Microsoft Technology Licensing, LLC patent applications on October 17th, 2024
Patent Applications by Microsoft Technology Licensing, LLC on October 17th, 2024
Microsoft Technology Licensing, LLC: 29 patent applications
Microsoft Technology Licensing, LLC has applied for patents in the areas of G06F40/40 (6), G06N20/00 (4), G06F40/166 (3), G06F40/20 (2), G06F40/35 (2) G06F40/40 (3), G06F40/166 (2), A45C11/00 (1), H04L41/5054 (1), H04L12/4633 (1)
With keywords such as: language, data, processing, image, large, based, user, systems, memory, and information in patent application abstracts.
Patent Applications by Microsoft Technology Licensing, LLC
Inventor(s): Michael Gordon OLDANI of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): A45C11/00, H01F7/02
CPC Code(s): A45C11/00
Abstract: a kickstand assembly comprises a backplate comprising a backplate retention magnet having a first magnetic pole orientation and an upper kickstand plate slidably connected to the backplate. a return biaser biases the upper kickstand plate toward a rest position. a lower kickstand plate is rotatably coupled to the upper kickstand plate, with the lower kickstand plate comprising a closing magnet having a second magnetic pole orientation that attracts the first magnetic pole orientation of the backplate retention magnet. a deployment biaser biases the lower kickstand plate to rotate away from the backplate when the upper kickstand plate is translated away from the rest position.
Inventor(s): Jatin SHARMA of Sammamish WA (US) for microsoft technology licensing, llc, Jonathan T. CAMPBELL of Redmond WA (US) for microsoft technology licensing, llc, Jay C. BEAVERS of Duvall WA (US) for microsoft technology licensing, llc, Peter John ANSELL of Renton WA (US) for microsoft technology licensing, llc
IPC Code(s): A61B5/16, A61B5/00
CPC Code(s): A61B5/163
Abstract: systems and methods are provided for collecting eye-gaze data for training an eye-gaze prediction model. the collecting includes selecting a scan path passing through a series of regions of a grid on a screen of a computing device, moving a symbol as an eye-gaze target along the scan path, and receiving facial images at eye-gaze points. the eye-gaze points are uniformly distributed within the respective regions. areas of the regions that are adjacent to edges and corners of the screen are smaller than other regions. the difference in areas shifts centers of the regions toward the edges, density of data closer to the edges. the scan path passes through locations in proximity to the edges and corners of the screen for capturing more eye-gaze points in the proximity. the methods interactively enhance variations of facial images by displaying instructions to the user to make specific actions associated with the face.
Inventor(s): Winston Allen SAUNDERS of Seattle WA (US) for microsoft technology licensing, llc, Eric C. PETERSON of Woodinville WA (US) for microsoft technology licensing, llc, Ruslan NAGIMOV of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): B01D53/26, B01D53/04, F16M11/20, F24S10/80, F24S50/80, H02N2/18
CPC Code(s): B01D53/261
Abstract: a device for harvesting ambient fluid includes a cool area and a hot area. the device includes an absorption material configured to absorb ambient fluid. the device includes a means for moving the absorption material between the hot area and the cool area based on a weight differential of the absorption material.
Inventor(s): Raymond Kirk PRICE of Carnation WA (US) for microsoft technology licensing, llc, Michael BLEYER of Seattle WA (US) for microsoft technology licensing, llc, Christopher Douglas EDMONDS of Carnation WA (US) for microsoft technology licensing, llc, William Chao-Hui HWANG of Bellevue WA (US) for microsoft technology licensing, llc
IPC Code(s): G02B27/01, H04N23/65, H04N25/441, H04N25/445
CPC Code(s): G02B27/0172
Abstract: a system for power efficient image acquisition is configurable to capture, using an image sensor, a plurality of partial image frames including at least a first partial image frame and a second partial image frame. the first partial image frame is captured at a first timepoint using a first subset of image sensing pixels of the plurality of image sensing pixels of the image sensor. the second partial image frame is captured at a second timepoint using a second subset of image sensing pixels of the plurality of image sensing pixels of the image sensor. the second subset of image sensing pixels includes different image sensing pixels than the first subset of image sensing pixels, and the second timepoint is temporally subsequent to the first timepoint. the system is configurable to generate a composite image frame based on the plurality of partial image frames.
20240345664. Through Hole Keyboard_simplified_abstract_(microsoft technology licensing, llc)
Inventor(s): Masaaki FUKUMOTO of Shenzhen (CN) for microsoft technology licensing, llc, Paul Christopher KOS of Shenzhen (CN) for microsoft technology licensing, llc, Kelong ZHAO of Shenzhen (CN) for microsoft technology licensing, llc, Haiji SUN of Shenzhen (CN) for microsoft technology licensing, llc, Bin ZHAI of Shenzhen (CN) for microsoft technology licensing, llc, Mingjie WANG of Shenzhen (CN) for microsoft technology licensing, llc
IPC Code(s): G06F3/02, H01H13/70, H01H13/704, H01H13/7065, H01H13/81
CPC Code(s): G06F3/0224
Abstract: the present description relates to devices, such as keyboards. one example can include a top portion and an opposing bottom portion and key switches that are mechanically retained in holes formed in the top cover. the example can also include electrical traces formed on the top cover that extend from the holes to a processing unit that is configured to receive electrical signals along the electrical traces when the key switches are closed.
Inventor(s): Eliyahu MASHHADI KALIMI of Netanya (IL) for microsoft technology licensing, llc, Roei Shlomo MENASHOF of Natanya (IL) for microsoft technology licensing, llc, David MANSOUR of Yehud (IL) for microsoft technology licensing, llc
IPC Code(s): G06F8/71, G06F8/36
CPC Code(s): G06F8/71
Abstract: central package management (cpm) across code repositories is disclosed. in an example, a primary “props” file in a git submodule references a version of a nuget package. a “.props” file in a root directory of each of multiple code repositories references the primary props file in the git submodule. each of multiple software projects in the code repositories has a package reference to the nuget package without a reference to the version of the nuget package. when each of the multiple the software projects is built (e.g., compiled), the build process automatically uses the version of the nuget package that is identified in the git submodule. this permits a single change to the version number of the nuget package in the git submodule to be propagated across multiple projects spanning multiple code repositories.
Inventor(s): Ravi Teja BELLAM of Redmond WA (US) for microsoft technology licensing, llc, Rohith Reddy GUNDREDDY of Redmond WA (US) for microsoft technology licensing, llc, Woo Sik KIM of Redmond WA (US) for microsoft technology licensing, llc, Vineeth THAYANITHI of Naperville IL (US) for microsoft technology licensing, llc, Neil Patrick GOMPF of Redmond WA (US) for microsoft technology licensing, llc, Arup ARCALGUD of Redmond WA (US) for microsoft technology licensing, llc, Gurpreet SOHAL of Seattle WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F11/07, G06N20/00
CPC Code(s): G06F11/079
Abstract: disclosed is a system for providing machine learning aided diagnostics and prognostics for large distributed systems. a diagnostics module applies two-tiered analysis to detect anomalous behavior of the large scale distributed system. first, multivariate telemetry and event data emitted from the large scale distributed systems is collected by a diagnostics component, which applies multivariate analysis to identify of set of n-anomalies. second, univariate telemetry and event data is obtained by the diagnostics component, which applies univariate analysis to the n-anomalies previously identified, ranks the results, and provides them to an ai to generate a diagnostics incident report. a prognostics module reviews the diagnostics incident report and maps each identified issue to a resolution plan. if execution of the resolution plan does not succeed in resolving the identified issue, the issue is escalated to a support team. the disclosed techniques may predict and prevent issues, or drastically reduce resolution time.
Inventor(s): Karunakara KOTARY of Vancouver WA (US) for microsoft technology licensing, llc, Akram HAMDY of Redmond WA (US) for microsoft technology licensing, llc, Pingfan SONG of Newcastle WA (US) for microsoft technology licensing, llc, Neeraj LADKANI of Bothell WA (US) for microsoft technology licensing, llc, Muhammad A. AHMED of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F11/14, G06F8/61
CPC Code(s): G06F11/1417
Abstract: a platform-independent method of securely resetting a processing device includes detecting a predefined trigger event by a baseboard management controller (bmc) that executes system firmware on behalf of a managed host. in response to the predefined trigger event, the system is booted into a safe mode. while in the safe mode, a central processing system of the managed host is maintained in an off state, and a self-heal agent detects architectural characteristics of the managed host, establishes a connection to a cloud-based firmware catalog service, transmits the architectural characteristics of the managed host to the cloud-based firmware catalog service, and downloads a new version of system firmware from the cloud-based firmware catalog service that is compatible with the architectural characteristics of the managed host. the new version of the system firmware is automatically installed without powering on the central processing system of the managed host.
Inventor(s): Maoni Zhang STEPHENS of Kirkland WA (US) for microsoft technology licensing, llc, Brendan Davis BURNS of Seattle WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F12/02
CPC Code(s): G06F12/0253
Abstract: embodiments control garbage collection priority based on both a local memory pressure and a global memory pressure. the local pressure represents volatile memory usage in a container or other isolation unit residing on a machine, and the global pressure represents volatile memory usage in the machine overall. the machine is a device or a virtual machine containing one or more isolation units. each isolation unit has a low threshold and a high threshold, and the machine has its own low threshold and its own high threshold. garbage collection execution priority is set to low, normal, or high, depending on the memory pressures and the thresholds. by basing garbage collection timing and performance on both local pressure and global pressure, embodiments optimize garbage collection efficiency, especially in memory overcommitment scenarios.
Inventor(s): Kyle Matthew MILLER of Lynnwood WA (US) for microsoft technology licensing, llc, Hariharan RAGUNATHAN of Woodinville WA (US) for microsoft technology licensing, llc, Tai XIN of Bellevue WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F16/957, G06F3/0481, G06F16/958
CPC Code(s): G06F16/9577
Abstract: systems and methods that implement a framework for providing a detachable browser interface (sidebar) for context-aware web services. the present framework allows for a user to detach the detachable sidebar from a web browser window and dock the sidebar to a desktop of an operating system. as a result, the context-aware web services of the sidebar can be used in conjunction with the web browser application and with other applications (e.g., browser or non-browser applications).
Inventor(s): Mallik BULUSU of Bellevue WA (US) for microsoft technology licensing, llc, Muhammad A. AHMED of Seattle WA (US) for microsoft technology licensing, llc, Ganesh KUMAR A of Hyderabad (IN) for microsoft technology licensing, llc, Kiran Bangalore SATHYANARAYANA of Bangalore (IN) for microsoft technology licensing, llc, Pingfan SONG of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F21/57, G06F21/12
CPC Code(s): G06F21/577
Abstract: disclosed herein is a system for limiting the rate at which system management interrupts can suspend normal execution of a central processing unit (cpu) by switching the operating mode of the cpu from one of the real mode or the protected mode to the system management mode. the rate limits imposed by the system provides a protective layer against cyberattacks (e.g., a distributed denial-of-service (ddos) attack) from malicious actors and ensures the cpu can be more efficient regarding the execution of workloads (e.g., processing threads).
Inventor(s): Erik Daniel ANDERSON of Redmond WA (US) for microsoft technology licensing, llc, Joseph J. PFEIFFER, III of Seattle WA (US) for microsoft technology licensing, llc, Denis Xavier CHARLES of Redmond WA (US) for microsoft technology licensing, llc, Aleksandr REBRIKOV of Bellevue WA (US) for microsoft technology licensing, llc, John Robert MOORING of Bellevue WA (US) for microsoft technology licensing, llc, Brandon J. MASLEN of Redmond WA (US) for microsoft technology licensing, llc, Davis Leland GILTON of Seattle WA (US) for microsoft technology licensing, llc, Sergey YEKHANIN of Redmond WA (US) for microsoft technology licensing, llc, Sivakanth GOPI of Skokie IL (US) for microsoft technology licensing, llc
IPC Code(s): G06F21/62, G06F21/60
CPC Code(s): G06F21/6254
Abstract: disclosed is a system that tracks website usage without compromising user privacy. the system aggregates website usage data of multiple users across multiple websites. website usage data is aggregated in a way that preserves each individual user's privacy. specifically, information relevant to a particular user may be obtained from the aggregated information without exposing what was actually collected from that user. in some configurations, user-specific website usage data is aggregated using trusted execution environment computing hardware. this ensures that privacy is preserved while user-specific data is transferred to and processed by the system. the trusted execution environment applies differential privacy techniques to ensure that use of the aggregated information does not reveal actual information about a user's website usage history. in this way, privacy is maintained while still enabling many of the scenarios that would otherwise rely on third-party cookies.
Inventor(s): Abed El Kader ASI of Sammamish WA (US) for microsoft technology licensing, llc, Alexander TSVETKOV of Tel Aviv (IL) for microsoft technology licensing, llc, Royi RONEN of Tel Aviv (IL) for microsoft technology licensing, llc, Yarin KUPER of Tel Aviv (IL) for microsoft technology licensing, llc, Shahar Zvi KEREN of Hemed (IL) for microsoft technology licensing, llc, Roy EISENSTADT of Tel Aviv (IL) for microsoft technology licensing, llc
IPC Code(s): G06F40/166, G06F40/40
CPC Code(s): G06F40/166
Abstract: example solutions for reducing the likelihood of hallucinations by language models, such as large language models (llms) are disclosed. by injecting a sufficient range and quantity of curated factual data into a prompt, the likelihood of a hallucination by an llm may be reduced. this enables language models to be used in a wider range of settings, in which fabrication of facts is problematic, while reducing the need for a human to carefully check the generated text for accuracy. examples include: generating a summary of a transcript using a summarization model; extracting topic-specific data from stored data using a scoring model; dynamically generating a language model prompt using the topic-specific data and the summary; and generating an output text using a language model and the language model prompt.
Inventor(s): Edy Daniel PAULINO of Bellevue WA (US) for microsoft technology licensing, llc, Kyle Matthew Unger of Seattle WA (US) for microsoft technology licensing, llc, Judah Gabriel Himango of Monroe WA (US) for microsoft technology licensing, llc, Wey Hsuan Low of Seattle WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F40/166, G06F40/40, G06N3/08
CPC Code(s): G06F40/166
Abstract: methods, computer systems, computer-storage media, and graphical user interfaces are provided for efficiently generating review summaries. in embodiments, reviews associated with an item are obtained. a set of the reviews are then determined or selected based on an attribute associated with the corresponding review. thereafter, a model prompt to be input into a trained machine learning model is generated. the model prompt can include an indication of the item and the determined set of the reviews. as output from the trained machine learning model, a review summary that summarizes the set of the reviews associated with the item is obtained.
Inventor(s): Vu Le of Bellevue WA (US) for microsoft technology licensing, llc, Quan NGUYEN of Redmond WA (US) for microsoft technology licensing, llc, Siddharth UPPAL of Bothell WA (US) for microsoft technology licensing, llc, Ankit GOVIL of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F40/30, G06F9/445, G06F40/289, G06N20/00, H04L51/214
CPC Code(s): G06F40/30
Abstract: described herein is a topic evaluation engine that operates in connection with a messaging service by analyzing individual text-based messages, received during a text-based communication session, to identify various message characteristics of each text-based message, and/or to infer one or more topics to which each message relates. each message that is determined to have a particular message characteristic is then forwarded to any application that previously subscribed with the messaging service to receive messages having the specific message characteristic. similarly, each message that is associated with a specific topic is distributed to any application integrated with the messaging service that has previously subscribed with the messaging service to receive messages relating to the specific topic. the integrated applications can then process the message and provide enhanced functionality.
Inventor(s): Yang LIU of Bellevue WA (US) for microsoft technology licensing, llc, Yichong XU of Bellevue WA (US) for microsoft technology licensing, llc, Dan ITER of Austin TX (US) for microsoft technology licensing, llc, Chenguang ZHU of Bellevue WA (US) for microsoft technology licensing, llc, Nanshan ZENG of Bellevue WA (US) for microsoft technology licensing, llc, Shuohang WANG of Belevue WA (US) for microsoft technology licensing, llc, Hiteshi SHARMA of San Jose CA (US) for microsoft technology licensing, llc
IPC Code(s): G06F40/40, G06F40/186, G06F40/20, G06F40/35, G06N20/00
CPC Code(s): G06F40/40
Abstract: the techniques described herein enhance the operations of natural language generation systems through training and/or augmentation by a large language model. in a first example, the large language model can execute training operations by processing a training dataset to produce a natural language output. the natural language generation system can analyze the training dataset and the natural language output to generate a natural language output mimicking the output of the large language model. the large language model can then evaluate the output of the natural language generation system to iteratively adjust and improve the quality of natural language outputs. in a second example, the large language can augment a small language model in executing natural language tasks. this is accomplished by retrieving external information using the large language model to generate an augmentation input to provide context and a language framework to the small language model to enhance overall outputs.
Inventor(s): Sebastian Johannes BLOHM of Munich (DE) for microsoft technology licensing, llc, Dmitriy MEYERZON of Bellevue WA (US) for microsoft technology licensing, llc, Aaron Lee HALFAKER of Seattle WA (US) for microsoft technology licensing, llc, James John HENSMAN of Cambridge (GB) for microsoft technology licensing, llc
IPC Code(s): G06F40/40, G06F16/332, G06F21/62, G06F40/166, G06F40/35
CPC Code(s): G06F40/40
Abstract: the techniques disclosed herein enable systems to enhance the efficiency and functionality of knowledge base systems through automated generation of knowledge base content such as topic definitions using a large language model. this is accomplished by utilizing a summarization module that processes incoming requests pertaining to a knowledge base topic. in response to a request, the summarization module can retrieve information related to the topic and generate an instruction directing a large language model to generate a natural language output. by generating the instruction from the specific context of the knowledge base, the disclosed techniques can ensure that outputs received from the large language model are consistent and relevant. in addition, content that was generated based on privileged information such as an access-controlled document can receive the same access controls to maintain information security. furthermore, large language model outputs can undergo a review and editing process to ensure accuracy.
Inventor(s): Yinghua QIN of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06F40/40
CPC Code(s): G06F40/40
Abstract: systems, methods, apparatuses, and computer program products are disclosed for using retrieval augmented artificial intelligence to generate a response to a query. a first feature vector is generated based at least on the query. the first feature vector is compared to a plurality of second feature vectors to determine a subset of the second feature vectors that satisfy a predetermined condition. augmentation information corresponding to the determined subset of second feature vectors are retrieved. an augmented prompt, generated based on the query and the retrieved augmentation information, is provided to a large language model. a response generated by the large language model is received.
Inventor(s): Yuan-Jyue CHEN of Seattle WA (US) for microsoft technology licensing, llc, Karin STRAUSS of Seattle WA (US) for microsoft technology licensing, llc, Christian PAQUIN of Herndon VA (US) for microsoft technology licensing, llc, Alexander Steven CROWN of Bellevue WA (US) for microsoft technology licensing, llc, Sergey YEKHANIN of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06K7/14, C12Q1/6823, G16B50/30
CPC Code(s): G06K7/1413
Abstract: molecular anti-counterfeiting taggants are made from a plurality of synthetic polynucleotides that collectively encode a bit sequence using the sequences and hybridization states of the polynucleotides. the polynucleotide taggant is placed on an item as a molecular identifier of authenticity. the bit sequence encoded by the polynucleotide taggant is read out using a substrate which has bound polynucleotides complexes that hybridize with the synthetic polynucleotides in the polynucleotide taggant. a detectable signal is present where hybridization occurs. to prevent a bad actor from reverse engineering and creating a copy of the polynucleotide taggant using the results of hybridization to the substrate, multiple versions of the substrate are created. each version hybridizes to different subsets of the synthetic polynucleotides in the polynucleotide taggant. a detectable pattern on the substrate that is present when exposed to the polynucleotide taggant is used for validating authenticity of the item.
Inventor(s): Weizhu CHEN of Kirkland WA (US) for microsoft technology licensing, llc, Pengcheng HE of Sammamish WA (US) for microsoft technology licensing, llc, Xiaodong LIU of Bellevue WA (US) for microsoft technology licensing, llc, Jianfeng GAO of Woodinville WA (US) for microsoft technology licensing, llc
IPC Code(s): G06N3/047, G06F40/20, G06N3/045, G06N3/088
CPC Code(s): G06N3/047
Abstract: this document relates to architectures and training procedures for multi-task machine learning models, such as neural networks. one example method involves providing a multi-task machine learning model having one or more shared layers and two or more task-specific layers. the method can also involve performing a pretraining stage on the one or more shared layers using one or more unsupervised prediction tasks. the method can also involve performing a tuning stage on the one or more shared layers and the two or more task-specific layers using respective task-specific objectives
Inventor(s): Choo Yei CHONG of Redmond WA (US) for microsoft technology licensing, llc, Matthew ONTELL of Glen Allen VA (US) for microsoft technology licensing, llc, Sakshi GURABA of San Jose CA (US) for microsoft technology licensing, llc
IPC Code(s): G06Q10/0631
CPC Code(s): G06Q10/06312
Abstract: a method for delivering relevant resources during the execution of an enterprise application is implemented via a computing system and includes executing the enterprise application on a remote computing system operated by an enterprise user, causing the surfacing of a user interface (ui) on the remote computing system's display, determining enterprise user attributes based on enterprise-level data, and responsive to user input including an interaction with the enterprise application, automatically detecting productive state attributes corresponding to a current productive state of the enterprise user with respect to the enterprise application. the method also includes automatically detecting when the current productive state includes a productive value that is below a threshold productive value, automatically determining resource(s) to increase the productive value to above the threshold productive value by applying a propensity model to the enterprise user attributes and the productive state attributes, and providing the resource(s) via the ui.
Inventor(s): Yeye HE of Bellevue WA (US) for microsoft technology licensing, llc, Yiming Stefania LIN of Irvine CA (US) for microsoft technology licensing, llc, Surajit CHAUDHURI of Kirkland WA (US) for microsoft technology licensing, llc
IPC Code(s): G06Q10/067, G06F16/2453
CPC Code(s): G06Q10/067
Abstract: the present disclosure relates to methods and systems that automatically predict a business intelligence model for tables of data provided as input. the methods and systems automatically generate a graph representing the business intelligence model and provide the graph as output. the graph provides a visual representation of the business intelligence model with nodes of the graph representing each input table and edges of the graph representing weighted edges joining pairs of tables together.
Inventor(s): Mayank SHRIVASTAVA of Woodinville WA (US) for microsoft technology licensing, llc, Sagar GOYAL of Vancouver (CA) for microsoft technology licensing, llc, Sahil BHATNAGAR of Vancouver (CA) for microsoft technology licensing, llc, Pin-Jung CHEN of Bellevue WA (US) for microsoft technology licensing, llc, Pushpraj SHUKLA of Dublin CA (US) for microsoft technology licensing, llc, Arko P. MUKHERJEE of Issaquah WA (US) for microsoft technology licensing, llc
IPC Code(s): G06Q30/0202, G06N3/04, G06N3/084, G06N20/00
CPC Code(s): G06Q30/0202
Abstract: the disclosure herein describes a system for generating embeddings representing sequential human activity by self-supervised, deep learning models capable of being utilized by a variety of machine learning prediction models to create predictions and recommendations. an encoder-decoder is provided to create user-specific journeys, including sequenced events, based on human activity data from a plurality of tables, a customer data platform, or other sources. events are represented by sequential feature vectors. a user-specific embedding representing user activities in relationship to activities of one or more other users is created for each user in a plurality of users. the embeddings are updated in real-time as new activity data is received. the embeddings can be fine-tuned using labeled data to customize the embeddings for a specific predictive model. the embeddings are utilized by predictive models to create product recommendations and predictions, such as customer churn, next steps in a customer journey, etc.
Inventor(s): Yinghua QIN of Redmond WA (US) for microsoft technology licensing, llc
IPC Code(s): G06Q30/0601, G06F40/134, G06F40/40
CPC Code(s): G06Q30/0631
Abstract: systems, methods, apparatuses, and computer program products are disclosed for using retrieval augmented artificial intelligence to provide content recommendations. a first feature vector is generated based at least on user contextual information. second feature vectors are determined based on a comparison of the first feature vector to a plurality of second feature vectors. content items corresponding to the determined second feature vectors are retrieved. an augmented prompt generated based on the user contextual information and the retrieved content items is provided to a large language model to request a recommendation. a content recommendation is received from the large language model based on the augmented prompt.
Inventor(s): Vaidehi ORUGANTI of Kirkland WA (US) for microsoft technology licensing, llc, Bharath RAMAKRISHNAN of Bellevue WA (US) for microsoft technology licensing, llc, Husam Atallah ALISSA of Redmond WA (US) for microsoft technology licensing, llc, Christian L. BELADY of Mercer Island WA (US) for microsoft technology licensing, llc
IPC Code(s): H01L23/473, H01L23/522
CPC Code(s): H01L23/473
Abstract: a processing unit includes a substrate, an electrical load, and a microfluidic volume. the electrical load is supported by the first surface of the substrate, and the microfluidic volume is positioned in the second surface of the substrate. the processing unit includes a first electrode positioned in the microfluidic volume and a second electrode positioned in the microfluidic volume. a first tsv connects the first electrode to the electrical load, and a second tsv connects the second electrode to the electrical load. an electrochemical fluid is positioned in the microfluidic volume to provide electrical power to the electrical load and receive heat from the electrical load.
Inventor(s): Brett K. DODDS of Boise ID (US) for microsoft technology licensing, llc, Terry M. GRUNZKE of Boise ID (US) for microsoft technology licensing, llc
IPC Code(s): H03M13/15, H03M13/00
CPC Code(s): H03M13/35
Abstract: a memory controller may receive memory data to be stored on a memory. a memory controller may receive metadata related to the memory data. the metadata may be selected from a predetermined list of metadata. a memory controller may identify an encoding polynomial of a plurality of polynomials that is associated with the metadata, each polynomial of the plurality of polynomials associated with different metadata from the predetermined list of metadata. a memory controller may generate a codeword using the encoding polynomial of the plurality of polynomials and the memory data.
Inventor(s): Paul David MATTES of Saint Paul MN (US) for microsoft technology licensing, llc, Umesh KRISHNASWAMY of San Jose CA (US) for microsoft technology licensing, llc, Ashlesha ATREY of Sunnyvale CA (US) for microsoft technology licensing, llc, Guruprasad Bangalore HIRIYANNAIAH of Fremont CA (US) for microsoft technology licensing, llc
IPC Code(s): H04L12/46, H04L43/10, H04L45/50, H04L45/745
CPC Code(s): H04L12/4633
Abstract: bi-directional tunnel probing in a network may be extended to multi-realm networks, providing bi-directional probing in a multi-realm network. bi-directional probing uses probe packets with a forward tunnel label, a reverse tunnel label, and an ip packet header. bi-directional probing in a multi-realm network uses a forward tunnel label, an sid, a reverse tunnel label, and an ip packet header. penultimate hop popping strips the outermost labels in a specific order. the sid gets the probe packet back into the originating realm, and the reverse tunnel label returns the probe packet along the reverse direction of the tunnel. no sid is needed for intra-realm probing, and the ip packet header is used to return the probe packet if the reverse tunnel label is absent.
Inventor(s): Ronald Mark PARKER of Manchester MA (US) for microsoft technology licensing, llc, Mark Gordon LIBBY of Groton MA (US) for microsoft technology licensing, llc, Michael Anthony BROWN of McKinney TX (US) for microsoft technology licensing, llc, Haibo QIAN of Frisco TX (US) for microsoft technology licensing, llc, Rahul BOSE of Westford MA (US) for microsoft technology licensing, llc
IPC Code(s): H04L41/5054, H04L41/5051, H04W4/50, H04W24/02
CPC Code(s): H04L41/5054
Abstract: the present disclosure relates to systems, methods, and computer readable media for facilitating placement of network functions based on a network slice profile that is received and based on internal knowledge of a cloud computing system having network resources thereon. the systems described herein involve tagging the network resources with various characteristics, generating resource management profiles including instructions that may be used to supplement information from the slice profile(s), and matching an incoming slice profile with a resource management profile. the systems described herein facilitate rolling out a deployment of network functions on the network resources in accordance with information from the resource management profile in a way that optimizes resources and allows automated placement of network functions based on a received network slice.
Inventor(s): Sumit MAHESHWARI of Westford MA (US) for microsoft technology licensing, llc, Gopi Mallikharjun BHIMAVARAPU of Hyderabad (IN) for microsoft technology licensing, llc, Ronaid Mark PARKER of Manchester MA (US) for microsoft technology licensing, llc
IPC Code(s): H04L49/50, H04L47/62, H04L49/00
CPC Code(s): H04L49/501
Abstract: an internal flow traffic controller of a multi-core processing system redirects packets among a plurality of processing cores with stateful flow awareness. the packets belong to flows of network traffic at a packet forwarding node of a 5g network or beyond. the internal flow traffic controller may include a memory storing computer-executable instructions; and a processor configured to execute the computer-executable instructions. the internal flow traffic controller is configured to distribute new incoming flows of network traffic to one of the plurality of processing cores; identify, based on an imbalance among the plurality of processing cores, an overloaded processing core to rebalance; identify a subject flow to move from the overloaded processing core; identify a target processing core with a lowest utilization; and migrate processing of the subject flow from the overloaded processing core to the target processing core.
Microsoft Technology Licensing, LLC patent applications on October 17th, 2024
- Microsoft Technology Licensing, LLC
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- H01F7/02
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- Microsoft technology licensing, llc
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- H04L47/62
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- CPC H04L49/501
