MICROSOFT TECHNOLOGY LICENSING, LLC patent applications on December 12th, 2024
Patent Applications by MICROSOFT TECHNOLOGY LICENSING, LLC on December 12th, 2024
MICROSOFT TECHNOLOGY LICENSING, LLC: 56 patent applications
MICROSOFT TECHNOLOGY LICENSING, LLC has applied for patents in the areas of H04L9/40 (4), G06F40/40 (3), G06F9/451 (3), G06F9/455 (3), G06F3/14 (3) G06F9/45558 (2), B01J19/0046 (1), H04L47/125 (1), G06N20/00 (1), G06Q30/016 (1)
With keywords such as: data, user, network, device, based, computing, information, include, systems, and methods in patent application abstracts.
Patent Applications by MICROSOFT TECHNOLOGY LICENSING, LLC
Inventor(s): Bichlien Hoang NGUYEN of Seattle WA (US) for microsoft technology licensing, llc, Karin STRAUSS of Seattle WA (US) for microsoft technology licensing, llc, Hsing-Yeh PARKER of Woodinville WA (US) for microsoft technology licensing, llc
IPC Code(s): B01J19/00, C07K1/04, C25D5/16, C25D5/48, C25D17/10
CPC Code(s): B01J19/0046
Abstract: high surface area coatings are applied to solid substrates to increase the surface area available for solid-phase synthesis of polymers. the high surface area coatings use three-dimensional space to provide more area for functional groups to bind polymers than an untreated solid substrate. the polymers may be oligonucleotides, polypeptides, or another type of polymer. the solid substrate is a rigid supportive layer made from a material such as glass, a silicon material, a metal material, and plastic. the coating may be thin films, hydrogels, microparticles. the coating may be made from a metal oxide, a high-� dielectric, a low-� dielectric, an etched metal, a carbon material, or an organic polymer. the functional groups may be hydroxyl groups, amine groups, thiolate groups, alkenes, n-alkenes, alkalines, n-hydroxysuccinimide (nhs)-activated esters, polyaniline, aminosilane groups, silanized oxides, oligothiophenes, and diazonium compounds. techniques for applying coatings to solid substrates and attaching functional groups are also disclosed.
Inventor(s): Chiqun ZHANG of Sunnyvale CA (US) for microsoft technology licensing, llc, Dragomir Dimitrov YANKOV of Palo Alto CA (US) for microsoft technology licensing, llc, Michael Robert EVANS of Seattle WA (US) for microsoft technology licensing, llc, Antonios KARATZOGLOU of San Francisco CA (US) for microsoft technology licensing, llc, Florin SABAU of Bellevue WA (US) for microsoft technology licensing, llc
IPC Code(s): G01C21/34, G06N3/0499
CPC Code(s): G01C21/3453
Abstract: a technique generates estimated time-of-arrival (eta) information to assist in navigating from one physical location to another. the technique uses a computer-implemented route-finding engine to identify a route between a specified starting location and an ending location of a trip. the route includes a sequence of segments. the technique then uses a machine-trained model to map information regarding the segments to eta information. the eta information provides an estimate of a time-of-arrival for the trip as a whole. the eta information also provides an estimate of parameters that describe the level of confidence of the time-of-arrival estimate for the trip. a training system produces the machine-trained model using a loss function, part of which models the time-of-arrival for the trip as a mixture of distributions.
Inventor(s): Eric Alexander PAPAMARCOS of Redmond WA (US) for microsoft technology licensing, llc, Anna Marion PFOERTSCH of Seattle WA (US) for microsoft technology licensing, llc, Robert Joseph DISANO of Seattle WA (US) for microsoft technology licensing, llc, Bret Paul ANDERSON of Seattle WA (US) for microsoft technology licensing, llc, Alex SNITKOVSKIY of Kirkland WA (US) for microsoft technology licensing, llc, Yash MISRA of Kirkland WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F3/0481, G06F3/0482, G06F3/04842, G06F3/14, G06F9/451, G06F11/34, G09G5/14
CPC Code(s): G06F3/0481
Abstract: a system for generating windows arrangements for a display is disclosed, which includes a processor and a computer-readable medium storing instructions for controlling the system to perform receiving a first user input requesting to generate windows arrangements for a display; in response to receiving the first user input, identifying, based on contextual information with respect to applications, a group of the applications to be included in the windows arrangements for the display; generating, based on display information of the display and the contextual information with respect to the applications, the windows arrangements, each windows arrangement providing a different on-screen arrangement of a plurality of windows associated with two or more applications of the group and defining a size and position of each of the windows on the display; and displaying a selectable list of the windows arrangements for the display.
Inventor(s): Xin LIU of Seattle WA (US) for microsoft technology licensing, llc, Chaochao HUANG of Suzhou (CN) for microsoft technology licensing, llc
IPC Code(s): G06F3/0482, G06F3/0485, G06T13/80
CPC Code(s): G06F3/0482
Abstract: systems and methods are provided for implementing a customizable and animatable popup card system. the popup card system includes animatable popup cards and popup card user interface (“ui”) elements, with location awareness functionality and scrolling awareness functionality. a user experience (“ux”) that is generated and displayed by the popup card system is adaptive to a multitude of scenarios and app (e.g., window) surfaces and is responsive to a variety of user inputs (including via mouse, keyboard, pen/stylus, touch, and voice). transitioning between popup card states and/or between corresponding popup cards is smooth and seamless, and is anchored within the ui or app window, without need for users to navigate to another page or app to complete an action triggered by user input.
Inventor(s): Taylor Alan HOPE of Redmond WA (US) for microsoft technology licensing, llc, Vinod R. SHANKAR of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F3/06
CPC Code(s): G06F3/0631
Abstract: a computer system configured to thinly provision a plurality of logical volumes over a plurality of types of storage. the plurality of logical volumes includes (1) a first logical volume that is thinly provisioned over a first type of storage, and (2) a second logical volume that is thinly provisioned over a second type of storage. the status of the plurality of logical volumes is monitored to detect one or more events associated with at least one of the plurality of logical volumes. in response to detecting a particular event among the one or more events, a set of data stored in a portion of the first logical volume is selected. the selected set of data is copied to the second logical volume, and the portion of the first logical volume that stores the set of data is deallocated.
Inventor(s): Kapil KUMAR of Bangalore (IN) for microsoft technology licensing, llc, Robert I. BUTTERWORTH of Burnaby (CA) for microsoft technology licensing, llc
IPC Code(s): G06F3/14, G06F1/16, G06F3/0484, G06F3/04883
CPC Code(s): G06F3/1423
Abstract: a dual-screen computing device includes two separate displays that are coupled to an interconnecting hinge. a hinge detector detects movement or position of the hinge, and the positions of the displays may be determined based on the hinge movement or position. the positions of the displays relative to each other may then be used to determine which mode of operation the dual-screen computing device is operating (e.g., tent mode, open, closed, etc.). additionally, the dual-screen computing device may include various sensors that detect different environmental, orientation, location, and device-specific information. applications are configured to operate differently based on the mode of operation and, optionally, the sensor data detected by the sensors.
Inventor(s): Peter GROENEWEGEN of Sammamish WA (US) for microsoft technology licensing, llc, Rohan Jagdish MALPANI of Everett WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F8/33, G06F8/73
CPC Code(s): G06F8/33
Abstract: a computer system provides a context for a source code document to an artificial intelligence (ai) model and obtains a first suggested edit at a first source code location. at a code editor user interface (ui), the computer system presents a first chat indicator at the first source code location. based on user interaction with the first chat indicator, the computer system presents the first suggested edit and receives a user response. the computer system provides an updated context to the ai model. the updated context comprises the context and the user response. the computer system obtains a second suggested edit at a second source code location. at the code editor ui, the computer system presents a second chat indicator at the second source code location. based on user interaction with the second chat indicator, the computer system presents the second suggested edit.
Inventor(s): Haishan ZHU of Bellevue WA (US) for microsoft technology licensing, llc, Ian BEARMAN of Seattle WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F8/41
CPC Code(s): G06F8/456
Abstract: a tool analyzes source code of a program that targets a parallel processing system, and searches for parallelism unstructured behavior values that potentially inhibit parallelism efficiency. example parallelism unstructured behavior values include particular memory addresses, memory masks, control divergences, and instruction predicates, which are identified according to their context and use in the program. the tool also locates program operations that contribute to these values, and determines a source of parallelism structure information in the program. in some scenarios, the tool populates a pattern data structure which is suitable to help guide code generation. patterns detected include addressing patterns, mask patterns, and thread control patterns. programs analyzed include single instruction multiple data programs and single instruction multiple thread programs. code generation guided by the pattern data structure to optimize parallelism efficiency produces smaller and faster program code which consumes less on-chip memory.
Inventor(s): Yao CHEN of San Jose CA (US) for microsoft technology licensing, llc, Lingjie Weng of Sunnyvale CA (US) for microsoft technology licensing, llc, Arvind Murali Mohan of Sunnyvale CA (US) for microsoft technology licensing, llc, Hongbo Zhao of San Jose CA (US) for microsoft technology licensing, llc, Lu Chen of Sunnyvale CA (US) for microsoft technology licensing, llc, Dipen Thakkar of San Jose CA (US) for microsoft technology licensing, llc, Xiaoxi Zhao of Milpitas CA (US) for microsoft technology licensing, llc, Shifu Wang of San Jose CA (US) for microsoft technology licensing, llc, Jim Chang of Cupertino CA (US) for microsoft technology licensing, llc, Daniel D. Thorndyke of San Jose CA (US) for microsoft technology licensing, llc, Smriti R. Ramakrishnan of Belmont CA (US) for microsoft technology licensing, llc
IPC Code(s): G06F9/451
CPC Code(s): G06F9/453
Abstract: in an example embodiment, machine learning is utilized to make recommendations for next actions by users of an online network. these next actions are called “next best actions.” the machine learning may be performed to train a multitask deep machine learning model to make recommendations based on a series of inputs, including, for example, contextual information that relies upon action sequences of the user and historical users, and user intent. the use of a multitask deep machine learning model allows for the model to generate action recommendations that are personalized, contextual, and coordinate across various different aspects of the online network, rather than being limited to only a single aspect. likewise, the multi-task deep machine learning model can also be tailored to optimized different use-case specific objectives while at the same time being easy to scale and maintain.
20240411580. FAST PATH INTERRUPT INJECTION_simplified_abstract_(microsoft technology licensing, llc)
Inventor(s): Rian Patrick QUINN of Highlands Ranch CO (US) for microsoft technology licensing, llc, Xin David ZHANG of Duvall WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F9/455
CPC Code(s): G06F9/45558
Abstract: the state of an interrupt is identified. an eligibility value corresponding to the interrupt is generated based on the state of the interrupt. the eligibility value is indicative of whether the interrupt should be processed by a first processing path or a second processing path, the second processing path being lower latency than the first processing path, and the second processing path bypassing operations performed in the first processing path. when an interrupt is received at an assembly language processing system, from a hardware device, the assembly language processing system accesses the eligibility value corresponding to the interrupt and routes the interrupt to the first or second processing path based on the eligibility value.
Inventor(s): Prasanna Chromepet PADMANABHAN of Redmond WA (US) for microsoft technology licensing, llc, Somesh GOEL of Newcastle WA (US) for microsoft technology licensing, llc, Jun SHI of Redmond WA (US) for microsoft technology licensing, llc, Scott Alan MANCHESTER of Moses Lake WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F9/455, H04L67/10, H04L67/141
CPC Code(s): G06F9/45558
Abstract: a computer implemented method includes granting a subscriber client access to a cloud service-based resource group within a subscriber controlled computing environment. the subscriber controlled computing environment has a virtual network. a direct network connection is established via the virtual network to a domain controller in the subscriber controlled computing environment. the virtual network is extended to a subscriber client virtual machine in a producer cloud service. the the subscriber client virtual machine is then joined with the virtual network. access to the subscriber client virtual machine is provided via the subscriber controlled computing environment and virtual network.
Inventor(s): David Thomas CHISNALL of Cambridge (GB) for microsoft technology licensing, llc, Matthew John PARKINSON of Cambridge (GB) for microsoft technology licensing, llc, Sylvan Wesley CLEBSCH of Cambridge (GB) for microsoft technology licensing, llc, Roy SCHUSTER of London (GB) for microsoft technology licensing, llc
IPC Code(s): G06F9/50, G06F9/355, G06F9/455, G06F9/54, G06F21/57
CPC Code(s): G06F9/5016
Abstract: a method of memory deallocation across a trust boundary between a first software component and a second software component is described. some memory is shared between the first and second software components. an in-memory message passing facility is implemented using the shared memory. the first software component is used to deallocate memory from the shared memory which has been allocated by the second software component. the deallocation is done by: taking at least one allocation to be freed from the message passing facility; and freeing the at least one allocation using a local deallocation mechanism while validating that memory access to memory owned by data structures related to memory allocation within the shared memory are within the shared memory.
Inventor(s): Karla Jean SAUR of Seattle WA (US) for microsoft technology licensing, llc, Joyce Yu CAHOON of Woodinville WA (US) for microsoft technology licensing, llc, Yiwen ZHU of San Francisco CA (US) for microsoft technology licensing, llc, Anna PAVLENKO of Edmonds WA (US) for microsoft technology licensing, llc, Jesus CAMACHO RODRIGUEZ of Sunnyvale CA (US) for microsoft technology licensing, llc, Brian Paul KROTH of Madison WI (US) for microsoft technology licensing, llc, Travis Austin WRIGHT of Issaquah WA (US) for microsoft technology licensing, llc, Michael Edward NELSON of Redmond WA (US) for microsoft technology licensing, llc, David LIAO of Sammamish WA (US) for microsoft technology licensing, llc, Andrew Sherman CARTER of Snoqualmie WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F9/50
CPC Code(s): G06F9/505
Abstract: system, methods, apparatuses, and computer program products are disclosed for auto-scaling of a deployment based on resource utilization data for a workload executing on the deployment. a resource availability is determined based on the resource utilization data and a current resource allocation of the deployment. a severity of resource throttling of the workload may be determined based on the resource utilization data, and a scaling factor is determined based at least on the severity of resource throttling. in response to at least the resource availability satisfying a predetermined condition with a predetermined threshold, the deployment is scaled based on the scaling factor.
Inventor(s): Sanjay RAMANUJAN of Sammamish WA (US) for microsoft technology licensing, llc, Karthik RAMAN of Sammamish WA (US) for microsoft technology licensing, llc, Rakesh KELKAR of Bellevue WA (US) for microsoft technology licensing, llc, Pei-Hsuan HSIEH of Bellevue WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F11/34, G06Q30/0283
CPC Code(s): G06F11/3414
Abstract: this document relates to predicting performance of large artificial intelligence (lai) models that are too large to be handled by a single computing device. one example can receive a sample workload for a trained lai model and identify multiple nodes functioning as a cluster to instantiate an instance of the trained lai model. the example can predict performance characteristics for accomplishing the sample workload on the cluster and can cause at least some of the predicted performance characteristics to be presented on a user interface.
Inventor(s): AARON YUE-CHIU CHAN of PROVO UT (US) for microsoft technology licensing, llc., ANANT GIRISH KHARKAR of HUNTERSVILLE NC (US) for microsoft technology licensing, llc., YEVHEN MOHYLEVSKYY of REDMOND WA (US) for microsoft technology licensing, llc., KALPATHY SITARAMAN SIVARAMAN of BOTHELL WA (US) for microsoft technology licensing, llc., NEELAKANTAN SUNDARESAN of BELLEVUE WA (US) for microsoft technology licensing, llc., ROSHANAK ZILOUCHIAN MOGHADDAM of KIRKLAND WA (US) for microsoft technology licensing, llc.
IPC Code(s): G06F11/36
CPC Code(s): G06F11/3624
Abstract: a vulnerability detection and repair system utilize a classifier model to detect a software vulnerability in a source code snippet and the tokens in the source code snippet attributable to the vulnerability. a large language model is then given the vulnerable source code snippet, its vulnerability type, the vulnerability tokens, and a few-shot examples to determine whether or not the source code snippet includes the identified vulnerability. the few-shot examples include positive and negative samples of the type of vulnerability to guide the large language model towards the correct output.
Inventor(s): Ori LASLO of Rehovot (IL) for microsoft technology licensing, llc, Gilad KIRSHENBOIM of Petach Tiqva (IL) for microsoft technology licensing, llc
IPC Code(s): G06F12/08
CPC Code(s): G06F12/08
Abstract: a method for computer memory access includes, during execution of a machine learning model, receiving an input vector for multiplication with a matrix of network weight values. each network weight value of the matrix of network weight values is stored in computer memory using a stored quantity(s) of bits. for a network weight value of the matrix of network weight values, a representation quantity (r) of bits is determined to be used for representing the network weight value during multiplication with a corresponding vector value of the input vector, based at least in part on a magnitude of the corresponding vector value. the r bits of the network weight value are retrieved from the computer memory for multiplication with the corresponding vector value.
Inventor(s): Marko RADMILAC of Bellevue WA (US) for microsoft technology licensing, llc, Andrew James WALD of Redmond WA (US) for microsoft technology licensing, llc, Joshua Bryan Wyman CLEMONS of Seattle WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F16/21, G06F40/40
CPC Code(s): G06F16/212
Abstract: a meta-model topology comprises a plurality of functions and conforms to a global label schema. a new function not included in the plurality of functions is integrated into the meta-model topology. a particular label of interest that is associated with the new function is identified and the new function is configured such that an output from the new function conforms to an output form corresponding to the particular label of interest from the global label schema. the new function is then integrated into the meta-model topology and the meta-model topology that includes the new function is used to generate a model graph. the model graph is then deployed to a remote application that is configured to receive data prompts comprising input data processed by nodes of the model graph.
Inventor(s): Yeye HE of Bellevue WA (US) for microsoft technology licensing, llc, Cong YAN of Issaquah WA (US) for microsoft technology licensing, llc, Yue WANG of Redmond WA (US) for microsoft technology licensing, llc, Surajit CHAUDHURI of Kirkland WA (US) for microsoft technology licensing, llc, Peng LI of Atlanta GA (US) for microsoft technology licensing, llc
IPC Code(s): G06F16/22, G06F16/21
CPC Code(s): G06F16/2282
Abstract: this document relates to relational databases and corresponding data tables. non-conforming data tables can be automatically transformed into conforming relational data tables. one example can obtain conforming relational data tables and can generate training data without human labelling by identifying a transformational operator that will transform an individual conforming relational data table to a non-conforming data table and an inverse transformational operator that will transform the non-conforming data table back to the individual conforming relational data table. the example can train a model with the training data. the trained model can synthesize programs to transform other non-conforming data tables to conforming relational data tables.
Inventor(s): Marko RADMILAC of Bellevue WA (US) for microsoft technology licensing, llc, Andrew James WALD of Redmond WA (US) for microsoft technology licensing, llc, Joshua Bryan Wyman CLEMONS of Seattle WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F16/242, G06F16/2457
CPC Code(s): G06F16/2433
Abstract: a model comprises different processing nodes that perform different functions on input data, such as data prompts. each node has batching criteria including a threshold value that when exceeded triggers a transmission of a batch of processing requests to the corresponding node. different processing requests for a node are routed to a batching cache for that node. processing requests are prevented from being routed to the node until the batching criteria is met. once the batching criteria is met, a batch of processing requests stored in the batching cache are routed to the corresponding node.
Inventor(s): Yinan LI of Redmond WA (US) for microsoft technology licensing, llc, Badrish CHANDRAMOULI of Redmond WA (US) for microsoft technology licensing, llc, Jianan LU of Plainsboro NJ (US) for microsoft technology licensing, llc
IPC Code(s): G06F16/2453
CPC Code(s): G06F16/2453
Abstract: systems and methods of processing queries for column-oriented database systems involve processing the queries to identify which of values of column data are to be accessed for the query. a select bitmap is then generated having k bits wherein each bit corresponds to one of the values of the column. the select bitmap is generated such that each bit representing a value of the column data that is to be accessed for the query has a first value and each bit representing a value of the column data that is not to be accessed for the query has a second value. encoded values are then extracted from memory for each of the values represented in the select bitmap by a bit having the first value. the extracted encoded values are decoded to generate decoded query data. the decoded query data is then processed to generate result data.
Inventor(s): Harish DORAISWAMY of Bengaluru (IN) for microsoft technology licensing, llc, Karthik Saligrama RAMACHANDRA of Bangalore (IN) for microsoft technology licensing, llc, Jayant Ramaswamy HARITSA of Bengaluru (IN) for microsoft technology licensing, llc
IPC Code(s): G06F16/2455, G06F16/2453, G06F16/248
CPC Code(s): G06F16/24556
Abstract: the present disclosure relates to methods and systems for using the computer graphics pipeline to execute database query operations on a graphics processing unit (gpu). the methods and systems use the graphics pipeline to transform relational data into images of the relational data. the methods and systems use the graphics pipeline to perform relational database operations on the images in response to a query and output a query result for the database operations.
Inventor(s): Harsh SHRIVASTAVA of Redmond WA (US) for microsoft technology licensing, llc, Shima IMANI of Sammamish WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F16/28, G06F16/26
CPC Code(s): G06F16/285
Abstract: this disclosure relates to a time series segmentation system that efficiently and accurately segments univariate time series data. for example, the time series segmentation system utilizes proxy variable time series to identify distinct segments in a univariate time series. to illustrate, the time series segmentation system generates proxy variables that approximate a univariate time series and combine with the time series to generate a supplemented multivariate time series. the time series segmentation system then divides the supplemented multivariate time series into portions using time-based windows, converts the windowed subsequences into graph objects using a sparse graph recovery model, utilizes a conditional similarity model to determine segmentation timestamps from the graph objects, and generates a segmented univariate time series from the segmentation timestamps.
Inventor(s): William BLUM of Bellevue WA (US) for microsoft technology licensing, llc, Sébastien Martin DIOTTE of Gatineau (CA) for microsoft technology licensing, llc, Martin Jean FONTAINE of Gatineau (CA) for microsoft technology licensing, llc, Edward Richard SOBEY of Cheltenham (GB) for microsoft technology licensing, llc, Tal Joseph MAOR of Seattle WA (US) for microsoft technology licensing, llc, Shruti RANJIT of Ajax (CA) for microsoft technology licensing, llc, Richard DONAGHY of Cheltenham (GB) for microsoft technology licensing, llc, Michal EZRETS GIL of Arad (IL) for microsoft technology licensing, llc, Cory James CLOWES of Ottawa (CA) for microsoft technology licensing, llc, Emily Maria MAKEDON of Chicago IL (US) for microsoft technology licensing, llc, Ross Kingsley WILYMAN of Churchdown (GB) for microsoft technology licensing, llc
IPC Code(s): G06F16/36, G06F16/332, G06F16/338, G06F16/35
CPC Code(s): G06F16/367
Abstract: a large language model consumes example query expressions, including a data access function, a data analytics function, or a data enrichment function. the large language model receives a centrally managed ontology. the large language model uses the centrally managed ontology, and identifies skill ontological types from the example query expressions. the skill ontological types are normalized (to the centrally managed ontology) input arguments types or structured output. the large language model receives context for an investigation and identifies a context ontological type. the large language model receives received skills, based on correlation between a skill ontological type, having connections in a graph to the received skills, and the context ontological type. the large language model produces and provides an indication of a suggested skill for the investigation.
Inventor(s): Tezan SAHU of Hyderabad (IN) for microsoft technology licensing, llc, Kishor CHAMUA of Hyderabad (IN) for microsoft technology licensing, llc, Anuska NANDY of Hyderabad (IN) for microsoft technology licensing, llc, Deepanjali SINGH of Uttar Pradesh (IN) for microsoft technology licensing, llc, Manish GUPTA of Hyderabad (IN) for microsoft technology licensing, llc
IPC Code(s): G06F16/9532, G06F40/274, G06F40/40
CPC Code(s): G06F16/9532
Abstract: this disclosure describes a query gateway system that provides an efficient and flexible framework for providing context-retained autosuggest queries from an autosuggest query system (e.g., a search engine query experience) to a generative language model system (e.g., an ai chat experience). for instance, the query gateway system establishes a framework to leverage the features and services of the autosuggest query system and automatically provides context-retained queries to the generative language model system using separate user interfaces that do not disrupt user navigation or require manual duplicative user input. additionally, the query gateway system incorporates additional enhancements, including an ai chat eligibility model and a query reformulation model, to improve the computational efficiency and accuracy of the ai chat system.
Inventor(s): Mojtaba Bisheh Niasar of Ithaca NY (US) for microsoft technology licensing, llc, Bharat S. Pillilli of El Dorado Hills CA (US) for microsoft technology licensing, llc
IPC Code(s): G06F17/14, G06F7/50, G06F7/523, G06F7/76
CPC Code(s): G06F17/142
Abstract: generally discussed herein are devices, systems, and methods for circuits that convert coefficients of a polynomial into or out of number theoretic transform (ntt) domain. a device can include butterfly operator circuits situated in parallel and to receive coefficients of a polynomial. the device can include a rearrange circuit configured to receive output of the butterfly operator circuits and route the output to input of the butterfly operator circuits. the device can include a memory situated to receive coefficients corresponding to the polynomial in a different domain that are output from the rearrange circuit.
Inventor(s): Shay Chriba SAKAZI of Herzliya (IL) for microsoft technology licensing, llc, Fady Copty of Haifa (IL) for microsoft technology licensing, llc, Tamer SALMAN of Haifa (IL) for microsoft technology licensing, llc, Ofir MONZA of Mishmar David (IL) for microsoft technology licensing, llc
IPC Code(s): G06F21/55
CPC Code(s): G06F21/552
Abstract: methods, systems, and computer storage media for providing data security posture management using an application discovery engine in a security management system. application discovery supports identifying and mapping various applications within a computing environment. in particular, application discovery can be provided as part of security management operations to assess security posture of applications, identify vulnerabilities, and ensure compliance with regulations. in operation, application discovery data associated with a plurality computing resources of a computing environment is accessed. an annotated application discovery graph comprising a plurality of entities that represent the plurality of computing resources is generated. the annotated application discovery graph is deployed to support generating security postures for computing environments. a request is received for a security posture of the computing environment. a security posture visualization that includes an application discovery graph annotation is generated. the security posture visualization is communicated to cause display of the security posture visualization.
20240411906. CONFIDENTIAL CONFERENCING_simplified_abstract_(microsoft technology licensing, llc)
Inventor(s): Ryen W. WHITE of Woodinville WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F21/62, G06V10/70, G06V20/40, H04L12/18
CPC Code(s): G06F21/62
Abstract: business and personal meetings are increasingly conducted virtually via video and/or audio conferencing. during such conferencing, participants can unwittingly leak private and/or confidential information with significant consequences for them and/or their employers. to prevent the disclosure of confidential content, one or more multimodal ml models are utilized by a conferencing service to detect and modify confidential content before, during, and/or after a live conferencing session. content considered private or confidential to one individual or organization may be different than to another, so the models may be trained to recognize individual or organization-specific content. furthermore, based on different user confidentiality levels, ml models may modify confidential content differently for different participants to a conferencing session.
Inventor(s): Hieu Trong Hoang of Seattle WA (US) for microsoft technology licensing, llc, Marcin Junczys-Dowmunt of Seattle WA (US) for microsoft technology licensing, llc, Anthony Aue of Seattle WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F40/58, G06F16/955, G06F40/205
CPC Code(s): G06F40/58
Abstract: systems and methods are provided for implementing url embeddings for aligning parallel documents that are corresponding web pages in at least two different languages. a computing system uses a pre-trained model of an ai system to calculate url embeddings for each url among a plurality of urls. the system identifies, based on closeness of the points represented by the url embeddings, a set of candidate parallel urls by analyzing the url embeddings for the plurality of urls or for a second plurality of urls that has been partitioned into a cluster, using a clustering algorithm. a set of parallel urls, associated with the parallel documents, is selected from the identified set of candidate parallel urls. document text and/or parallel sentences are extracted from web documents associated with the set of parallel urls to train a machine translation model for translating between two or more languages.
Inventor(s): Weiwei YANG of Seattle WA (US) for microsoft technology licensing, llc, Kateryna LYTVYNETS of Redmond WA (US) for microsoft technology licensing, llc, Prachi Manishkumar PATEL of Redmond WA (US) for microsoft technology licensing, llc, Amber HOAK of Silverdale WA (US) for microsoft technology licensing, llc, Spencer FOWERS of Duvall WA (US) for microsoft technology licensing, llc, Christopher Patrick O'DOWD of Seattle WA (US) for microsoft technology licensing, llc, Andrea BRITTO MATTOS LIMA of Sao Paulo (BR) for microsoft technology licensing, llc, Thiago VALLIN SPINA of Campinas (BR) for microsoft technology licensing, llc, Hayden HELM of San Francisco CA (US) for microsoft technology licensing, llc
IPC Code(s): G06N3/006, G06N3/045, G06N3/0475
CPC Code(s): G06N3/006
Abstract: in addition to an original prompt that is manually provided by a user, contextual information is sent to a generative ai to elicit a higher quality response. sensors collect audio, video, physiological, cognitive, environmental, and digital data from the user. machine-learning models evaluate the sensor data to infer the emotional state of the user. the emotional state is used to augment the original prompt with contextual information. the augmented prompt is fed into the generative ai to make it context-aware. accordingly, the generative ai can automatically pick up on non-verbal cues that the user did not manually articulate in the original prompt. just as a human-to-human conversation involves a combination of verbal and non-verbal communications, the present concepts enable the generative ai to also leverage non-verbal communication when interacting with human users.
Inventor(s): Marko RADMILAC of Bellevue WA (US) for microsoft technology licensing, llc, Andrew James WALD of Redmond WA (US) for microsoft technology licensing, llc, Joshua Bryan Wyman CLEMONS of Seattle WA (US) for microsoft technology licensing, llc
IPC Code(s): G06N3/0455
CPC Code(s): G06N3/0455
Abstract: a model graph receives a data prompt as input. the data prompt is segmented into multiple segments. an instance of the model graph is generated for each segment of the data prompt. each instance of the model graph is also pruned according to policy information associated with the model graph instance's corresponding data prompt segment. each instance of the model graph generates an intermediary output. a final output of the model graph for the entire data prompt is generated based on a combination of the intermediary outputs.
20240412045. LARGE TENSOR TILING_simplified_abstract_(microsoft technology licensing, llc)
Inventor(s): Yaron Baruch SHAPIRO of Petach Tikva (IL) for microsoft technology licensing, llc, Khalil Abdul-Hamid WATTAD of Jatt (IL) for microsoft technology licensing, llc, Evgeny ROYZEN of Qiryat Ono (IL) for microsoft technology licensing, llc, Asaf LEVY of Hod Hasharon (IL) for microsoft technology licensing, llc
IPC Code(s): G06N3/0464
CPC Code(s): G06N3/0464
Abstract: techniques for performing large tensor tiling (ltt) in hardware are enabled. ltt divides a large tensor (e.g., of unsupported size) into overlapping tiles (e.g., having supported tensor size(s)). a tensor may be processed processing the tiles. the output of each processed tile is stored, for example, in a systolic array considering the tile's placement in the large tensor. the output of all processed tiles is identical to the output of processing the large tensor. tiles may be processed by reusing data overlapping boundaries shared with other tiles. in some examples, overlapping data may be reused (e.g., written once) or partly reused (e.g., written twice). tiling large tensors with boundary duplication supports dynamic adaptation to a wide variety of tensor sizes, avoids re-reading duplicated data, and avoids reorganizing hardware for large tiles, which reduces power consumption and area, reduces complexity, and increases processing efficiency.
Inventor(s): Zhuo RUAN of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06N3/0495, G06T1/20
CPC Code(s): G06N3/0495
Abstract: various embodiments discussed herein are directed to improving hardware consumption and computing performance by performing neural network operations on dense tensors using sparse value information from original tensors. such dense tensors are condensed representations of other original tensors that include zeros or other sparse values. in order to perform these operations, particular embodiments provide an indication, via a binary map, of a position of where the sparse values and non-sparse values are in the original tensors. particular embodiments additionally or alternatively determine shape data of the original tensors so that these operations are accurate.
Inventor(s): Matthew Benjamin HASTINGS of Seattle WA (US) for microsoft technology licensing, llc, Parsa BONDERSON of Santa Barbara CA (US) for microsoft technology licensing, llc, Zhenghan WANG of Goleta CA (US) for microsoft technology licensing, llc, Jeongwan HAAH of Bellevue WA (US) for microsoft technology licensing, llc, David Alexander AASEN of Santa Barbara CA (US) for microsoft technology licensing, llc
IPC Code(s): G06N10/70
CPC Code(s): G06N10/70
Abstract: aspects of the disclosure include removing a faulty qubit in a quantum circuit. the faulty qubit is determined to be in the quantum circuit, the faulty qubit being associated with a plaquette having other qubits, where adjacent plaquettes are neighboring the plaquette. a route is determined to isolate the plaquette from the adjacent plaquettes. measurements are caused to be performed on the quantum circuit for the route that isolates the plaquette having the faulty qubit and the other qubits.
Inventor(s): Anand PADMANABHA IYER of Redmond WA (US) for microsoft technology licensing, llc, Ganesh ANANTHANARAYANAN of Sammamish WA (US) for microsoft technology licensing, llc, Yiwen ZHANG of Ann Arbor MI (US) for microsoft technology licensing, llc
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: optimizing ml pipeline deployment using an ml pipeline management system. a method includes receiving an indication of an input data source and input data type from the input data source. an indication of a plurality filters to be included in the pipeline, an ml model, and predetermined performance criteria is received. the method includes determining a physical topology of the ml pipeline and configuration of the filters or the ml model. the determined physical topology includes placement of the filters and the model, and the configuration. the determined physical topology satisfies the performance criteria. the filters and ml model are placed across an infrastructure, comprising a plurality of tiers, according to the determined physical topology.
Inventor(s): Kartik MATHUR of Seattle WA (US) for microsoft technology licensing, llc, Andrew David MYERS of Seattle WA (US) for microsoft technology licensing, llc, Yinyu Jin QUAN of Fullerton CA (US) for microsoft technology licensing, llc, Dalia Ahmed Essa SWELLUM of Snohomish WA (US) for microsoft technology licensing, llc, Fa Qiang TANG of Bellevue WA (US) for microsoft technology licensing, llc, Justin Jack TRAENKENSCHUH of Seattle WA (US) for microsoft technology licensing, llc
IPC Code(s): G06Q30/016, G06F9/451
CPC Code(s): G06Q30/016
Abstract: a system and method for evaluating performance of a model used in providing a response to a product help inquiry includes receiving the product help inquiry, classifying the product help inquiry as being associated with a topic related to a product, and retrieving a path of actions provided in a help documentation associated with the topic. a prompt is also generated based on the product help inquiry for transmission to the model and a response is provided by the model, before a path of actions included the response is extracted. contextual embeddings for the extracted path are generated and semantic similarities between contextual embeddings for the extracted path and embeddings generated for an expected response are measured. by generating contextual embeddings for the extracted path instead of the entire response, resources required for evaluating the response are significantly reduced. a path coverage metric is measured for the extracted path. a total evaluation value for the response is determined based on a weighted combination of one or more of the measured semantic similarity, path coverage metric, a path length metric or a path frequency metric.
Inventor(s): Aman Gupta of San Jose CA (US) for microsoft technology licensing, llc, Xincen Yu of Palo Alto CA (US) for microsoft technology licensing, llc, Ning Jin of Sunnyvale CA (US) for microsoft technology licensing, llc, Kuan Chen of Bellevue WA (US) for microsoft technology licensing, llc, Madhura Anil Deo of San Jose CA (US) for microsoft technology licensing, llc, Gina Paola Rangel of New York NY (US) for microsoft technology licensing, llc, Smriti R. Ramakrishnan of Belmont CA (US) for microsoft technology licensing, llc, Xiaoxi Zhao of Milpitas CA (US) for microsoft technology licensing, llc, Chun Lo of Mountain View CA (US) for microsoft technology licensing, llc, Arvind Murali Mohan of Sunnyvale CA (US) for microsoft technology licensing, llc, Hongbo Zhao of San Jose CA (US) for microsoft technology licensing, llc, Shifu Wang of San Jose CA (US) for microsoft technology licensing, llc, Jim Chang of Cupertino CA (US) for microsoft technology licensing, llc
IPC Code(s): G06Q30/0204, G06N3/08, G06Q10/1053, G06Q50/00
CPC Code(s): G06Q50/01
Abstract: in an example embodiment, a deep machine learning model ranks cohorts of users as well as cohorts of products in a single ranking. when utilized to determine which cohort members to display to a user, the system selects one user cohort and one product cohort as the “best” (e.g., the top ranked user cohort and the top ranked product cohort). this ranking may be based on a number of contextual and non-contextual features, including viewer features (characteristics of the user operating the user interface), viewee features (characteristics of or related to the litem that the user is viewing, such as the characteristics of another user whose profile the user is viewing), and viewer-viewee relationship features (indications about how the viewer and viewee are related, such as common schools, locations, places of employment, etc.).
Inventor(s): Jian WU of Bellevue WA (US) for microsoft technology licensing, llc, Jinyu LI of Bellevue WA (US) for microsoft technology licensing, llc, Zhuo CHEN of Woodinville WA (US) for microsoft technology licensing, llc, Naoyuki KANDA of Bellevue WA (US) for microsoft technology licensing, llc, Takuya YOSHIOKA of Bellevue WA (US) for microsoft technology licensing, llc
IPC Code(s): G10L17/18, G10L17/02, G10L17/04, G10L17/14, G10L25/78
CPC Code(s): G10L17/18
Abstract: systems and methods are provided for instantiating, modifying, adapting, and using a factorized neural transducer for multi-speaker automatic speech recognition. the factorized neural transducer includes a vocabulary predictor with multiple hidden states to process speech from different speakers, a non-vocabulary predictor that facilitates the prediction of channel change tokens indicating a speaker change in input speech data, an encoder used to encode acoustic features of the input speech data, and a joint network.
Inventor(s): Jon Thomas WOODYARD of Livermore CA (US) for microsoft technology licensing, llc, Rahul AGARWAL of Livermore CA (US) for microsoft technology licensing, llc
IPC Code(s): H01L23/31, H01L23/14, H01L23/498, H01L25/16
CPC Code(s): H01L23/3121
Abstract: examples are provided that relate to embedding, in a core of a substrate, an electronic component having a thickness less than a thickness of the core. one example provides an electronic device comprising a substrate comprising a core and one or more buildup layers coupled with the core, each buildup layer comprising a metal layer and a dielectric layer. the core comprises a center comprising a plurality of plies, and an additional layer comprising one or more additional plies. the electronic device further comprises an electronic component embedded in at least one of the center or the additional layer of the core. the electronic component comprises a thickness less than a thickness of the core. the electronic device further comprises an integrated circuit die coupled with the substrate and electrically connected to the electronic component.
Inventor(s): Vaidehi ORUGANTI of Kirkland WA (US) for microsoft technology licensing, llc, Husam Atallah ALISSA of Snoqualmie WA (US) for microsoft technology licensing, llc, Christian L. BELADY of Mercer Island WA (US) for microsoft technology licensing, llc
IPC Code(s): H01M4/36
CPC Code(s): H01M4/366
Abstract: techniques for increasing an atomic surface area of contact surfaces of an energy source to cause the energy source to increase its energy output are disclosed. an energy source includes first and second contact surfaces, where these contact surfaces are structured to facilitate energy transfer between the energy source and a receiving unit. the contact surfaces each have a first surface area state with a first amount of atomic surface area. a process is applied to the contact surfaces to change the first surface area state to a second surface area state. the second surface area state has a second amount of atomic surface area which is more than the first amount of atomic surface area. the applied process may include applying a current or applying a short to the contact surfaces.
Inventor(s): Mojtaba BISHEH NIASAR of Ithaca NY (US) for microsoft technology licensing, llc, Bharat S. PILLILLI of El Dorado Hills CA (US) for microsoft technology licensing, llc
IPC Code(s): H04L9/32
CPC Code(s): H04L9/32
Abstract: generally discussed herein are devices, systems, and methods for high-level synthesis of a kyber cryptography circuit. a method can include defining, by a high-level programming language, behavior of a kyber cryptography circuit resulting in a behavior definition. the behavior of the kyber cryptography circuit can include parallel butterfly operations with output of the parallel butterfly operations fedback directly to inputs of the parallelized butterfly operations. the method can include converting, by high-level synthesis (hls), the behavior definition to a gate-level implementation resulting in a circuit definition. the method can include implementing the circuit definition in hardware.
Inventor(s): Mayukh RAY of Sammamish WA (US) for microsoft technology licensing, llc, Alistair James LOWE of Ipswich (GB) for microsoft technology licensing, llc
IPC Code(s): H04L9/32
CPC Code(s): H04L9/3268
Abstract: methods and systems are described which obtain a service token at an edge device. embodiments obtain a device certificate from an authentication service based on a private key which is associated with a public key. the public key is further associated with a device identifier for the edge device at a directory service. embodiments send a request for a service token to an authentication service from a directory service based on the private key where the directory service has identified the public key for the edge device. other embodiments extract the device identifier from the device certificate and send a request for a service token to the directory service, where the request includes the device certificate and the device identifier. embodiments receive the service token from the directory service and use the service token to access a service.
Inventor(s): Nidhi VERMA of Bellevue WA (US) for microsoft technology licensing, llc, James John WALSH of Wicklow (IE) for microsoft technology licensing, llc, Oliver John CASTLE of Dublin (IE) for microsoft technology licensing, llc, Orla Patricia SHERIDAN of Westmeath (IE) for microsoft technology licensing, llc
IPC Code(s): H04L41/0604, H04L41/0681
CPC Code(s): H04L41/0618
Abstract: a fault injection system for a cloud infrastructure utilizes fault injection agents instantiated on components of the cloud infrastructure to inject fault into the components. the faults are based on fault definitions which define the type(s) of fault(s) to inject, the scope for injecting the fault into the cloud infrastructure, deployment information for deploying the fault in the cloud, and remediation information which defines a plan for remediating the fault in the cloud infrastructure. the system monitors the impact of faults on the components which enables component dependencies to be determined.
Inventor(s): Mustafa KASAP of Kenmore WA (US) for microsoft technology licensing, llc
IPC Code(s): H04L41/0631, G06F40/166, G06F40/40
CPC Code(s): H04L41/065
Abstract: systems and methods are provided for determining a root cause of an incident that occurred in a 5g/6g multi-access edge computing and core network system. in particular, the disclosed technology is directed to using a pre-trained generative model to determine the root cause for an incident as recorded in event data of a system log. the present disclosure generates a prompt for the generative model to determine the root cause for an incident as recorded in the system log. the prompt comprises a combination including event data from a system log, function hierarchy graph data, and function information as grounding information in a prefix of the prompt. the prompt further includes a question that requests determining a root cause of an incident as recorded in the event data. an answer as generated by the pre-trained generative model indicates of the root cause and a function call associated with the incident.
Inventor(s): Prashant RANJAN of San Jose CA (US) for microsoft technology licensing, llc, Abdulkader KABBANI of Menlo Park CA (US) for microsoft technology licensing, llc
IPC Code(s): H04L47/125, H04L47/26, H04L47/62
CPC Code(s): H04L47/125
Abstract: a computing system for transport layer network recovery on a packet-switched computer network includes a source computing device with a processor that executes a network traffic communication module, a load balancing module, and a congestion control module. the network traffic communication module provisions a plurality of source ports to transmit outbound packets to a destination computing device, each source port being associated with a respective network path. the load balancing module assigns each outbound packet to one of the source ports using a port scheduling algorithm to uniformly distribute the packets among the source ports and associated network paths. the congestion control module detects a congestion control condition for a packet transmitted via a source port associated with a congested network path. the load balancing module assigns a next source port for a next outbound packet from a remainder of the source ports not associated with the congested network path.
Inventor(s): Felix ANDREW of Seattle WA (US) for microsoft technology licensing, llc, Ryan Gregory CROPP of Seattle WA (US) for microsoft technology licensing, llc, Laurentiu T. NEDELCU of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): H04L51/02, H04L9/40, H04L51/046, H04L51/18
CPC Code(s): H04L51/02
Abstract: systems and method for providing an application chatbot that provides a conversational interface that receives natural language input from an application user, interprets the user's intent, and uses application-related context for generating and providing a contextually accurate response in a conversation with the user. in some examples, the application chatbot determines an action to perform corresponding to the response and provides an option to perform the action in the conversational user interface. a selection of the option causes the action to be performed.
Inventor(s): Suyin LIU of Suzhou (CN) for microsoft technology licensing, llc, Jie LIU of Bellevue WA (US) for microsoft technology licensing, llc, Na LI of Bellevue WA (US) for microsoft technology licensing, llc, Yizhong WU of Shanghai (CN) for microsoft technology licensing, llc, Chuanbo ZHANG of Suzhou (CN) for microsoft technology licensing, llc, Xiangyi DENG of Suzhou (CN) for microsoft technology licensing, llc, Yiteng YU of Shanghai (CN) for microsoft technology licensing, llc, Yu ZHANG of Suzhou (CN) for microsoft technology licensing, llc, Yu XIA of Suzhou (CN) for microsoft technology licensing, llc, Jonathan SHI of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): H04L9/40
CPC Code(s): H04L63/083
Abstract: this disclosure relates to a context enforcement system that efficiently and securely protects tenant context information that travels across microservices in a multi-tenant distributed cloud computing system and protects against data leaks that often occur in conventional microservice management systems. for example, the context enforcement system ensures secure external and internal communications and context isolation by providing various shared library functions to microservices of a multi-tenant distributed cloud computing system. additionally, the shared library provided by the context enforcement system improves the efficiency of the multi-tenant distributed cloud computing system by allowing microservices to focus on target operations rather than also maintaining and performing additional redundant functions.
Inventor(s): Rohan GANDHI of Pune (IN) for microsoft technology licensing, llc
IPC Code(s): H04L9/40
CPC Code(s): H04L63/101
Abstract: the present disclosure relates to methods and systems for reducing access control lists (acls). the methods and systems combine multiple allowed internet protocol (ip) addresses from the acls to a single or small number of ip prefixes. the methods and systems calculate a minimum of the bit changes in transforming the ip addresses from one to another. using the information for the minimum bit changes in transforming the ip addresses from one to another, the methods and systems build a graph of ip addresses, where the nodes are the ip addresses, and the edges denote that the ip addresses are transformed from one to another using the minimum number of bit changes. the methods and systems recursively merge the nodes to reduce the acl rules into a compressed acl rule graph. the methods and systems generate a reduced set of acl rules using the compressed acl rule graph.
20240414178. SECURITY SYSTEM_simplified_abstract_(microsoft technology licensing, llc)
Inventor(s): Hani NEUVIRTH-TELEM of Tel Aviv (IL) for microsoft technology licensing, llc, Michal Shechter Nachmany of Hod Hasharon (IL) for microsoft technology licensing, llc, Yoram Cohen of Givataim (IL) for microsoft technology licensing, llc, Hadas Orgad of Herzliya (IL) for microsoft technology licensing, llc
IPC Code(s): H04L9/40
CPC Code(s): H04L63/1425
Abstract: in accordance with the methods herein, an incident description characterizing a security incident and a manually-assigned incident characterization label characterizing the same security incident are received. the manually-assigned incident characterization label corresponds to one of a plurality of incident classification categories (including, for example, true positive, false positive) assigned by a user of a security monitoring system such as a siem system. a trained incident classification model is applied to the incident description, to classify the security incident in relation to the incident classification categories, thus generating a model classification prediction. the model classification prediction is compared with the manually-assigned incident characterization label and where it is determined that the manually-assigned incident characterization label and the model classification prediction are mismatched, a security mitigation action associated with the security incident is performed.
Inventor(s): Xavier GEERINCK of Aventin (BE) for microsoft technology licensing, llc
IPC Code(s): H04L67/62, G06F16/2455, H04L67/10
CPC Code(s): H04L67/62
Abstract: a stream tool is disclosed that allows a user to seamlessly connect with the different data streams, regardless of the streams' transmission platforms or communication protocols, in order to visually see a representation of the type of data that the data streams are transmitting. a user may specify a particular data stream and provide corresponding connection details. a collection of abstracted software functions enable interaction with the different stream platforms and protocols. using these abstracted functions, a stream-processing service accesses a requested data stream and samples its data events for either sample timeframe or up to a threshold number of data events. the sampled data events are parsed and visually presented to the user in an easy-to-understand format. the user may then inspect the data stream's data for use in developing robust applications that may integrate and use such data.
Inventor(s): Amer Aref Hassan of Kirkland WA (US) for microsoft technology licensing, llc, Vandana THOMAS of Woodinville WA (US) for microsoft technology licensing, llc, Michael J. DAVIS of Seattle WA (US) for microsoft technology licensing, llc, Abhilash Chandrasekharan NAIR of Bothell WA (US) for microsoft technology licensing, llc
IPC Code(s): H04M1/72484, H04L65/1089, H04M1/72403, H04M1/72463
CPC Code(s): H04M1/72484
Abstract: a device and method for routing an incoming call event to a selected telephony application based on network quality. upon receiving an incoming call, the device measures the quality of the network connection and compares it to a predetermined threshold. based on this comparison, the device automatically selects between a default first telephony application and a second telephony application that requires higher network quality. the incoming call is then routed to the selected application, and a communication link is established with capabilities adapted to the measured network quality. the device may continuously monitor network quality and transition between applications if conditions change. additional features include detecting screen viewability to trigger transitions, associating call continuity identifiers for seamless switching, and adjusting thresholds based on network type. this approach optimizes call handling by dynamically selecting the most appropriate application given current network conditions.
Inventor(s): Suvarna Raju MADHEY of Pune (IN) for microsoft technology licensing, llc
IPC Code(s): H04M3/56, G06F3/04842, G06F3/14
CPC Code(s): H04M3/568
Abstract: techniques are described for providing real-time audio and/or video feedback during a conference call. audio feedback can be provided during a conference call in response to a participant unmuting the participant's microphone. for example, a feedback period can be initiated upon receiving an unmute indication. during the feedback period, the participant's microphone audio can be sent, in real-time, back to the participant for playback in addition to sending to the other participants. after the feedback period is over, the participant can be removed from receiving their microphone audio. video feedback can be provided during a conference call in response to screen sharing. for example, during a feedback period, the participant can receive, in real-time, a screen content thumbnail of their shared screen content. after the feedback period is over, the screen content thumbnail can stop being sent.
Inventor(s): Gary J. Sullivan of Bellevue WA (US) for microsoft technology licensing, llc, You Zhou of Sammamish WA (US) for microsoft technology licensing, llc, Chih-Lung Lin of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): H04N19/174, H04N19/105, H04N19/109, H04N19/136, H04N19/139, H04N19/142, H04N19/147, H04N19/17, H04N19/177, H04N19/179, H04N19/513, H04N19/52, H04N19/523
CPC Code(s): H04N19/174
Abstract: approaches to selection of motion vector (“mv”) precision during video encoding are presented. these approaches can facilitate compression that is effective in terms of rate-distortion performance and/or computational efficiency. for example, a video encoder determines an mv precision for a unit of video from among multiple mv precisions, which include one or more fractional-sample mv precisions and integer-sample mv precision. the video encoder can identify a set of mv values having a fractional-sample mv precision, then select the mv precision for the unit based at least in part on prevalence of mv values (within the set) having a fractional part of zero. or, the video encoder can perform rate-distortion analysis, where the rate-distortion analysis is biased towards the integer-sample mv precision. or, the video encoder can collect information about the video and select the mv precision for the unit based at least in part on the collected information.
Inventor(s): Charbel KHAWAND of Sammamish WA (US) for microsoft technology licensing, llc
IPC Code(s): H04W4/02, H04W4/021
CPC Code(s): H04W4/027
Abstract: the disclosure herein describes enabling menuless operations using spatially aware tags with virtual geo-fenced boundaries. a tag manager implemented on a processor obtains movement data associated with an ultrawide band (uwb) enabled initiator device within a selected geo-fenced zone. the movement data is generated by one or more uwb enabled responder(s) within the selected geo-fenced zone. the movement data includes data describing 3d movements of the initiator device within the selected geo-fenced zone. a tag manager identifies a sequence of 3d movements of the initiator device using the movement data. the tag manager maps the sequence of 3d movements to zone-specific and/or user-specific action(s) using a menuless operations mapping table. the tag manager triggers a target computing device within the selected geo-fenced zone to perform the matched action(s). the target computing device can include an internet of things (iot) device, a computing device, or the initiator device itself.
Inventor(s): Manikanta KOTARU of Kenmore WA (US) for microsoft technology licensing, llc
IPC Code(s): H04W24/02
CPC Code(s): H04W24/02
Abstract: systems and methods are provided for determining a set of control parameter data associated with a base station of a 5g multi-access edge computing and core network. in particular, the disclosed technology is directed to use a deep reinforcement-based learning (drl) model to iteratively reinforce and improve the set of control parameter data at the base station. the drl model determines the set of control parameter data as action based on a current set of network state data as state, according a set of target conditions used as rewards. a drl server periodically receives network state data from the base station through a radio access network intelligent controller (ric). given the network state data, the drl model determines control parameter data as output. the drl server transmits the control parameter data to the base station via ric. the periodic reinforcement-based learning dynamically improves a network performance of the base station.
Inventor(s): Adam Kristopher SPIEGELMAN of The Hills TX (US) for microsoft technology licensing, llc, Shane Gerrard KAVANAGH of Leander TX (US) for microsoft technology licensing, llc
IPC Code(s): H05K7/20
CPC Code(s): H05K7/20727
Abstract: a device may include a body including a channel therein, wherein the channel includes a longitudinal component and a lateral component transverse to the longitudinal component and at least a portion of the channel is diagonal to a longitudinal direction. a device may include a frame with rails to receive the body and allow longitudinal movement of the body relative to the frame in the longitudinal direction. a device may include a carrier connected to the body by fasteners positioned in the channels and movable relative to the body via movement of the fasteners within the channels.
Inventor(s): Ehsan NASR AZADANI of Bellevue WA (US) for microsoft technology licensing, llc, Eric C. PETERSON of Woodinville WA (US) for microsoft technology licensing, llc, Winston Allen SAUNDERS of Seattle WA (US) for microsoft technology licensing, llc, Sean Patrick ABBOTT of Redmond WA (US) for microsoft technology licensing, llc, Mark Alan MONROE of Louisville CO (US) for microsoft technology licensing, llc, Sean Michael JAMES of Olympia WA (US) for microsoft technology licensing, llc, Kristofer Andrew JOHNSON of Snoqualmie WA (US) for microsoft technology licensing, llc, Christian L. BELADY of Mercer Island WA (US) for microsoft technology licensing, llc, Anthony Joseph BIANCHI of Urbandale IA (US) for microsoft technology licensing, llc
IPC Code(s): H05K7/20, G06N3/02
CPC Code(s): H05K7/20836
Abstract: a system may include a datacenter supervisory control system (scs). a system may include at least one datacenter sensor in data communication with the scs to communicate a datacenter state variable to the scs. a system may include at least one environmental sensor in data communication with the scs to communicate an environmental state variable to the scs. a system may include a water aware controller in data communication with the scs, wherein the water aware controller includes a predictor that receives a datacenter state input based on the datacenter state variable, the environmental state input based on the environmental state variable, and at least one user objective function, and the water aware controller transmits a selected action to the scs to meet a setpoint based on the datacenter state input and the environmental state input.
Inventor(s): Parsa BONDERSON of Santa Barbara CA (US) for microsoft technology licensing, llc, Christina Paulsen KNAPP of Goleta CA (US) for microsoft technology licensing, llc, Roman Bela BAUER of Santa Barbara CA (US) for microsoft technology licensing, llc, Emily Anne TOOMEY of Santa Barbara CA (US) for microsoft technology licensing, llc, David Alexander AASEN of Santa Barbara CA (US) for microsoft technology licensing, llc
IPC Code(s): H10N60/10, H01L23/522, H01L29/778
CPC Code(s): H10N60/11
Abstract: a computing system is presented. the computing system comprises a majorana island at which a plurality of majorana zero modes are instantiated, and a grounded region tunably coupled to one of the majorana zero modes. the grounded region comprises at least a two-dimensional electron gas (2deg) layer. a first dielectric layer is adjacent to the 2deg layer. a grounded gate directly contacts the 2deg layer through a via fill.
MICROSOFT TECHNOLOGY LICENSING, LLC patent applications on December 12th, 2024
- MICROSOFT TECHNOLOGY LICENSING, LLC
- B01J19/00
- C07K1/04
- C25D5/16
- C25D5/48
- C25D17/10
- CPC B01J19/0046
- Microsoft technology licensing, llc
- G01C21/34
- G06N3/0499
- CPC G01C21/3453
- G06F3/0481
- G06F3/0482
- G06F3/04842
- G06F3/14
- G06F9/451
- G06F11/34
- G09G5/14
- CPC G06F3/0481
- G06F3/0485
- G06T13/80
- CPC G06F3/0482
- G06F3/06
- CPC G06F3/0631
- G06F1/16
- G06F3/0484
- G06F3/04883
- CPC G06F3/1423
- G06F8/33
- G06F8/73
- CPC G06F8/33
- G06F8/41
- CPC G06F8/456
- CPC G06F9/453
- G06F9/455
- CPC G06F9/45558
- H04L67/10
- H04L67/141
- G06F9/50
- G06F9/355
- G06F9/54
- G06F21/57
- CPC G06F9/5016
- CPC G06F9/505
- G06Q30/0283
- CPC G06F11/3414
- G06F11/36
- CPC G06F11/3624
- Microsoft technology licensing, llc.
- G06F12/08
- CPC G06F12/08
- G06F16/21
- G06F40/40
- CPC G06F16/212
- G06F16/22
- CPC G06F16/2282
- G06F16/242
- G06F16/2457
- CPC G06F16/2433
- G06F16/2453
- CPC G06F16/2453
- G06F16/2455
- G06F16/248
- CPC G06F16/24556
- G06F16/28
- G06F16/26
- CPC G06F16/285
- G06F16/36
- G06F16/332
- G06F16/338
- G06F16/35
- CPC G06F16/367
- G06F16/9532
- G06F40/274
- CPC G06F16/9532
- G06F17/14
- G06F7/50
- G06F7/523
- G06F7/76
- CPC G06F17/142
- G06F21/55
- CPC G06F21/552
- G06F21/62
- G06V10/70
- G06V20/40
- H04L12/18
- CPC G06F21/62
- G06F40/58
- G06F16/955
- G06F40/205
- CPC G06F40/58
- G06N3/006
- G06N3/045
- G06N3/0475
- CPC G06N3/006
- G06N3/0455
- CPC G06N3/0455
- G06N3/0464
- CPC G06N3/0464
- G06N3/0495
- G06T1/20
- CPC G06N3/0495
- G06N10/70
- CPC G06N10/70
- G06N20/00
- CPC G06N20/00
- G06Q30/016
- CPC G06Q30/016
- G06Q30/0204
- G06N3/08
- G06Q10/1053
- G06Q50/00
- CPC G06Q50/01
- G10L17/18
- G10L17/02
- G10L17/04
- G10L17/14
- G10L25/78
- CPC G10L17/18
- H01L23/31
- H01L23/14
- H01L23/498
- H01L25/16
- CPC H01L23/3121
- H01M4/36
- CPC H01M4/366
- H04L9/32
- CPC H04L9/32
- CPC H04L9/3268
- H04L41/0604
- H04L41/0681
- CPC H04L41/0618
- H04L41/0631
- G06F40/166
- CPC H04L41/065
- H04L47/125
- H04L47/26
- H04L47/62
- CPC H04L47/125
- H04L51/02
- H04L9/40
- H04L51/046
- H04L51/18
- CPC H04L51/02
- CPC H04L63/083
- CPC H04L63/101
- CPC H04L63/1425
- H04L67/62
- CPC H04L67/62
- H04M1/72484
- H04L65/1089
- H04M1/72403
- H04M1/72463
- CPC H04M1/72484
- H04M3/56
- CPC H04M3/568
- H04N19/174
- H04N19/105
- H04N19/109
- H04N19/136
- H04N19/139
- H04N19/142
- H04N19/147
- H04N19/17
- H04N19/177
- H04N19/179
- H04N19/513
- H04N19/52
- H04N19/523
- CPC H04N19/174
- H04W4/02
- H04W4/021
- CPC H04W4/027
- H04W24/02
- CPC H04W24/02
- H05K7/20
- CPC H05K7/20727
- G06N3/02
- CPC H05K7/20836
- H10N60/10
- H01L23/522
- H01L29/778
- CPC H10N60/11
