公开(公告)号：US20240283854A1

公开(公告)日：2024-08-22

申请号：US18635660

申请日：2024-04-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Tusher CHAKRABORTY ,  Ranveer CHANDRA ,  Nissanka Arachchige Bodhi PRIYANTHA ,  Vaibhav SINGH

IPC: H04L69/22 ,  H04B7/08 ,  H04L69/00

CPC classification number: H04L69/22 ,  H04B7/0854 ,  H04L69/03

Abstract: A computing system including a processor configured to receive packet preamble binary data and packet header binary data associated with a satellite. The processor may generate a simulated signal that encodes the packet preamble binary data and the packet header binary data. The processor may receive a satellite downlink signal. Within each of a plurality of sample intervals of the satellite downlink signal, the processor may compute a respective correlation between the satellite downlink signal and at least a portion of the simulated signal. The processor may select an identified sample interval of the plurality of sample intervals based at least in part on the plurality of correlations. The processor may decode binary satellite signal data based at least in part on the identified sample of the satellite downlink signal. The processor may output the binary satellite signal data.

公开(公告)号：US20240223270A1

公开(公告)日：2024-07-04

申请号：US18090033

申请日：2022-12-28

Applicant: Microsoft Technology Licensing, LLC

Inventor： Zerina KAPETANOVIC ,  Nissanka B. PRIYANTHA ,  Ranveer CHANDRA

IPC: H04B7/22 ,  H04B1/00 ,  H04B7/06 ,  H04L27/02

CPC classification number: H04B7/22 ,  H04B1/0078 ,  H04B7/0602 ,  H04L27/02

Abstract: This document relates to communication by backscattering of satellite signals. One example includes a satellite backscatter transmitter having a first antenna configured to receive a radio frequency satellite signal, a modulator configured to modulate the radio frequency satellite signal to obtain a modulated radio frequency satellite signal, a digital logic circuit configured to selectively control the modulator to encode information according to a communication scheme, and a second antenna configured to passively retransmit the modulated radio frequency satellite signal to a receiver.

公开(公告)号：US20240055100A1

公开(公告)日：2024-02-15

申请号：US18146123

申请日：2022-12-23

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： Sara Malvar MAUA ,  Anvita Kriti Prakash BHAGAVATHULA ,  Ranveer CHANDRA ,  Maria Angels de LUIS BALAGUER ,  Anirudh BADAM ,  Roberto DE MOURA ESTEVÃO FILHO ,  Swati SHARMA

IPC: G16H20/60

CPC classification number: G16H20/60

Abstract: This disclosure provides a machine learning technique to predict a protein characteristic. A first training set is created that includes, for multiple proteins, a target feature, protein sequences, and other information about the proteins. A first machine learning model is trained and then used to identify which of the features are relevant as determined by feature importance or causal relationships to the target feature. A second training set is created with only the relevant features. Embeddings generated from the protein sequences are also added to the second training set. The second training set is used to train a second machine learning model. The first and second machine learning models may be any type of regressors. Once trained, the second machine learning model is used to predict a value for the target feature for an uncharacterized protein. The model of this disclosure provides 91% accuracy in predicting an ideal digestibility score.

公开(公告)号：US20240046073A1

公开(公告)日：2024-02-08

申请号：US18163156

申请日：2023-02-01

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： Swati SHARMA ,  Somya SHARMA ,  Emre Mehmet KICIMAN ,  Ranveer CHANDRA ,  Sara MALVAR ,  Eduardo Rocha RODRIGUES

IPC: G06N3/0455 ,  G06N7/01

CPC classification number: G06N3/0455 ,  G06N7/01

Abstract: This disclosure provides a data-driven and scalable method to discover cause-and-effect relationships in data from natural systems that include sparse data sets. This technique can learn a causal graph from heterogenous data sources by combining embeddings from real data and embeddings from simulated data generated by process-based models. The causal graph is used for what-if analysis in out-of-distribution settings. One application is understanding the factors that affect soil carbon. A causal model created by these techniques can be used to discover cause-and-effect relationships that affect soil carbon. This model has applications such as forecasting soil carbon for a future time point to help inform farm practices. Farm practices, like tilling, may be modified in response to predictions provided by the model.

公开(公告)号：US20230313276A1

公开(公告)日：2023-10-05

申请号：US17657120

申请日：2022-03-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yuan-Jyue CHEN ,  Karin STRAUSS ,  Ranveer CHANDRA

IPC: C12Q1/6818 ,  C12Q1/6806 ,  C12Q1/686

CPC classification number: C12Q1/6818 ,  C12Q1/686 ,  C12Q1/6806

Abstract: An authentication assay using embedded deoxyribonucleic acid (DNA) taggants includes a substrate and a sample of an authenticity label collected from a product. The substrate has a plurality of assay locations, each of which includes a reporter oligonucleotide bound to the substrate. The reporter oligonucleotide includes a first region with a single-stranded toehold sequence, a second region with a universal sequence, and a third region with a unique sequence, the second and third regions being prehybridized with a complementary strand. The sample includes at least one fluorophore-labeled DNA taggant complementary to the first and second regions of the reporter oligonucleotide. Incubation of the substrate with the sample results in a toehold-mediated DNA strand displacement reaction that exchanges the complementary strand for the fluorophore-labeled DNA taggant. Excitation of the fluorophore molecule attached to the DNA taggant produces a pattern of light emitted at one or more assay locations.

公开(公告)号：US20230222433A1

公开(公告)日：2023-07-13

申请号：US17647924

申请日：2022-01-13

Applicant: Microsoft Technology Licensing, LLC

Inventor： Vaishnavi NATTAR RANGANATHAN ,  Upinder KAUR ,  Peeyush KUMAR ,  Ranveer CHANDRA ,  Michael McNab BASSANI ,  Vishal JAIN

IPC: G06Q10/08 ,  G06Q30/00

CPC classification number: G06Q10/0833 ,  G06Q30/018

Abstract: A traceability system for a bulk commodity supply chain is provided. The system includes a tracking device, a location determination subsystem, and at least one computing device having at least one processor. The location determination subsystem is configured to determine positional information of the tracking device while placed in a bulk commodity traveling along the bulk commodity supply chain. The processor receives the positional information from the location subsystem, extracts positional values from the positional information, and processes the positional values to identify motion primitives. A modeling tool is applied to the identified motion primitives to produce a positional path of the tracking device, which is output, for example, via a user interface. The positional path represents travel of the bulk commodity along the supply chain.

公开(公告)号：US20210409110A1

公开(公告)日：2021-12-30

申请号：US17178201

申请日：2021-02-17

Applicant: Microsoft Technology Licensing, LLC

Inventor： Tusher CHAKRABORTY ,  Apurv MEHRA ,  Ranveer CHANDRA ,  Siddharth PRAKASH

IPC: H04B7/185 ,  H04L29/06

Abstract: Systems and methods for reliable content delivery from a satellite to sub-edge devices are described. Content is delivered to a plurality of edge devices. Missing portions of the content are identified. One or more of the missing portions are selected, and the selected portions are recovered via a satellite network or a non-satellite network. The recovery is coordinated by a central cloud device based on one or more recovery factors.

公开(公告)号：US20210360608A1

公开(公告)日：2021-11-18

申请号：US17037332

申请日：2020-09-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Tusher CHAKRABORTY ,  Deepak VASISHT ,  Ranveer CHANDRA ,  Zerina KAPETANOVIC ,  Heping SHI ,  Nissanka Arachchige Bodhi PRIYANTHA

IPC: H04W72/04 ,  H04W64/00

Abstract: The disclosure described herein configures a client device for communication using dynamic spectrum access within a frequency spectrum, such as television white space (TVWS), using a determined location of the client device based on location information, such as from a global positioning system. A dynamic spectrum access database of channels is accessed based on the location information. Available channels are determined for the client device from the channels based on the location information. A list of the available channels for use by the client device are transmitted to the client device, thereby allowing narrowband communication over the channels.

公开(公告)号：US20210326723A1

公开(公告)日：2021-10-21

申请号：US16917702

申请日：2020-06-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Peeyush KUMAR ,  Ranveer CHANDRA ,  Chetan BANSAL ,  Dang Khoa TRAN ,  Emmanuel AZUH MENSAH ,  Michael Raymond GRANT

IPC: G06N5/04 ,  G01W1/10 ,  G06N20/00

Abstract: A computing system configured to execute a predictive program is provided. The predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.

公开(公告)号：US20240354371A1

公开(公告)日：2024-10-24

申请号：US18760715

申请日：2024-07-01

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： Peder A. OLSEN ,  Ranveer CHANDRA ,  Olaoluwa ADIGUN

IPC: G06F18/214 ,  G06T3/4053 ,  G06T3/4076

CPC classification number: G06F18/214 ,  G06T3/4053 ,  G06T3/4076 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for generating predicted high-resolution images from low-resolution images. To generate the predicted high-resolution images, the present technology may utilize machine learning models and super resolution models in a series of processes. For instance, the low-resolution images may undergo a sensor transformation based on processing by a machine learning model. The low-resolution images may also be combined with land structure features and/or prior high-resolution images to form an augmented input that is processed by a super resolution model to generate an initial predicted high-resolution image. The predicted initial high-resolution image may be combined or stacked with other predicted high-resolution images to form a stacked image. That stacked image may then be processed by another super resolution model to generate a final predicted high-resolution image.