component receives feedback including a scanning result based on the scan for access points. ML component is then updated based on the scanning result, the scan pattern, and the profile data. Improving the ML component and thereby, the scanning efficiency of the device provides consistent network connection and improved battery performance.

Intelligent access point scanning based on a profile

A digital assistant supported across computing devices is configured to interact with an operating system (OS) upgrade system so that various user experiences, services, content, or features associated with support for peripheral devices during an OS upgrade of a computing device can be provided by the digital assistant and rendered as a native digital assistant user experience. The digital assistant is configured to surface a notification through a user interface (UI) when an OS upgrade is available for a user's computing device and recommended for installation. The OS upgrade system executes a confidence model in a machine learning system using real world crowd-sourced data to make predictions of successful post-upgrade operations of peripheral devices with an associated level of confidence. The digital assistant personalizes the OS upgrade notification to the user based on the configuration of computing and peripheral devices, applicable context, and the confidence level.

Peripheral device support with a digital assistant for operating system upgrades

A pedestrian alert system supported on a mobile device such as a smartphone, tablet computer, or a wearable computing device which is employed by a user to engage in various activities, is configured to provide visual, auditory, or haptic alerts when the system uses location sensing and map data to determine the user's proximity to a pedestrian street crossing. The alert attracts the user's attention by having the user perform an explicit action to dismiss the alert before being able to continue with the activities on the device. Operations are suspended for applications rendering content on the device display (e.g., a touch screen), user inputs are disabled, and the display may be blurred, dimmed, obscured, or otherwise deactivated. The pedestrian alert system can take a snapshot of application state prior to suspension so that the applications can gracefully resume operations without disruption once the alert is manually or automatically dismissed.

Pedestrian alerts for mobile devices

A computing device includes at least one processor and a memory, the memory storing computer-executable instructions for causing the device to be configured to load a subset of geo-fences from secondary storage to primary storage for tracking by the computing device, the subset of geo-fences selected from a set of available geo-fences stored in the secondary storage, based on a selection criteria. The computing device further creates a boundary geo-fence with a radius corresponding to a distance between a current location of the computing device and an edge of a geo-fence in the subset of geo-fences that is furthest from the current location of the computing device. Upon detecting a geo-fence event associated with a new location of the computing device in relation to the boundary geo-fence, a new subset of geo-fences is re-loaded, and a new boundary geo-fence is created based on the new location of the computing device.

Scalability and reliability of hardware geo-fencing with failover support

Systems and methods disclosed herein may include tracking one or more geo-fences using a GNSS hardware processor within a computing device. The tracking may use at least one GNSS signal. State changes of the one or more geo-fences during the tracking may be saved in a shared state database. The shared state database may be shared between the GNSS hardware processor and an application processor within the computing device. Upon detecting a deterioration of the at least one GNSS signal, tracking the one or more geo-fences may be switched from using the GNSS hardware processor to using the application processor. After the switching, an initial state of each of the one or more geo-fences may be set by using states currently stored in the shared state database prior to the switching.

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Bellevue, WA, United States of America

Parmjeet Singh