Microsoft Technology Licensing, LLC patent applications on March 27th, 2025
Patent Applications by Microsoft Technology Licensing, LLC on March 27th, 2025
Microsoft Technology Licensing, LLC: 31 patent applications
Microsoft Technology Licensing, LLC has applied for patents in the areas of G06T17/00 (3), H01M10/0525 (2), G06T7/11 (2), H04L9/32 (2), H01M10/0562 (2) H04L9/3236 (2), G06F9/45558 (2), H01M10/0562 (2), G06F3/04144 (1), G06N3/0495 (1)
With keywords such as: data, based, entity, input, computing, primary, replica, access, processing, and network in patent application abstracts.
Patent Applications by Microsoft Technology Licensing, LLC
Inventor(s): Molly Jo GESELL of Bothell WA US for microsoft technology licensing, llc, Elizabeth Ann PINA of Lynnwood WA US for microsoft technology licensing, llc, Adam Benjamin MESHI of Herzliya IL for microsoft technology licensing, llc, Robert Bryce JOHNSON of Sammamish WA US for microsoft technology licensing, llc, Solomon Stone ROMNEY of Bellevue WA US for microsoft technology licensing, llc, Tal BIZINSKY TOVEL of Ramat HaSharon IL for microsoft technology licensing, llc, Arthur GERSHFELD of Holon IL for microsoft technology licensing, llc, Hila MALIHI of Jerusalem IL for microsoft technology licensing, llc
IPC Code(s): G06F3/041, G06F3/0354
CPC Code(s): G06F3/04144
Abstract: computing devices, touchpads, and related methods for adjusting a force threshold for touch input on a touchpad are disclosed. in one example, a computing device comprises a force sensing touchpad, a processor, and a memory storing instructions executable by the processor to receive a first contact on the touchpad. an area of touch of the first contact is determined, and an adjusted force threshold for touch input is determined based at least on the area of the touch. a second contact is received on the touchpad, and a touch force of the second contact is determined. based at least on determining that the touch force exceeds the adjusted force threshold, a touch input on the touchpad is registered.
Inventor(s): Nir DAVID of Tel Aviv IL for microsoft technology licensing, llc, Oren ISTRIN of Tel Aviv IL for microsoft technology licensing, llc, Segev RAVGAD of Ramat Hasharon IL for microsoft technology licensing, llc, Anatoly TSVETOV of Kfar Yona IL for microsoft technology licensing, llc
IPC Code(s): G06F7/485, G06F7/487
CPC Code(s): G06F7/485
Abstract: artificial intelligence (ai) operation is improved by combining pre-processing with quantization and post-processing with dequantization. floating point conversion may be implemented as fixed point to fixed point conversion. floating point conversion and precision may be mimicked, for example, using high precision parameters in a fixed point to fixed point conversion. mimicking floating point using hardware acceleration reduces sequential operations, such as machine learning model preprocessing and quantization by a cpu, to one or two clock cycles in a single step operation. accordingly, computing resources, such as computing device cameras, may provide raw data to a hardware accelerator configured to quickly render the input in the correct format to an inference model by simultaneously performing preprocessing and quantization, substantially reducing inference latency and device power consumption while freeing up a cpu for other tasks.
Inventor(s): SHENGYU FU of REDMOND WA US for microsoft technology licensing, llc., NEELAKANTAN SUNDARESAN of BELLEVUE WA US for microsoft technology licensing, llc., ALEXEY SVYATKOVSKIY of BELLEVUE WA US for microsoft technology licensing, llc., SHUO ZHANG of ISSAQUAH WA US for microsoft technology licensing, llc.
IPC Code(s): G06F8/73, G06F8/41, G06F40/30
CPC Code(s): G06F8/73
Abstract: a code review is automatically generated by a large language model given a prompt that includes code changes made to a source code program, an associated intent, and an extended context. the intent represents an issue with the code changes from a code reviewer's perspective and is predicted from a neural classifier given the code changes in a code diff format. the neural classifier is a neural encoder transformer model pre-trained on various code review datasets and fine-tuned on code diff hunks of code changes labeled with an intent.
Inventor(s): Ishwar AGARWAL of Redmond WA US for microsoft technology licensing, llc, George CHRYSOS of Portland OR US for microsoft technology licensing, llc, Oscar ROSELL MARTINEZ of Barcelona ES for microsoft technology licensing, llc, Yevgeniy BAK of Redmond WA US for microsoft technology licensing, llc
IPC Code(s): G06F9/38, G06F9/445, G06F9/50, G06F12/0815
CPC Code(s): G06F9/3816
Abstract: systems and methods related to direct swap caching with zero line optimizations are described. a method for managing a system having a near memory and a far memory comprises receiving a request from a requestor to read a block of data that is either stored in the near memory or the far memory. the method includes analyzing a metadata portion associated with the block of data, the metadata portion comprising: both (1) information concerning whether the near memory contains the block of data or whether the far memory contains the block of data and (2) information concerning whether a data portion associated with the block of data is all zeros. the method further includes instead of retrieving the data portion from the far memory, synthesizing the data portion corresponding to the block of data to generate a synthesized data portion and transmitting the synthesized data portion to the requestor.
Inventor(s): Steven Rhejohn Barlin SO of Burlington MA US for microsoft technology licensing, llc, Philip TO of Burlington MA US for microsoft technology licensing, llc, Tasneem NASEEM of Burlington MA US for microsoft technology licensing, llc
IPC Code(s): G06F9/455
CPC Code(s): G06F9/45558
Abstract: computing and network capacity are allocated in a computing environment provided by a virtualized computing service provider. an ai-based optimization model is run to quantify current network traffic in the computing environment based on processing and storage usage patterns using key performance indicators (kpis). the quantified current network traffic is used to calculate, by a sizing and capacity model of the ai-based optimization model, a number, types, and sizes of disk storage and processing resources based on estimated cost.
Inventor(s): Pushkar Vijay CHITNIS of Bothell WA US for microsoft technology licensing, llc, Ahmad ABDULLATEEF of Duvall WA US for microsoft technology licensing, llc, David Kimler ALTOBELLI of Redmond WA US for microsoft technology licensing, llc, Sergio Shingchow WONG of Redmond WA US for microsoft technology licensing, llc
IPC Code(s): G06F9/455
CPC Code(s): G06F9/45558
Abstract: systems, methods, devices, and computer readable storage media described herein provide techniques utilizing key hierarchies for virtual trusted platform modules (vtpms). in an aspect, a system comprises a vtpm. the vtpm receives a first seed value representative of a system feature of the system. the vtpm generates the first key from the first seed value, the first key configured to unseal a sealed state of an operating system of a virtual machine. the vtpm utilizes the first key to unseal the sealed state. the vtpm provides the unsealed state to an instance of the virtual machine to cause the instance to boot the operating system based on the unsealed state. in a further aspect, the vtpm receives, from an application executed by the instance, a request to perform a cryptographic operation to modify an object utilizing the first key.
Inventor(s): Igor BRAGAIA of Redmond WA US for microsoft technology licensing, llc, Judenique Brierna AUGUSTE of Redmond WA US for microsoft technology licensing, llc, Alycia Yohsuan Habo LEE of Redmond WA US for microsoft technology licensing, llc, Laurentiu Titi NEDELCU of Redmond WA US for microsoft technology licensing, llc
IPC Code(s): G06F11/36
CPC Code(s): G06F11/3684
Abstract: the techniques disclosed herein enhance software testing through automated test case generation and implementation. this is accomplished through utilizing a natural language interface and a generative probabilistic model, such as a large language model. a first user input can provide descriptions of a software feature to be tested and various use cases for the software feature. accordingly, the probabilistic model generates a plurality of test cases for evaluating various aspects of the software feature in accordance with the first user input. the natural language descriptions can be accompanied by a contextual input to constrain the probabilistic model and ensure consistent outputs. subsequently, a second user input selects a test case from the generated test cases to be implemented. in response, the probabilistic model generates a software implementation of the selected test case. the software implementation is then executed with log data extracted from the testing environment for processing results.
Inventor(s): Tarique Ashraf SIDDIQUI of Redmond WA US for microsoft technology licensing, llc, Vivek Ravindranath NARASAYYA of Redmond WA US for microsoft technology licensing, llc, Marius DUMITRU of Issaquah WA US for microsoft technology licensing, llc, Surajit CHAUDHURI of Kirkland WA US for microsoft technology licensing, llc
IPC Code(s): G06F16/242, G06F16/2455
CPC Code(s): G06F16/244
Abstract: a data processing system implements a cache-conscious aggregation framework for cache-efficient top-k aggregation over high cardinality large datasets. the framework leverages skew in the distribution of data in the datasets to minimize data movements within the local caches of the cores of the multicore processors of the data processing system. the framework performs representative sampling on the dataset and utilizes these samples to identify candidate groups in the dataset for the top-k results. the system performs exact aggregations for the candidate groups and performs hashing and pruning on the non-candidate groups in the dataset to identify top-k results included in the non-candidate groups without having to calculate the exact aggregations for the non-candidate groups.
Inventor(s): Nimit Dipakkumar SHAH of Redmond WA US for microsoft technology licensing, llc, Muthukumaran ARUMUGAM of Redmond WA US for microsoft technology licensing, llc, Yogesh KUMAR of Redmond WA US for microsoft technology licensing, llc, Hari Sudan SUNDAR of Redmond WA US for microsoft technology licensing, llc, Ankur JAUHARI of San Francisco CA US for microsoft technology licensing, llc, Per Mikael HORAL of Renton WA US for microsoft technology licensing, llc, Subramanyam PATTIPAKA of Bellevue WA US for microsoft technology licensing, llc, Venkata Sivaramakrishna RAMADUGU of Redmond WA US for microsoft technology licensing, llc
IPC Code(s): G06F16/27
CPC Code(s): G06F16/275
Abstract: a data processing system implements techniques for operating and recovering databases that includes a plurality of replicas. the plurality of replicas includes a primary replica, a secondary replica, and a delta replica. the primary replica handles read and write operations, the secondary replica is a backup of the primary replica capable of read-only operations. the delta replica comprising a copy of the transactional log information of the primary replica but not the data. the system detects a failure of the primary and secondary replicas. the system instantiates a recovery workflow using the delta replica that includes initializing a second secondary replica; causing the second secondary replica to download backup data that represents a state of the primary replica prior to failure of the primary replica; updating the secondary replica using the backup data; and promoting the secondary replica to the primary replica.
Inventor(s): Gungor Polatkan of San Jose CA US for microsoft technology licensing, llc, Jun Liu of Bellevue WA US for microsoft technology licensing, llc, Diane Lee of Sunnyvale CA US for microsoft technology licensing, llc, Rui Ma of New York NY US for microsoft technology licensing, llc, Yanen Li of Savoy IL US for microsoft technology licensing, llc, Di Zhou of Newark CA US for microsoft technology licensing, llc, Zhuliu Li of San Jose CA US for microsoft technology licensing, llc, Necip Fazil Ayan of Menlo Park CA US for microsoft technology licensing, llc
IPC Code(s): G06F16/332, G06F16/335, G06F16/383, G06N7/01
CPC Code(s): G06F16/332
Abstract: embodiments of the disclosed technologies obtain evidence from at least one electronic data source. the evidence can include unstructured data associated with an entity. a set of entity features is extracted from the evidence. the set of entity features includes at least one digital description of expertise associated with the entity. the set of entity features, including the at least one digital description of expertise, is encoded, in digital form, into an entity feature embedding. at least one entity expertise embedding is extracted from the entity feature embedding. the at least one entity expertise embedding encodes an entity domain and a level of expertise of the entity in the entity domain. the at least one entity expertise embedding can be stored in digital form as an entity embedding and/or provided to at least one downstream process, model, component, network, and/or system.
Inventor(s): Lu LI of Kirkland WA US for microsoft technology licensing, llc, Huangxing LI of Bellevue WA US for microsoft technology licensing, llc, Li HUANG of Sammamish WA US for microsoft technology licensing, llc
IPC Code(s): G06F16/9538, G06F16/951
CPC Code(s): G06F16/9538
Abstract: aspects of the disclosure include methods and systems for dynamically creating tagged search results. an exemplary method includes identifying, responsive to receiving a search query, search results. the search results include a plurality of web page links to respective web pages. the method includes identifying, for each web page, one or more page features, generating a plurality of descriptive tags for the respective web pages, generating one or more page-tag pairs by pairing a web page of the respective web pages with a descriptive tag of the plurality of descriptive tags, and determining a relevance score for each page-tag pair. one or more descriptive tags having a relevance score above a predetermined threshold are selected. tagged search results are provided that include the plurality of web page links and, for each web page link, the selected one or more descriptive tags for the respective web page.
Inventor(s): Chao MA of Cambridge GB for microsoft technology licensing, llc, Cheng ZHANG of Cambridge GB for microsoft technology licensing, llc, Colleen TYLER of Redmond WA US for microsoft technology licensing, llc, Marife DEFANTE of Woodinville WA US for microsoft technology licensing, llc, Lisa Lynne PARKS of Redmond WA US for microsoft technology licensing, llc, Agrin Aram HILMKIL of Stockholm SE for microsoft technology licensing, llc, Nick PAWLOWSKI of Cambridge GB for microsoft technology licensing, llc
IPC Code(s): G06F17/18, G06F17/16
CPC Code(s): G06F17/18
Abstract: a computer-implemented method is provided. a dataset comprising a covariate matrix, a treatment vector, and an outcome vector is received. using a trained causal model applied to the dataset, an inverse probability weighted (ipw) model estimation of treatment effect is generated. an inverse probability weighted estimation of groundtruth treatment effect is computed. a causal treatment error is calculated based on the inverse probability weighted model estimation of treatment effect and the inverse probability weighted estimation of groundtruth treatment effect.
Inventor(s): Robert Lee MCCANN of Snoqualmie WA US for microsoft technology licensing, llc, Scott Alexander FREITAS of Phoenix AZ US for microsoft technology licensing, llc, Jovan KALAJDJIESKI of Vancouver CA for microsoft technology licensing, llc, Amirhossein GHARIB of Toronto CA for microsoft technology licensing, llc
IPC Code(s): G06F21/57, G06F16/28, G06F40/40
CPC Code(s): G06F21/577
Abstract: a computer-implemented method comprising: receiving a first input associated with a first entity at a first level of a hierarchy; receiving a second input, associated with a second entity at a second level of the hierarchy, the second entity linked to the first entity within the hierarchy; generating a first low-dimensional feature representation based on the first input, the first low-dimensional feature representation representing the first entity; and generating a second low-dimensional feature representation based on the first input, the second input and the first low-dimensional feature representation, the second low-dimensional feature representation representing the second entity.
Inventor(s): David Alan HILL of Seattle WA US for microsoft technology licensing, llc, Jennifer Marie BOERTLEIN of McLean VA US for microsoft technology licensing, llc
IPC Code(s): G06F21/62, G06F21/31
CPC Code(s): G06F21/6209
Abstract: hybrid access control management systems for managing role-based access control resources and attribute-based access control resources are provided. one aspect provides a computing system for implementing hybrid access control management, the computing system comprising: processing circuitry coupled to memory that stores instructions, which, upon execution by the processing circuitry, cause the processing circuitry to: receive a request from a user account to access an access-controlled resource; determine a protection mechanism of the access-controlled resource, wherein the protection mechanism is an attribute-based protection mechanism or a role-based protection mechanism; validate the request from the user account based on the determination of the protection mechanism; and permit the user account to access the access-controlled resource upon successful validation of the request.
Inventor(s): Xiang MA of Portland OR US for microsoft technology licensing, llc, Aanchal GUPTA of Hillsboro OR US for microsoft technology licensing, llc
IPC Code(s): G06F30/3308, G06F30/327, G06F111/02
CPC Code(s): G06F30/3308
Abstract: embodiments of the present disclosure include techniques for simulating circuits, such as systems-on-a-chip (soc). in various embodiments, a circuit being simulated may comprise one or more processors. however, simulation of the circuit using a hardware description language (hdl) may use a virtualized processor to execute firmware instructions rather than simulating the processor using hdl. certain instructions from the virtualized processor may be sent, during simulation, to the hdl simulation for execution.
Inventor(s): Ahmed Elgohary GHONEIM of Redmond WA US for microsoft technology licensing, llc, Pengcheng HE of Sammamish WA US for microsoft technology licensing, llc
IPC Code(s): G06F40/194, G06F40/289, G06F40/35
CPC Code(s): G06F40/194
Abstract: a technique determines whether a target item is adequately supported by a source item, and therefore likely free of hallucinations. the technique operates by progressively expanding a scope of source content to be considered when determining whether an individual target part of the target item has support in the source item. for instance, the technique initially determines whether any individual source part in the source item supports the target part. if this stage fails to identify support, the technique next considers whether a larger portion of the source item supports the particular target item. in some implementations, the technique selects a scope of analysis at a particular stage by choosing a group of source parts that most closely match the target part under consideration. the technique concatenates those source parts in the same order in which they appear in the source item.
Inventor(s): Chi CHEN of Renton WA US for microsoft technology licensing, llc, Alexander Solomon CHOCRON of Seattle WA US for microsoft technology licensing, llc, Yousif Waleed ALMULLA of Bellevue WA US for microsoft technology licensing, llc
IPC Code(s): G06N3/0455, G06N3/0475
CPC Code(s): G06N3/0455
Abstract: examples are disclosed that relate to passing complex data objects as context variables between iterative prompts to ai models. one example provides a method, comprising receiving an input, constructing a prompt based upon the input, and inputting the prompt into an orchestrator. the method further comprises, based on the prompt, forming a context variable to reference complex data. the method further comprises providing modified input to a first ai plugin at a first orchestration stage, receiving the complex data from the first ai plugin, and storing the complex data as the context variable. the method further comprises modifying the prompt to include a reference to the context variable without modifying the prompt to include the complex data, thereby forming a modified prompt. the method further comprises providing the modified prompt to a generative model, receiving generated text from the generative model, and outputting the generated text with the complex data.
Inventor(s): Marcelo GENNARI DO NASCIMENTO of Cambridge GB for microsoft technology licensing, llc, James John HENSMAN of Cambridge GB for microsoft technology licensing, llc, Saleh ASHKBOOS of Cambridge GB for microsoft technology licensing, llc, Maximilian Louis CROCI of Cambridge GB for microsoft technology licensing, llc
IPC Code(s): G06N3/0495, G06N3/082
CPC Code(s): G06N3/0495
Abstract: embodiment herein relate to a neural network compression technique, in which a weight matrix within the neural network is transformed via matrix multiplication with an orthogonal matrix. the orthogonal matrix is derived from a calibration dataset (which is generally chosen to be broadly representative of expected runtime input data), and the transformation is such that a resulting modified weight matrix has components ordered by relative significance. the modified weight matrix is incorporated in a compressed neural network with fewer weights. by removing one or more components of lower significance, the size of the neural network (and, therefore, its storage and execution overhead) are reduced, whilst still maintaining an acceptable level of performance.
Inventor(s): Ramesh Kamasamodram of Sammamish WA US for microsoft technology licensing, llc, Gayathri Chandrasekaran of Seattle WA US for microsoft technology licensing, llc, Jyoti Mathur of Bothell WA US for microsoft technology licensing, llc, Jason Eric Voldseth of Campbell CA US for microsoft technology licensing, llc, Ragavenderan Venkatesan of Mill Creek WA US for microsoft technology licensing, llc
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: the technology described herein uses local computing resources, rather than remote resources (e.g., server based), to provide a result upon determining that the local resource is capable of providing the result with above a threshold quality. when a local machine-learning model is not capable of providing the result with above the threshold quality, then a remote machine-learning model may be used to provide the result. a goal of the technology is to select the most efficient resource to provide a result without significantly compromising the quality of the result. the technology described herein makes a series of determinations to identify one or more machine-learning model results that may be provided locally with or without hybrid resources. different machine-learning model results may be provided using different hybrid workflows. in aspects, a remote result and a local result are generated and ranked by the client.
Inventor(s): Qintian ZHANG of Seattle WA US for microsoft technology licensing, llc, Choo Yei CHONG of Redmond WA US for microsoft technology licensing, llc, Catherine Kursch BOEGER of Kirkland WA US for microsoft technology licensing, llc, Matthew Bradley ONTELL of Richmond VA US for microsoft technology licensing, llc
IPC Code(s): G06Q10/0639
CPC Code(s): G06Q10/06395
Abstract: a system and method for analyzing connections between a first enterprise and a second enterprise includes retrieving connection data related to connections between the first enterprise and the second enterprise from a plurality of data sources and utilizing a connection graph generating engine to generate a connection graph for the connections between the first enterprise and the second enterprise, where the connection graph is generated based on the retrieved connection data. the connection graph is provided as an input to a trained machine-learning (ml) model to identify connection patterns in the connections between the first enterprise and the second enterprise and recommendations are generated using a second trained ml model based on the identified connection patterns and based on at least one of a context of the first enterprise or the second enterprise. the recommendations are provided for display to a user.
Inventor(s): Yanwei WANG of Bellevue WA US for microsoft technology licensing, llc, Pascal PARE of Bothell WA US for microsoft technology licensing, llc, Christopher Douglas EDMONDS of Carnation WA US for microsoft technology licensing, llc, Mark Anthony PLAGGE of Sammamish WA US for microsoft technology licensing, llc, Miguel SUSFFALICH of Kirkland WA US for microsoft technology licensing, llc, Anuj Bharat GOSALIA of Sammamish WA US for microsoft technology licensing, llc
IPC Code(s): G06T5/00, G06T5/10, G06T7/11, G06T17/00, G06V10/26
CPC Code(s): G06T5/80
Abstract: digital image processing methods performed by a computer are disclosed. in one example, a digital image captured by a real camera having intrinsic and extrinsic parameters is received. one or more distortion correction transformations are applied to the digital image to generate a distortion-corrected digital image. the distortion-corrected digital image is sub-divided into a plurality of distortion-corrected sub-images. for each distortion-corrected sub-image of the plurality of distortion-corrected sub-images, the distortion-corrected sub-image is associated with a synthesized recapture camera having synthesized intrinsic and extrinsic parameters mapped from the intrinsic and extrinsic parameters of the real camera.
Inventor(s): Yanwei WANG of Bellevue WA US for microsoft technology licensing, llc, Pascal PARE of Bothell WA US for microsoft technology licensing, llc, Christopher Douglas EDMONDS of Carnation WA US for microsoft technology licensing, llc, Mark Anthony PLAGGE of Sammamish WA US for microsoft technology licensing, llc, Miguel SUSFFALICH of Kirkland WA US for microsoft technology licensing, llc, Anuj Bharat GOSALIA of Sammamish WA US for microsoft technology licensing, llc
IPC Code(s): G06T7/11, G06T7/80, G06T17/00, G06V10/25, G06V10/44
CPC Code(s): G06T7/11
Abstract: digital image processing methods performed by a computer are disclosed. in one example, a first digital image captured by a real camera is sub-divided into a first plurality of sub-images. a second digital image captured by a real camera is sub-divided into a second plurality of sub-images. a set of image features in a first sub-image of the first plurality of sub-images is identified. a subset of neighboring sub-images is identified from the second plurality of sub-images based at least on each neighboring sub-image of the subset of neighboring sub-images having one or more corresponding image features in common with the set of image features identified in the first sub-image.
Inventor(s): Mrinal Kumar SHARMA of Noida IN for microsoft technology licensing, llc
IPC Code(s): G10L13/02, G10L15/22
CPC Code(s): G10L13/02
Abstract: a system and method and for providing speech assistance during a virtual meeting includes receiving a request over a communication network to provide speech assistance during a virtual meeting between a plurality of participants and analyzing speech data of the virtual meeting, via a speech impediment detection engine, to detect a speech impediment for one of the plurality of participants. upon detecting the speech impediment, an avatar is automatically generated for the participant experiencing speech impediment and the avatar is synchronized with the participant's speech in real-time during the communication session to provide real-time visual feedback to the participant. the avatar is then provided for display to the participant.
Inventor(s): Chi CHEN of Renton WA US for microsoft technology licensing, llc, Nathan Andrew BAKER of Kennewick WA US for microsoft technology licensing, llc, Brian Allen BILODEAU of Redmond WA US for microsoft technology licensing, llc, Matthias TROYER of Zurich CH for microsoft technology licensing, llc, Craig Daniel OWEN of Sammamish WA US for microsoft technology licensing, llc, Linda Shieh-You LAUW of Bellevue WA US for microsoft technology licensing, llc
IPC Code(s): H01M10/0562, H01M10/0525
CPC Code(s): H01M10/0562
Abstract: disclosed herein is the compound naliycl(0<x<3), which is usable as an effective solid-state battery electrolyte. the disclosed compound can have a trigonal ordered crystal structure. the disclosed compound can exhibit characteristics beneficial for solid-state battery electrolyte applications.
Inventor(s): Chi CHEN of Renton WA US for microsoft technology licensing, llc, Nathan Andrew BAKER of Kennewick WA US for microsoft technology licensing, llc, Brian Allen BILODEAU of Redmond WA US for microsoft technology licensing, llc, Matthias TROYER of Zurich CH for microsoft technology licensing, llc, Craig Daniel OWEN of Sammamish WA US for microsoft technology licensing, llc, Linda Shieh-You LAUW of Bellevue WA US for microsoft technology licensing, llc
IPC Code(s): H01M10/0562, H01M10/0525, H01M10/054
CPC Code(s): H01M10/0562
Abstract: disclosed herein is a solid-state battery comprising an anode, cathode, and solid electrolyte disposed between and in conductive contact with the anode and the cathode. the solid electrolyte comprises the compound naliycl(0<x<3), which can have a trigonal ordered crystal structure. the solid-state battery can be configured as a lithium-ion battery or as a sodium-ion battery.
20250105915. HYBRID 5G NETWORKS_simplified_abstract_(microsoft technology licensing, llc)
Inventor(s): Michael Anthony BROWN of McKinney TX US for microsoft technology licensing, llc, Christian Joseph RODRIGUEZ of Loxahatchee FL US for microsoft technology licensing, llc, Khawaja Sadiq Naveed DANIYAL of Dubai AE for microsoft technology licensing, llc, Bandhan DUTTA of Redmond WA US for microsoft technology licensing, llc, Shimona GORELICK of Redmond WA US for microsoft technology licensing, llc
IPC Code(s): H04B7/185, H04W84/06
CPC Code(s): H04B7/18539
Abstract: a system provides interoperation between fifth generation (5g) non-terrestrial networks (ntn) and 3rd generation partnership project (3gpp) 5g network with a satellite-based communications network. the system includes a satellite access network (san) configured to process satellite digital video broadcast (dbb) waveforms, a satellite access node (san) configured with functionality of a ground station modem and portions of an existing network management system (nms), and a fixed network-residential gateway (fn-rg) that comprises a logical representation of the remote modem.
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): H04L9/3236
Abstract: secure hash-based message authentication code (hmac) deterministic random bit generator (drbg) architectures are provided. a circuit can include hmac drbg circuitry including a counter configured to increment based on a clock state and provide a counter output, and hmac function circuitry coupled to the hmac drbg circuitry, the hmac function circuitry including first and second hashing circuits, the hmac function circuitry configured to implement an hmac function using the first and second hashing circuits and the counter output, the hmac function circuitry configured to split a key into first and second shares based on the counter output and provide the first share to the first hashing circuit and the second share to the second hashing circuit.
Inventor(s): John P. FALCONE of Kirkland WA US for microsoft technology licensing, llc, Daniel J. Oliver of Seattle WA US for microsoft technology licensing, llc, Michael J. NOVAK of Redmond WA US for microsoft technology licensing, llc
IPC Code(s): H04L9/32
CPC Code(s): H04L9/3236
Abstract: systems and methods for configuration verification using variable inputs and hash functions are disclosed. a computing device may receive, via an operating system user interface, configuration data associated with a configuration setting of the computing device. in response to receiving the configuration data, the computing device may store the configuration data and obtain, based on a hash configuration, a set of inputs and a hash function. the computing device may hash, using the hash function, the set of inputs in an input order indicated by the hash configuration to generate a first verification code based on the configuration data. the computing device may subsequently verify stored configuration data by generating a second verification code based on the stored configuration data and comparing the second verification code to the first verification code.
Inventor(s): Pritesh Rajesh KANANI of Bellevue WA US for microsoft technology licensing, llc, Mantian Helen Lin of Seattle WA US for microsoft technology licensing, llc, Siunie Aquawati Sutjahjo of Redmond WA US for microsoft technology licensing, llc, Wei Zhong of Redmond WA US for microsoft technology licensing, llc
IPC Code(s): H04L43/045, G06T17/00
CPC Code(s): H04L43/045
Abstract: disclosed in some examples are methods, systems, and machine-readable mediums that that utilize targeted telemetry in a highly complex ui such as a 3d ui using dynamic object references, subscription-based retrieval, and hierarchical object model to provide advanced telemetry streams. these streams may be used in accurately diagnosing issues in real-time for highly complex ui environments, such as 3d communication systems. while a 3dcs is used herein as an example of an environment to which the present disclosure is applicable, one of ordinary skill in the art with the benefit of the present disclosure will appreciate that the techniques described here may be applied to other highly complex and dynamic environments as well.
Inventor(s): David Alan HILL of Seattle WA US for microsoft technology licensing, llc
IPC Code(s): H04L9/40
CPC Code(s): H04L63/20
Abstract: implementations of an access bridge to facilitate migration from a role-based access control management system to an attribute-based access control management system are provided. one aspect provides a computing system comprising processing circuitry coupled to memory that stores instructions, which, upon execution by the processing circuitry, cause the processing circuitry to: identify a role-based protected resource; identify a set of user accounts permitted to access the role-based protected resource; query a set of attributes associated with the identified set of user accounts; coalesce the set of attributes to determine instances of each attribute among the identified set of user accounts; generate a set of candidate attribute-based access control policies using the coalesced set of attributes; implement an attribute-based access control policy from the set of candidate attribute-based access control policies; and convert the role-based protected resource into an attribute-based protected resource associated with the implemented attribute-based access control policy.
Inventor(s): Christopher M. SANO of Seattle WA US for microsoft technology licensing, llc, Emily Rachel LEDERMAN of Bellevue WA US for microsoft technology licensing, llc
IPC Code(s): H04N5/262, G06V40/20, G10L17/02, G10L17/06, G10L25/57, H04N5/278
CPC Code(s): H04N5/2628
Abstract: the techniques disclosed herein provide equitable presence of video conference attendees. a system gives attribution to a primary attendee in response to the detection of active speaking activities of users who are delegated as assistants for the primary attendee. attribution can include at least one of (1) highlighting a video of the primary attendee, (2) positioning a video of the primary attendee in a primary region of a user interface, or (3) associating the primary attendee to transcript text or close captioning text that is generated from the speaking activities of the delegated assistants. by providing attribution to a primary attendee, instead of providing attribution to an assistant who is speaking on behalf of the primary attendee, a system can provide equitable meeting presence for those who are truly making substantive contributions to a video conference.
Inventor(s): Kathryn M. OSEEN-SENDA of Seattle WA US for microsoft technology licensing, llc, Dennis TRIEU of Calgary CA for microsoft technology licensing, llc, Oscar FARIAS MOGUEL of Seattle WA US for microsoft technology licensing, llc, Martha Geoghegan PETERSON of Woodinville WA US for microsoft technology licensing, llc, Alexis Grace SCHUBERT of Seattle WA US for microsoft technology licensing, llc
IPC Code(s): H05K7/20, G06F1/20
CPC Code(s): H05K7/2039
Abstract: a device may include a heat sink body defining a fluid reservoir. a device may include a working fluid in the fluid reservoir. a device may include a movable contact surface configured to transfer heat from a heat-generating component to the working fluid, wherein at least a portion of the movable contact surface is movable relative to the heat sink body.
Microsoft Technology Licensing, LLC patent applications on March 27th, 2025
- Microsoft Technology Licensing, LLC
- G06F3/041
- G06F3/0354
- CPC G06F3/04144
- Microsoft technology licensing, llc
- G06F7/485
- G06F7/487
- CPC G06F7/485
- G06F8/73
- G06F8/41
- G06F40/30
- CPC G06F8/73
- Microsoft technology licensing, llc.
- G06F9/38
- G06F9/445
- G06F9/50
- G06F12/0815
- CPC G06F9/3816
- G06F9/455
- CPC G06F9/45558
- G06F11/36
- CPC G06F11/3684
- G06F16/242
- G06F16/2455
- CPC G06F16/244
- G06F16/27
- CPC G06F16/275
- G06F16/332
- G06F16/335
- G06F16/383
- G06N7/01
- CPC G06F16/332
- G06F16/9538
- G06F16/951
- CPC G06F16/9538
- G06F17/18
- G06F17/16
- CPC G06F17/18
- G06F21/57
- G06F16/28
- G06F40/40
- CPC G06F21/577
- G06F21/62
- G06F21/31
- CPC G06F21/6209
- G06F30/3308
- G06F30/327
- G06F111/02
- CPC G06F30/3308
- G06F40/194
- G06F40/289
- G06F40/35
- CPC G06F40/194
- G06N3/0455
- G06N3/0475
- CPC G06N3/0455
- G06N3/0495
- G06N3/082
- CPC G06N3/0495
- G06N20/00
- CPC G06N20/00
- G06Q10/0639
- CPC G06Q10/06395
- G06T5/00
- G06T5/10
- G06T7/11
- G06T17/00
- G06V10/26
- CPC G06T5/80
- G06T7/80
- G06V10/25
- G06V10/44
- CPC G06T7/11
- G10L13/02
- G10L15/22
- CPC G10L13/02
- H01M10/0562
- H01M10/0525
- CPC H01M10/0562
- H01M10/054
- H04B7/185
- H04W84/06
- CPC H04B7/18539
- H04L9/32
- CPC H04L9/3236
- H04L43/045
- CPC H04L43/045
- H04L9/40
- CPC H04L63/20
- H04N5/262
- G06V40/20
- G10L17/02
- G10L17/06
- G10L25/57
- H04N5/278
- CPC H04N5/2628
- H05K7/20
- G06F1/20
- CPC H05K7/2039
