Microsoft Technology Licensing, LLC patent applications on March 13th, 2025
Patent Applications by Microsoft Technology Licensing, LLC on March 13th, 2025
Microsoft Technology Licensing, LLC: 31 patent applications
Microsoft Technology Licensing, LLC has applied for patents in the areas of H04L9/40 (5), G06N20/00 (2), G06F9/50 (2), G06F16/2455 (2), G06F40/30 (2) H04L63/20 (2), G01R31/2896 (1), G06F40/42 (1), H04L63/1425 (1), H04L63/1416 (1)
With keywords such as: user, security, data, based, language, time, ranking, service, text, and generate in patent application abstracts.
Patent Applications by Microsoft Technology Licensing, LLC
Inventor(s): Terrence Huat Hin TAN of Bayan Lepas (MY) for microsoft technology licensing, llc, Charles Walter BOECKER of Ames IA (US) for microsoft technology licensing, llc, Ravi SHIVNARAINE of Pickering (CA) for microsoft technology licensing, llc, Edwin Magtoto GOZUN of Folsom CA (US) for microsoft technology licensing, llc, Sokratis VAMVAKOS of Sunnyvale CA (US) for microsoft technology licensing, llc
IPC Code(s): G01R31/28, G01R31/66
CPC Code(s): G01R31/2896
Abstract: systems and methods for isolating faults in die-to-die interconnects are provided. a method includes providing a first transmission path, along a die-to-die interconnect, from a transmitter associated with a first die to an asynchronous buffer associated with a second die. the method further includes providing a second transmission path from voltage reference circuitry associated with the second die to the asynchronous buffer associated with the second die. the method further includes simultaneously enabling both the first transmission path and the second transmission path to allow the asynchronous buffer to receive inputs from both the transmitter associated with the first die and the voltage reference circuitry associated with the second die, such that the inputs received by the asynchronous buffer are indicative of: (1) no failure in the die-to-die interconnect, (2) an open failure in the die-to-die interconnect, or (3) a short failure in the die-to-die interconnect.
Inventor(s): Anton Viljami AUTERE of Jarvenpaa (FI) for microsoft technology licensing, llc, Simo Kaarlo Tapani TAMMELA of Espoo (FI) for microsoft technology licensing, llc, Esa Tapani RAIKKONEN of Espoo (FI) for microsoft technology licensing, llc
IPC Code(s): G02B21/14
CPC Code(s): G02B21/14
Abstract: a microscope system includes a laser light source to emit illumination light toward an observation sample. a plurality of optical fibers are disposed along an optical path between the laser light source and the observation sample, such that each optical fiber of the plurality of optical fibers propagates illumination light toward the observation sample. each optical fiber of the plurality of optical fibers is longer than a coherence length of the illumination light. a first optical fiber of the plurality of optical fibers has a first length that differs from a second length of a second optical fiber by at least the coherence length.
Inventor(s): Keenan MAY of Bothell WA (US) for microsoft technology licensing, llc
IPC Code(s): G02B27/01, G02B27/00, G06T19/00
CPC Code(s): G02B27/0172
Abstract: undesirable light leakage is reduced in a mixed-reality head-mounted display device with a selective color see-through visor by alternatively reflecting or absorbing forward-propagating virtual image light by matching color selection for virtual images generated by a display engine to characteristics of an optical filter that is disposed downstream of an output coupler used in a waveguide combiner. the optical filter is alternatively configured as a static spectral-sensitive filter such as a notch reflector or as a dynamically variable spectral-selective filter that selectively absorbs particular wavelengths of virtual image light responsively to a control voltage signal.
Inventor(s): Assaf BAR-NESS of Ness Zionna (IL) for microsoft technology licensing, llc, Shoham DEKEL of Tel Aviv (IL) for microsoft technology licensing, llc
IPC Code(s): G06F3/0354, G06F3/01, G06F3/041, G06F3/04883
CPC Code(s): G06F3/03545
Abstract: a method for a touch-sensitive display device includes detecting a first real-world movement of a stylus from a first surface position to a second surface position separated from the first surface position by a first real-world movement distance. the first real-world movement is translated into a first virtual movement from a first virtual input position to a second virtual input position using an absolute movement translation. based on one or more movement translation context parameters, movement translation is switched from the absolute movement translation to a relative movement translation. a second real-world movement of the stylus is detected from the second surface position to a third surface position separated from the second surface position by a second real-world movement distance. the second real-world movement is translated into a corresponding second virtual movement from the second virtual input position to a third virtual input position using the relative movement translation.
Inventor(s): Ahia PERETZ of Elkana (IL) for microsoft technology licensing, llc, On HARAN of Kfar Saba (IL) for microsoft technology licensing, llc, Lior ZAGIEL of Tel Aviv (IL) for microsoft technology licensing, llc
IPC Code(s): G06F3/041
CPC Code(s): G06F3/0418
Abstract: methods and computing devices for estimating a force f exerted on a touchpad are disclosed. in one example, a method comprises determining that the touchpad is not being touched. at least on condition of determining that the touchpad is not being touched, a no-touch capacitance value of the pcb is calculated. after calculating the no-touch capacitance value, the method includes determining that the touchpad is being touched. at least on condition that the touchpad is being touched, the no-touch capacitance value and a touch-based capacitance value are used to estimate the force f exerted on the touchpad.
Inventor(s): Saket SUMAN of Bellevue WA (US) for microsoft technology licensing, llc, Gangadhara Swamy SHIVAGANGA NAGARAJU of Kirkland WA (US) for microsoft technology licensing, llc, Simran PARKHE of Seattle WA (US) for microsoft technology licensing, llc, Pushkar V. CHITNIS of Bothell WA (US) for microsoft technology licensing, llc, Vikas BHATIA of Kirkland WA (US) for microsoft technology licensing, llc, Alec Stephen FERNANDEZ of Durham NC (US) for microsoft technology licensing, llc
IPC Code(s): G06F9/455
CPC Code(s): G06F9/45558
Abstract: example solutions for performing attestation for a confidential virtual machine (cvm) provision a confidential virtual machine within a virtualization platform. the virtualization platform includes confidential computing hardware configured to support encryption services to data while that data is in use on the cvm. a third party with administrative rights is provided to the cvm. the administrative rights allow the third party to modify a configuration of the cvm. after the administrative rights of the third party are removed from the cvm, a measurement is received from the cvm. the measurement is added to a build attestation report for the cvm. the attestation report is transmitted to a primary administrative party of the cvm. using the confidential computing hardware, the cvm enters operational service with confidential data upon receiving certification user input from the primary administrative party who has reviewed the attestation report.
Inventor(s): Olga POPPE of Issaquah WA (US) for microsoft technology licensing, llc, Qun GUO of Bellevue WA (US) for microsoft technology licensing, llc, Willis LANG of Edina MN (US) for microsoft technology licensing, llc, Pankaj ARORA of Sammamish WA (US) for microsoft technology licensing, llc, Ajay KALHAN of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F9/50
CPC Code(s): G06F9/5005
Abstract: a proactive resource allocator in a database management system is configured to make database resource allocation decisions for users accessing a database, including proactively pausing resources allocated to a user for accessing a database. to determine whether to proactively pause resources that are allocated to a user who has logged out, the proactive resource allocator accesses historical data to predict a next time the user with log back in. if the predicted next time of user login is relatively soon, the proactive resource allocator maintains the allocation of the resources to the user. if the predicted next time of user login is relatively far away, the proactive resource allocator pauses the resources. the proactive resource allocator may logically pause the resources or may physically pause the resources.
Inventor(s): Ali KANSO of Kirkland WA (US) for c/o microsoft technology licensing, llc, Arijit TARAFDAR of Sammamish WA (US) for c/o microsoft technology licensing, llc, Sumeet KHUSHALANI of Kenmore WA (US) for c/o microsoft technology licensing, llc
IPC Code(s): G06F9/50
CPC Code(s): G06F9/505
Abstract: described are examples for distributing stateful workloads to clusters including obtaining, for a cluster, a previous configuration from a shared database, wherein the previous configuration includes a previous indication of a previous owner of a stateful workload, obtaining, for the cluster and from the shared database, a configuration including an indication of a current owner of the stateful workload. based on determining that the cluster is the current owner, the cluster can continue processing a next state of the stateful workload, which may be based on one or more other considerations.
Inventor(s): Navjot SINGH of Patiala (IN) for microsoft technology licensing, llc
IPC Code(s): G06F11/07
CPC Code(s): G06F11/0766
Abstract: the techniques disclosed herein implement a log collector module that monitors geographically dispersed components in a distributed network for an event that disrupts the normal operation of a service. the distributed network can include a 5g network. in response to determining that the event has occurred, the log collector module triggers the collection of logs related to metrics associated with the event. the logs that are collected include an increased level of verbosity compared to logs that are collected during the normal operation of the service. this enables a cloud service provider to ensure that the root-cause analysis required by service level agreements can be effectively performed. to address the technical challenge imposed by resource constraints, the log collector module only collects the more verbose logs for a predefined time period. upon expiration of the predefined time period, the collection of the more verbose logs is halted to conserve resources.
Inventor(s): Mohit VERMA of Seattle WA (US) for microsoft technology licensing, llc, Julien HOACHUCK of San Francisco CA (US) for microsoft technology licensing, llc, Qingwei LIN of Beijing (CN) for microsoft technology licensing, llc, Pooja RANI of Woodinville WA (US) for microsoft technology licensing, llc, Namrata JAIN of Sammamish WA (US) for microsoft technology licensing, llc, Rakesh NAMINENI of Sammamish WA (US) for microsoft technology licensing, llc, Jimmy WONG of Bellevue WA (US) for microsoft technology licensing, llc, Si QIN of Beijing (CN) for microsoft technology licensing, llc, Yu KANG of Beijing (CN) for microsoft technology licensing, llc, Jeffrey Ding HE of Bellevue WA (US) for microsoft technology licensing, llc, Yingnong DANG of Sammamish WA (US) for microsoft technology licensing, llc, Jian ZHANG of Bellevue WA (US) for microsoft technology licensing, llc, Bo QIAO of Beijing (CN) for microsoft technology licensing, llc, Kamaljit BATH of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F11/07
CPC Code(s): G06F11/079
Abstract: methods and systems for detecting systemwide service issues by using anomaly localization. in an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.
Inventor(s): Olga POPPE of Issaquah WA (US) for microsoft technology licensing, llc, Qun GUO of Bellevue WA (US) for microsoft technology licensing, llc, Willis LANG of Edina MN (US) for microsoft technology licensing, llc, Pankaj ARORA of Sammamish WA (US) for microsoft technology licensing, llc, Ajay KALHAN of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F11/34, G06F9/50
CPC Code(s): G06F11/3423
Abstract: a proactive resource allocator in a database management system is configured to make database resource allocation decisions for users accessing a database, including proactively resuming resources reclaimed from a user accessing a database. to determine whether to proactively resume resources that are reclaimed from a user who has logged out, the proactive resource allocator accesses historical data to predict a time the user will log back in. if the probability of the user logging back in is high, the proactive resource allocator reallocates resources to the user at the predicted time and may predict a next time the user will log back in. the proactive resource allocator may then logically pause the resources or may physically pause the resources prior to the next predicted time.
Inventor(s): Jose Antonio HIJAR MIRANDA of Jalisco (MX) for microsoft technology licensing, llc, Luke Robert SCHOEN of Woodinville WA (US) for microsoft technology licensing, llc, Mitansh Rakesh SHAH of Seattle WA (US) for microsoft technology licensing, llc, Jorge Alejandro VELASCO REYNA of Zapopan (MX) for microsoft technology licensing, llc, Samuel Akwesi YEBOAH of Houston TX (US) for microsoft technology licensing, llc, Sereym BAEK of Marietta GA (US) for microsoft technology licensing, llc, Michael Joseph LAUCELLA of Flushing NY (US) for microsoft technology licensing, llc, Everson Ramon RODRIGUEZ MUNIZ of Bellevue WA (US) for microsoft technology licensing, llc, Ranjodh Singh SANDHU of St. Visalia CA (US) for microsoft technology licensing, llc, Florin LAZAR of Woodinville WA (US) for microsoft technology licensing, llc, Robert Allen LAND of Kirkland WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F11/36, G06N20/00
CPC Code(s): G06F11/3624
Abstract: a machine learning model is trained from characteristics of code changes and characteristics of tests to generate an output indicative of a likely test result of running a corresponding test on a code change. one or more machine learning models may be trained for a specific code repository and based on developer feedback. when a code change is generated by a developer to code in a code repository, a machine learning model is selected based on the repository and characteristics or features of the code change are extracted and input to the machine learning model. the machine learning model generates a model output indicative of the likely test results of running each of a plurality of different tests on the code change. the model output indicates how likely it is that each of the plurality of different tests will fail. based on the model output, a test selection system selects a subset of the plurality of different tests that should be run against the code changes.
Inventor(s): Mohsen FAYYAZ of Berlin (DE) for microsoft technology licensing, llc, Ayyoob IMANIGOOGHARI of Munich (DE) for microsoft technology licensing, llc, Eric Chris Wolfgang SOMMERLADE of Oxford (GB) for microsoft technology licensing, llc
IPC Code(s): G06F16/2455
CPC Code(s): G06F16/24564
Abstract: a technique executes a client machine-trained model (âclient modelâ) on a client device. in operation, the client device submits a description of a task to be performed by the client device to a network-accessible main system. the main system uses a main-system machine-trained model (âmain-system modelâ) to produce a task prompt based on the task description. the client device subsequently uses the task prompt to process queries pertaining to the task. the main-system is trained to increase the accuracy of responses produced by the client model, while reducing the sizes of task prompts produced by the main system. the training process is performed by holding weights of the client model constant.
Inventor(s): Yiwen ZHU of San Francisco CA (US) for microsoft technology licensing, llc, Subramaniam Venkatraman KRISHNAN of Santa Clara CA (US) for microsoft technology licensing, llc, Weihan TANG of Beijing (CN) for microsoft technology licensing, llc, Tengfei HUANG of Beijing (CN) for microsoft technology licensing, llc, Rui FANG of Beijing (CN) for microsoft technology licensing, llc, Rahul Kumar CHALLAPALLI of San Jose CA (US) for microsoft technology licensing, llc, Mo LIU of San Jose CA (US) for microsoft technology licensing, llc, Long TIAN of Beijing (CN) for microsoft technology licensing, llc, Karuna Sagar KRISHNA of Issaquah WA (US) for microsoft technology licensing, llc, Estera Zaneta KOT of Redmond WA (US) for microsoft technology licensing, llc, Xin HE of Beijing (CN) for microsoft technology licensing, llc, Ashit R. GOSALIA of Sammamish WA (US) for microsoft technology licensing, llc, Dario Kikuchi BERNAL of Cambridge MA (US) for microsoft technology licensing, llc, Aditya LAKRA of Boston MA (US) for microsoft technology licensing, llc, Arshdeep SEKHON of Fort Mill SC (US) for microsoft technology licensing, llc, Sule KAHRAMAN of Boston MA (US) for microsoft technology licensing, llc, Carlo Aldo CURINO of Woodinville WA (US) for microsoft technology licensing, llc, Brian Paul KROTH of Madison WI (US) for microsoft technology licensing, llc, Rathijit SEN of Redmond WA (US) for microsoft technology licensing, llc, Andreas Christian MUELLER of Los Gatos CA (US) for microsoft technology licensing, llc, Shaily Jignesh FOZDAR of New York NY (US) for microsoft technology licensing, llc, Dhruv Harendra RELWANI of Bellevue WA (US) for microsoft technology licensing, llc, Xiang LI of Beijing (CN) for microsoft technology licensing, llc, Sergiy MATUSEVYCH of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F16/28, G06F11/34, G06F16/2455
CPC Code(s): G06F16/285
Abstract: systems, methods and computer-readable memory devices are provided for greater efficiency in the configuration of a database cluster for performing a query workload. a database cluster configuration system is provided that includes a database cluster comprising one or more compute resources configured to perform database queries. a query workload comprising a plurality of queries is received. an initial workload-level configuration is applied. for each query of the query workload, a query-level configuration is generated using a query configuration model corresponding to each query in a contextual bayesian optimization with centroid learning while also leveraging the query plan for each executing query for query characterization and including application of virtual operators. query events are collected and used to update the corresponding query configuration model. the workload-level configuration is updated based on the query events and cached for use during a subsequent execution of the workload.
Inventor(s): Saksham GUPTA of Bellevue WA (US) for microsoft technology licensing, llc, Maxim SIGALOV of Seattle WA (US) for microsoft technology licensing, llc, Aliaksei BONDARIONOK of Redmond WA (US) for microsoft technology licensing, llc, Artashes TER-VARDANYAN of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F16/951, G06F16/2457, G06F16/9535
CPC Code(s): G06F16/951
Abstract: aspects of the disclosure include methods and systems for a time and click based updatable static web page ranking. an exemplary method includes identifying a web page at a discovery time, predicting, by a first ranking model, a first ranking of the web page using first features available at the discovery time, and predicting, by a second ranking model, a second ranking of the web page using the first features. an index score is generated from the first ranking and the second ranking. the method includes, after discovery time, predicting, by the first ranking model, an updated first ranking of the web page using the first features, predicting, by the second ranking model, an updated second ranking of the web page using the first features and second features not available at the discovery time, and updating the index score from a combination of the updated first ranking and the updated second ranking.
Inventor(s): Guillaume Didier Jean-Marc Dufour of Petaluma CA (US) for microsoft technology licensing, llc, Yang Chen of Sunnyvale CA (US) for microsoft technology licensing, llc, Lukasz Janusz Karolewski of San Jose CA (US) for microsoft technology licensing, llc
IPC Code(s): G06F16/9535, G06F16/9032, G06F40/30
CPC Code(s): G06F16/9535
Abstract: in an example embodiment, an embedding model is used to generate an embedding of a natural language searching goal specified by a user, the embedding representing user intent of the user. playbooks in a database of playbooks are also run through the embedding model to generate an embedding for each playbook indicative of a meaning of each playbook. a semantic relationship score can then be computed for each combination of the natural language search goal and a playbook, using the embeddings. these semantic relationship scores can then be passed into a ranking machine learning model, along with measured success rates for the playbooks, to generate a ranking of the playbooks. based on this ranking, a set of filters and action corresponding to at least one of the playbooks may then be recommended to the user.
Inventor(s): Longqi YANG of Issaquah WA (US) for microsoft technology licensing, llc, Chirag Shah of Kenmore WA (US) for microsoft technology licensing, llc, Mengting Wan of Bellevue WA (US) for microsoft technology licensing, llc, Jennifer Lynay Neville of West Lafayette IN (US) for microsoft technology licensing, llc, Tara Lynn Safavi of Seattle WA (US) for microsoft technology licensing, llc, Scott Joseph Counts of Seattle WA (US) for microsoft technology licensing, llc, Siddharth Suri of Redmond WA (US) for microsoft technology licensing, llc, Ryen William White of Woodinville WA (US) for microsoft technology licensing, llc, Reid Marlow Andersen of Los Angeles CA (US) for microsoft technology licensing, llc, Georg Ludwig Wilhelm Buscher of San Jose CA (US) for microsoft technology licensing, llc, Sathish Kumar Manivannan of Redmond WA (US) for microsoft technology licensing, llc, Leijie Wang of Seattle WA (US) for microsoft technology licensing, llc, Sarkar Snigdha Sarathi Das of Redmond WA (US) for microsoft technology licensing, llc, Ali Montazeralghaem of Amherst MA (US) for microsoft technology licensing, llc
IPC Code(s): G06F40/35, G06F40/169, G06F40/40, G06N20/00
CPC Code(s): G06F40/35
Abstract: methods, computer systems, computer-storage media, and graphical user interfaces are provided for efficiently generating and using intent taxonomies. in embodiments, training data, including data requests for information, is obtained. thereafter, a model prompt to be input into a large language model is generated. the model prompt includes an instruction to generate an intent taxonomy, an indication of the training data to use for generating the intent taxonomy, and a taxonomy attribute desired to be used as criteria to generate a quality intent taxonomy. an intent taxonomy that includes user intent classes is obtained as output from the large language model. the intent taxonomy is analyzed to determine whether the intent taxonomy is valid. when the intent taxonomy is determined as valid, the intent taxonomy is provided for use in identifying user intent, and when the intent taxonomy is determined as invalid, the intent taxonomy is refined.
Inventor(s): Nan Duan of Beijing (CN) for microsoft technology licensing, llc, Ming Zhou of Beijing (CN) for microsoft technology licensing, llc, Yaobo Liang of Beijing (CN) for microsoft technology licensing, llc
IPC Code(s): G06F40/42, G06F40/30
CPC Code(s): G06F40/42
Abstract: implementations of the present disclosure relate to text generation with a customizable style. in a method, a first natural language is received; the first natural language text is converted, via a text generation model, into a second natural language text that at least partly reflects the meaning of the first natural language text and has a style distinguishable from the first natural language text, the text generation model comprising a modifiable parameter; and in response to receiving a modification to the parameter, the first natural language text is converted, via the text generation model, into a third natural language text that at least partly reflects the meaning of the first natural language text and includes a style distinguishable from both the first natural language text and the second natural language text.
Inventor(s): SĂ©bastien BUBECK of Seattle WA (US) for microsoft technology licensing, llc, Ronen ELDAN of Seattle WA (US) for microsoft technology licensing, llc, Allison DEL GIORNO of Kirkland WA (US) for microsoft technology licensing, llc, Suriya GUNASEKAR of Seattle WA (US) for microsoft technology licensing, llc, Yin Tat LEE of Seattle WA (US) for microsoft technology licensing, llc, Yuanzhi Li of Monroe WA (US) for microsoft technology licensing, llc, Mojan JAVAHERIPI of San Diego CA (US) for microsoft technology licensing, llc
IPC Code(s): G06N3/091, G06N3/0475
CPC Code(s): G06N3/091
Abstract: systems and methods for generating a small language model are provided. in particular, a computing device may obtain a general dataset including a plurality of general data, annotate a subset of the general dataset based on one or more classifier metrics indicative of a quality of the general dataset, train a classifier based on the annotated subset of the general dataset and the one or more classifier metrics, analyze each general data of the general dataset to determine a score for each of the one or more classifier metrics associated with the respective general data using the trained classifier, generate a filtered general dataset by filtering the general dataset based on one or more filters, train the small language model with the filtered general dataset, generate a synthetic dataset for refining the small language model, and train the small language model with the synthetic dataset.
Inventor(s): Michael BLEYER of Seattle WA (US) for microsoft technology licensing, llc, Christian Markus MAEKELAE of Redmond WA (US) for microsoft technology licensing, llc, Christopher Douglas EDMONDS of Carnation WA (US) for microsoft technology licensing, llc
IPC Code(s): G06T5/10, G06T5/50, G06T7/246
CPC Code(s): G06T5/10
Abstract: a system for regulating temporal filtering strength is configurable to: (i) obtain a light level indicator indicating light level associated with a real-world environment; (ii) determine a motion compensation confidence indicator using a current image and a previous image; (iii) determine a filter weight by processing the light level indicator and the motion compensation confidence indicator using a filter strength regulation module; and (iv) generate an output image by using at least the filter weight to filter a current frame with a previous frame.
Inventor(s): Stanislav Kruchinin of Vienna (AT) for microsoft technology licensing, llc, Dushyant Sharma of Mountain House CA (US) for microsoft technology licensing, llc, Rong Gong of Vienna (AT) for microsoft technology licensing, llc
IPC Code(s): G10L21/16, H04S7/00
CPC Code(s): G10L21/16
Abstract: a method, computer program product, and computing system for enhancement of audio signals received from a plurality of microphones. a multichannel audio signal is received from a plurality of microphones and is processed with a short-time discrete cosine transform (stdct) to generate a real-valued spectral representation of the multichannel signal encoding both magnitude and phase information. magnitude- and phase-dependent weights are generated, and an enhanced single-channel signal is produced based upon, at least in part, the spectral representation of the multichannel signal and the magnitude- and phase-dependent weights.
Inventor(s): Dushyant SHARMA of Mountain House CA (US) for microsoft technology licensing, llc, Patrick NAYLOR of Reading (GB) for microsoft technology licensing, llc, Daniel T. JONES of London (GB) for microsoft technology licensing, llc
IPC Code(s): G10L25/78, G10L19/02, G10L25/30
CPC Code(s): G10L25/78
Abstract: there is provided a speech processing system that includes a neural encoder module. a processor that receives an audio signal; and the memory that contains instructions that control said processor to perform operations that process speech. in an implementation, a front end module can include a neural spatial rtf estimator and a neural spatial and residual encoder (nsre) configured accept as inputs a spectral encoded reference channel stream to output neural transfer functions (ntfs). in another implementation, a front end module encodes and outputs a ch1 bitstream; computes a plurality of relative transfer functions (rtfs) for an n-channel signal and outputs an nâ1 rtfs or an rtf codebook ids and computes and processes an nâ1 residual stream; and a back end module comprising a neural encoder module configured to accept the rtfs and output an encoded speech signal comprising an embedding that comprises features extracted from rtfs. there is also provided a speech processing system that includes a relative transfer function estimator module.
Inventor(s): Thomas Torsten DR WINKLER of Cambridge (GB) for microsoft technology licensing, llc, Rokas DREVINSKAS of Cambridge (GB) for microsoft technology licensing, llc, Ariel GOMEZ DIAZ of Cambridge (GB) for microsoft technology licensing, llc, Charles Ernest WHITTAKER of Cambridge (GB) for microsoft technology licensing, llc, Timothy John DEEGAN of Cambridge (GB) for microsoft technology licensing, llc, James Hilton CLEGG of Cambridge (GB) for microsoft technology licensing, llc, Daniel Jonathan Finchley CLETHEROE of Cambridge (GB) for microsoft technology licensing, llc, Hugh David Paul WILLIAMS of Cambridge (GB) for microsoft technology licensing, llc, Austin Nicholas DONNELLY of Cambridge (GB) for microsoft technology licensing, llc, Richard John BLACK of Cambridge (GB) for microsoft technology licensing, llc, Masaaki SAKAKURA of Cambridge (GB) for microsoft technology licensing, llc, Teodora ILIEVA of Cambridge (GB) for microsoft technology licensing, llc, Bridgette Rosanna Doris COOPER of London (GB) for microsoft technology licensing, llc, Ioan Alexandru STEFANOVICI of Cambridge (GB) for microsoft technology licensing, llc, Erika Blancada ARANAS of London (GB) for microsoft technology licensing, llc, Pablo Rafael Andreas Wilke BERENGUER of Berlin (DE) for microsoft technology licensing, llc
IPC Code(s): G11B7/005, G11B7/00, G11B7/126, G11B7/253
CPC Code(s): G11B7/0051
Abstract: a method of writing data to a transparent substrate comprises forming a first voxel by focusing a first laser pulse on a first location in a transparent substrate; and forming a second voxel by focusing a second laser pulse on a second location in the transparent substrate. the first laser pulse and the second laser pulse have different amplitudes, resulting in the first and second voxels having different strengths. also provided are a system useful for implementing the method; an optical data storage medium obtainable by the method; and a method of reading data from the optical data storage medium.
Inventor(s): Sudipto MUKHERJEE of Seattle WA (US) for microsoft technology licensing, llc, Liang DU of Redmond WA (US) for microsoft technology licensing, llc, Ke JIANG of Bellevue WA (US) for microsoft technology licensing, llc, Robin ABRAHAM of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G16C20/70, G06N3/04, G16C20/10
CPC Code(s): G16C20/70
Abstract: this disclosure describes a machine learning system that includes a contrastive learning based two-tower model for retrieval of relevant chemical reaction procedures given a query chemical reaction. the two-tower model uses attention-based transformers and neural networks to convert tokenized representations of chemical reactions and chemical reaction procedures to embeddings in a shared embedding space. each tower can include a transformer network, a pooling layer, a normalization layer, and a neural network. the model is trained with labeled data pairs that include a chemical reaction and the text of a chemical reaction procedure for that chemical reaction. new queries can locate chemical reaction procedures for performing a given chemical reaction as well as procedures for similar chemical reactions. the architecture and training of the model make it possible to perform semantic matching based on chemical structures. the model is highly accurate providing an average recall at k=5 of 95.9%.
Inventor(s): Tsuwang HSIEH of Sammamish WA (US) for microsoft technology licensing, llc, Santiago Martin SEGARRA of Houston TX (US) for microsoft technology licensing, llc, Sathiya Kumaran MANI of Kirkland WA (US) for microsoft technology licensing, llc, Srikanth KANDULA of Redmond WA (US) for microsoft technology licensing, llc, Michael Dean WONG of Princeton NJ (US) for microsoft technology licensing, llc
IPC Code(s): H04L41/16, H04L41/14, H04L41/147, H04L43/045
CPC Code(s): H04L41/16
Abstract: this document relates to automating detecting anomalies in network behavior of an application generally, the disclosed techniques can obtain network flow data for an application. a machine learning model can be used to process the network flow data to detect anomalies. the machine learning model can be retrained over time to adapt to changing network behavior of the application. in some cases, a graph neural network is employed to detect the anomalies.
Inventor(s): Pol LLADO of Brookline MA (US) for microsoft technology licensing, llc, Lihao ZHANG of Quincy MA (US) for microsoft technology licensing, llc, Victor Renjie FU of New York NY (US) for microsoft technology licensing, llc, Tiancong ZHOU of Sammamish WA (US) for microsoft technology licensing, llc, Anirudhan VIJAYAKANTHAN of Bellevue WA (US) for microsoft technology licensing, llc, Victor MacĂȘdo ALEXANDRINO of Viçosa â MG (BR) for microsoft technology licensing, llc, Ke WANG of Redmond WA (US) for microsoft technology licensing, llc, Ahmed Hassan MOHAMED of Bellevue WA (US) for microsoft technology licensing, llc, Kushal Manohar AURANGABADKAR of Renton WA (US) for microsoft technology licensing, llc
IPC Code(s): H04L41/5009, G06Q10/1093, H04L41/50
CPC Code(s): H04L41/5012
Abstract: the present disclosure relates to providing personalized service schedule in a computing network for a service provider to provide a service. in particular, the systems described herein utilize signal history of a plurality of users to train a model and to predict a cumulative signal amount for an individual user for a predetermined time frame in the future by drawing inferences from the model. the system described herein further transforms the predicted cumulative signal amounts to activity data and a personalized service schedule for the individual user may be updated based on the predicted activity data. the personalized service schedule may be utilized by disabling the service, or pausing the service or pausing a feature of the service when the predicted activity data indicates that the user is likely to be inactive.
20250088502. EVENT BASED AUTHENTICATION_simplified_abstract_(microsoft technology licensing, llc)
Inventor(s): Rahul Ramachandra VARRIER of Bothell WA (US) for microsoft technology licensing, llc, Shawn P. HENRY of Kirkland WA (US) for microsoft technology licensing, llc
IPC Code(s): H04L9/40, G06F21/36
CPC Code(s): H04L63/083
Abstract: a method and system for determining a custom personal identification number (pin) for a user based on an ordered series of events are disclosed, comprising generating the custom pin for the user based on a set of stored events of the user, each event associated with a point in time and authenticating the user responsive to receiving information that matches the custom pin. a chronological order of a selected subset of events can be determined. a chronological identifier can be assigned representative of a position of the respective event in the determined chronological order. the custom pin can be determined as an arrangement of the chronological identifiers. the selected subset of events can be presented to the user in the arranged order. the user can be authenticated responsive to determining that a received indication of a chronological arrangement matches the determined pin.
Inventor(s): Daniel DAVRAEV of Or Yehuda (IL) for microsoft technology licensing, llc, Idan Yehoshua HEN of Tel Aviv (IL) for microsoft technology licensing, llc, Tamer SALMAN of Haifa (IL) for microsoft technology licensing, llc
IPC Code(s): H04L9/40
CPC Code(s): H04L63/1416
Abstract: the disclosure focuses on using a context-based insight system to determine security incident reports that include security incident insights and remediation actions based on various combinations of security alerts in cloud computing systems. the context-based insight system uses a security alert generative language model (glm) to generate security incident reports based on correlated security alerts within a security incident and the attack-type contexts of those security alerts. by using the security alert glm guided by attack-type contexts to generate security incident reports, the context-based insight system provides understandable text narratives that provide clear and accurate insights into security incidents including remediation actions to address the security incidents as a whole rather than just reporting individual security alerts of the security incident. further, the context-based insight system dynamically updates the security incident report as additional related security alerts are detected and received.
Inventor(s): Alyssa Nicole RAHMAN of Washington DC (US) for microsoft technology licensing, llc, Austin Lyman Baker of Houston TX (US) for microsoft technology licensing, llc, Anna Swanson Bertiger of Seattle WA (US) for microsoft technology licensing, llc, Nicholas Thomas Deneweth of Lake Orion MI (US) for microsoft technology licensing, llc, Tyler Anthony Smith of Richardson TX (US) for microsoft technology licensing, llc, Naresh Krishnamoorthy of Vancouver (CA) for microsoft technology licensing, llc
IPC Code(s): H04L9/40
CPC Code(s): H04L63/1425
Abstract: described are systems and methods for measuring the similarity of security alerts, security incidents, or other complex data structures at scale using machine-learned signature vectors suitable for efficient similarity-based filtering in conjunction with hashes of the security alerts for more detailed comparisons. in some embodiments, similarity measurements between security incidents are used to base the processing of a current security incident on similar prior security incidents.
Inventor(s): Annapurna Lakshmi SARIPALLI of Hyderabad (IN) for microsoft technology licensing, llc, Srivalli CHAVALI of Redmond WA (US) for microsoft technology licensing, llc, Ashish MEHNDI of Hyderabad (IN) for microsoft technology licensing, llc, Rajeethkumar DHARMARAJ of Coimbatore (IN) for microsoft technology licensing, llc, Chithirai Meenal THIYAGARAJAN of Chennai (IN) for microsoft technology licensing, llc, Chinmaya MISHRA of Bangalore (IN) for microsoft technology licensing, llc, Jovin Vasanth Kumar DEVA SAHAYAM ARUL RAJ of Redmond WA (US) for microsoft technology licensing, llc, Deepika PURI of Syosset NY (US) for microsoft technology licensing, llc, Ankit SRIVASTAVA of Seattle WA (US) for microsoft technology licensing, llc
IPC Code(s): H04L9/40
CPC Code(s): H04L63/20
Abstract: some embodiments address technical challenges arising from efforts to identify and mitigate security risks, in particular but not only, risks that sensitive data will be exfiltrated. some embodiments provide or utilize an anomaly detector which is configured to detect a security anomaly in data based on at least a distribution of sensitive information type documents in a collection of documents and classifications of documents by trainable classifiers based on machine learning. some embodiments provide or utilize a security policy generator which is configured to proactively and automatically generate security policy recommendations, rank at least two of the security policy recommendations, and present at least one top-ranked generated security policy recommendation in a user interface. some embodiments generate a security policy in a managed computing system based on at least an anomaly score, and then configure the managed computing system according to the generated security policy.
Inventor(s): Irene VOSKAMP of Friday Harbor WA (US) for microsoft technology licensing, llc, Michiel VAN OTEGEM of Wormer (NL) for microsoft technology licensing, llc, Doreen Lynn GALLI of Henderson NV (US) for microsoft technology licensing, llc, Adrian Vincenzo DIGLIO of Bellevue WA (US) for microsoft technology licensing, llc
IPC Code(s): H04L9/40, G06F21/62, G06F21/64
CPC Code(s): H04L63/20
Abstract: systems generate and utilize ledger records of verifiable evidence that attest to the authenticity and integrity attributes of hardware and software components of a particular computing environment. the ledger record can be sealed to provide a level of assurance that the attestations and evidence in the ledger record have not been tampered with. the ledger record can be shared with different systems and referenced to assess a level of trust and compliance of a computing environment relative to a particular policy context. subsequent endorsements and controls can also be based on the assessed trust and compliance derived from the ledger records.
Inventor(s): Oscar FARIAS MOGUEL of Seattle WA (US) for microsoft technology licensing, llc, Dennis TRIEU of Calgary (CA) for microsoft technology licensing, llc, Kathryn M. OSEEN-SENDA of Seattle WA (US) for microsoft technology licensing, llc, Hien Anh THAI of Kirkland WA (US) for microsoft technology licensing, llc
IPC Code(s): H05K7/20
CPC Code(s): H05K7/203
Abstract: a system may include an immersion working fluid having a flow direction. a system may include a heat extraction plate with an internal chamber therein. a system may include an internal working fluid positioned in the heat extraction plate. a system may include a heat sink in fluid communication with the internal chamber by at least one fluid conduit, wherein the heat sink is downstream from the heat extraction plate in the flow direction.
Microsoft Technology Licensing, LLC patent applications on March 13th, 2025
- Microsoft Technology Licensing, LLC
- G01R31/28
- G01R31/66
- CPC G01R31/2896
- Microsoft technology licensing, llc
- G02B21/14
- CPC G02B21/14
- G02B27/01
- G02B27/00
- G06T19/00
- CPC G02B27/0172
- G06F3/0354
- G06F3/01
- G06F3/041
- G06F3/04883
- CPC G06F3/03545
- CPC G06F3/0418
- G06F9/455
- CPC G06F9/45558
- G06F9/50
- CPC G06F9/5005
- CPC G06F9/505
- C/o microsoft technology licensing, llc
- G06F11/07
- CPC G06F11/0766
- CPC G06F11/079
- G06F11/34
- CPC G06F11/3423
- G06F11/36
- G06N20/00
- CPC G06F11/3624
- G06F16/2455
- CPC G06F16/24564
- G06F16/28
- CPC G06F16/285
- G06F16/951
- G06F16/2457
- G06F16/9535
- CPC G06F16/951
- G06F16/9032
- G06F40/30
- CPC G06F16/9535
- G06F40/35
- G06F40/169
- G06F40/40
- CPC G06F40/35
- G06F40/42
- CPC G06F40/42
- G06N3/091
- G06N3/0475
- CPC G06N3/091
- G06T5/10
- G06T5/50
- G06T7/246
- CPC G06T5/10
- G10L21/16
- H04S7/00
- CPC G10L21/16
- G10L25/78
- G10L19/02
- G10L25/30
- CPC G10L25/78
- G11B7/005
- G11B7/00
- G11B7/126
- G11B7/253
- CPC G11B7/0051
- G16C20/70
- G06N3/04
- G16C20/10
- CPC G16C20/70
- H04L41/16
- H04L41/14
- H04L41/147
- H04L43/045
- CPC H04L41/16
- H04L41/5009
- G06Q10/1093
- H04L41/50
- CPC H04L41/5012
- H04L9/40
- G06F21/36
- CPC H04L63/083
- CPC H04L63/1416
- CPC H04L63/1425
- CPC H04L63/20
- G06F21/62
- G06F21/64
- H05K7/20
- CPC H05K7/203
