View source for GOOGLE LLC patent applications on July 11th, 2024

=Patent Applications by GOOGLE LLC on July 11th, 2024=



[[:Category:GOOGLE LLC|GOOGLE LLC]]: 66 patent applications[[Category:GOOGLE LLC]]

GOOGLE LLC has applied for patents in the areas of [[:Category:G06F3/01|G06F3/01]] (5), [[:Category:G10L15/30|G10L15/30]] (5), [[:Category:G10L15/26|G10L15/26]] (4), [[:Category:G06N3/04|G06N3/04]] (4), [[:Category:G06N20/00|G06N20/00]] (4)
[[:Category:CPC_G01C21/3476|G01C21/3476]] (2), [[:Category:CPC_G06N3/063|G06N3/063]] (2), [[:Category:CPC_G10L15/16|G10L15/16]] (2), [[:Category:CPC_G10L15/26|G10L15/26]] (2), [[:Category:CPC_A63F13/35|A63F13/35]] (1)

With keywords such as: data, device, based, include, user, media, computing, receiving, memory, and processing in patent application abstracts.




====Patent Applications by GOOGLE LLC====


[[20240226729. HYBRID CLOUD AND LOCAL RENDERING_simplified_abstract_(google llc)]]

Inventor(s): Sim Douglas Dietrich, Jr. of Los Gatos CA (US) for google llc

IPC Code(s): A63F13/35, G06T7/20, G06T7/40, G06T7/70

CPC Code(s): A63F13/35




Abstract: information is received by one or more servers regarding a plurality of components of a virtual environment to be presented for display by a remote client device. a set of remote rendering prioritization values, indicating a degree of prioritization for rendering individual components of the plurality of components by the one or more server computing devices, is determined based on the received information. based on the set of remote rendering prioritization values, a partial scene of the virtual environment is generated at the one or more servers by rendering a set of first components of the plurality of components. the generated partial scene of the virtual environment is transmitted to the remote client device for compositing with a set of second components of the plurality of components to be rendered by the remote client device.[[Category:A63F13/35]][[Category:G06T7/20]][[Category:G06T7/40]][[Category:G06T7/70]][[Category:CPC_A63F13/35]][[Category:google llc]]



[[20240226731. LOW-DELAY MULTI-PASS FRAME-LEVEL RATE CONTROL USING A SHARED REFERENCE FRAME_simplified_abstract_(google llc)]]

Inventor(s): Danny Hong of New York NY (US) for google llc, Ramachandra Tahasildar of Cupertino CA (US) for google llc, Alex Sukhanov of Sunnyvale CA (US) for google llc

IPC Code(s): A63F13/355, A63F13/358, G06T9/00

CPC Code(s): A63F13/355




Abstract: a multi-pass encoding operation using a shared reference frame is implemented to encode one or more gaming frames into a game stream. the multi-pass encoding operation includes producing a shared reference frame based upon a second-pass reference frame used by a second pass encoding of the multi-pass encoding operation. the encoding operation also includes performing a first encoding pass on a current frame using the shared reference frame. as a result of the first encoding pass, an estimated complexity for the current frame is determined. a second pass encoding is then performed on the current frame according to the second-pass reference frame and the estimated complexity, resulting in an encoded frame. this encoded frame is then transmitted as part of a stream to a client system.[[Category:A63F13/355]][[Category:A63F13/358]][[Category:G06T9/00]][[Category:CPC_A63F13/355]][[Category:google llc]]



[[20240230353. Cost Based Navigation and Route Planning_simplified_abstract_(google llc)]]

Inventor(s): Yan Mayster of Aurora CO (US) for google llc, Robert Bruce Bahnsen of Boulder CO (US) for google llc, Zhiyuan Weng of Superior CO (US) for google llc

IPC Code(s): G01C21/34, G06Q10/047

CPC Code(s): G01C21/3476




Abstract: methods, systems, devices, and tangible non-transitory computer readable media for navigation and route planning are provided. the disclosed technology can include accessing request data including information associated with requests of a user. based on the user requests, locations associated with satisfying the requests can be determined. routes associated with the locations that satisfy the user requests can be determined. completion costs respectively associated with satisfying the requests at the locations can be determined. based on travel criteria, a travel route can be selected from the routes. the travel criteria can be associated with the completion costs of each of the routes. output including indications associated with the travel route can be generated.[[Category:G01C21/34]][[Category:G06Q10/047]][[Category:CPC_G01C21/3476]][[Category:google llc]]



[[20240230354. Constrained Navigation and Route Planning_simplified_abstract_(google llc)]]

Inventor(s): Yan Mayster of Aurora CO (US) for google llc

IPC Code(s): G01C21/34

CPC Code(s): G01C21/3476




Abstract: a computing system determines, for each of a plurality of routes that respectively connect a starting location to a destination, one or more travel costs associated with travelling from the starting location to the destination, and determines, for each of the plurality of routes, one or more convenience costs associated with an availability of one or more facilities which are located away from the plurality of routes. the computing system further determines, based on the one or more travel costs and the one or more convenience costs, a first route from among the plurality of routes that is associated with a lowest combination of the one or more travel costs and the one or more convenience costs, and provides, to a computing device, route data associated with the first route for controlling one or more vehicle systems associated with navigating a vehicle.[[Category:G01C21/34]][[Category:CPC_G01C21/3476]][[Category:google llc]]



[[20240230358. Flexible Navigation and Route Generation_simplified_abstract_(google llc)]]

Inventor(s): Matthew Sharifi of Kilchberg  (CH) for google llc, Victor Carbune of Zurich  (CH) for google llc

IPC Code(s): G01C21/36, G01C21/34

CPC Code(s): G01C21/3617




Abstract: methods, systems, devices, and tangible non-transitory computer readable media for navigation are provided. the disclosed technology can include accessing navigation data that includes information associated with a navigation request from a user. based on the navigation data, a determination of whether the navigation request indicates a specific location or a deferred travel time can be made. based on the navigation request, one or more locations and one or more travel times associated with fulfilling the navigation request can be determined. the one or more locations can be based on whether the navigation request indicates a specific location. the one or more travel times can be based on whether the navigation request indicates a deferred travel time. furthermore, output including a time window of the one or more travel times for the user to travel to at least one of the one or more locations can be generated.[[Category:G01C21/36]][[Category:G01C21/34]][[Category:CPC_G01C21/3617]][[Category:google llc]]



[[20240231100. METHODS AND APPARATUSES FOR IMPLEMENTING VARIED OPTICAL GRATING GEOMETRIES IN AN AUGMENTED REALITY DISPLAY_simplified_abstract_(google llc)]]

Inventor(s): Wei Jin of Saratoga CA (US) for google llc, Joseph Daniel Lowney of Tucson AZ (US) for google llc, Lu Tian of Palo Alto CA (US) for google llc, Qinglan Huang of Mountain View CA (US) for google llc, Thomas Mercier of Weston FL (US) for google llc

IPC Code(s): G02B27/01, G02B6/34

CPC Code(s): G02B27/0172




Abstract: an augmented-reality (ar) eyewear display utilizes an optical waveguide having multi-layered optical gratings in a repeating arrangement. the optical gratings include varying depths, slope angles, lengths, and/or widths in order to tune the gratings to provide an improved ar eyewear display. by using the different configurations of two-dimensional or three-dimensional gratings disclosed herein in a waveguide of an ar eyewear display, optical characteristics of the waveguide are optimized to provide, e.g., high resolution and/or contrast, high display uniformity, high input coupling efficiency, and/or high output coupling efficiency. accordingly, in some embodiments, aspects of the present disclosure enable lower-power ar eyewear displays to produce the same quality of display of a higher-power conventional ar eyewear display waveguide.[[Category:G02B27/01]][[Category:G02B6/34]][[Category:CPC_G02B27/0172]][[Category:google llc]]



[[20240231221. FABRICATION OF NANOIMPRINT WORKING STAMPS WITH COMBINED PATTERNS FROM MULTIPLE MASTER STAMPS_simplified_abstract_(google llc)]]

Inventor(s): Lu Tian of Palo Alto CA (US) for google llc, Wei Jin of Saratoga CA (US) for google llc, Thomas Mercier of Weston FL (US) for google llc

IPC Code(s): G03F7/00, G02B6/34, G02B27/01, G03F7/40

CPC Code(s): G03F7/0002




Abstract: a method for fabricating a working stamp for forming surface gratings in a waveguide workpiece includes performing a series of step lithography processes using a series of master stamps so as to form a working stamp having a first surface having a plurality of surface gratings formed therein. each step lithography process includes pressing a master stamp of the series of master stamps into a material layer of a working stamp workpiece at a corresponding region of the material layer, the master stamp having a plurality of surface grating patterns formed thereon. each step lithography process further includes applying ultraviolet light to the corresponding region to locally cure the material layer at the corresponding region and detaching the master stamp from the material of the working stamp after applying the ultraviolet light.[[Category:G03F7/00]][[Category:G02B6/34]][[Category:G02B27/01]][[Category:G03F7/40]][[Category:CPC_G03F7/0002]][[Category:google llc]]



[[20240231505. Facilitating Ambient Computing Using a Radar System_simplified_abstract_(google llc)]]

Inventor(s): Eiji Hayashi of Cupertino CA (US) for google llc, Jaime Lien of Mountain View CA (US) for google llc, Nicholas Edward Gillian of Palo Alto CA (US) for google llc, Andrew C. Felch of Palo Alto CA (US) for google llc, Jin Yamanaka of Mountain View CA (US) for google llc, Blake Charles Jacquot of San Carlos CA (US) for google llc

IPC Code(s): G06F3/01, H04L27/10

CPC Code(s): G06F3/017




Abstract: techniques and apparatuses are described that facilitate ambient computing using a radar system. compared to other smart devices that rely on a physical user interface, a smart device with a radar system can support ambient computing by providing an eye-free interaction and less cognitively demanding gesture-based user interface. the radar system can be designed to address a variety of challenges associated with ambient computing, including power consumption, environmental variations, background noise, size, and user privacy. the radar system uses an ambient-computing machine-learned module to quickly recognize gestures performed by a user up to at least two meters away. the ambient-computing machine-learned module is trained to filter background noise and have a sufficiently low false positive rate to enhance the user experience.[[Category:G06F3/01]][[Category:H04L27/10]][[Category:CPC_G06F3/017]][[Category:google llc]]



[[20240231596. REAL-TIME VIDEO DISCOVERY-FRIENDLY USER EXPERIENCE FOR MOBILE DEVICES_simplified_abstract_(google llc)]]

Inventor(s): Olayinka Sylvia Nguessan of San Francisco CA (US) for google llc, Samuel Edward Russell, JR. of San Francisco CA (US) for google llc, Christopher Griffin of San Francisco CA (US) for google llc

IPC Code(s): G06F3/0485, G06F3/04817, G06F3/04842, G06F16/738, G06F16/74

CPC Code(s): G06F3/0485




Abstract: a plurality of video items comprising a first subset of video items and a second subset of video items are received at a client device. a graphical user interface comprising a scrollable area displaying the first subset of video items and a focus area displaying one of the plurality of video items is presented. preview of video items of the second subset of video items is provided in or adjacent to the focus area in the gui on the client device prior to receiving a scrolling request of a user of the client device to cause the second subset of video items to become visible in the first scrollable area. video items of the second subset of video items are presented in the preview as gui elements each comprising an icon representing a corresponding video item.[[Category:G06F3/0485]][[Category:G06F3/04817]][[Category:G06F3/04842]][[Category:G06F16/738]][[Category:G06F16/74]][[Category:CPC_G06F3/0485]][[Category:google llc]]



[[20240231607. Predictive Input Interface Having Improved Robustness for Processing Low Precision Inputs_simplified_abstract_(google llc)]]

Inventor(s): Zebedee Pedersen of East Sussex  (GB) for google llc

IPC Code(s): G06F3/04886, G06F3/01, G06F3/04842

CPC Code(s): G06F3/04886




Abstract: the present disclosure is directed to prediction and assistive techniques using a multi-region graphical keyboard interface. in particular, the system can present, on a display of a computing device, a graphical keyboard having a plurality of key regions. the plurality of key regions can include a first key region having a first set of keys and a second key region having a second set of keys. additionally, the system can receive a first input selecting a first selected region from the plurality of key regions. moreover, the system can determine, based at least in part on the first input, a first suggestion and a second suggestion. furthermore, in response to the first input, the system can present, on the display of the computing device, an updated graphical keyboard having the plurality of key regions and a suggestion region. the suggestion region includes the first suggestion and the second suggestion.[[Category:G06F3/04886]][[Category:G06F3/01]][[Category:G06F3/04842]][[Category:CPC_G06F3/04886]][[Category:google llc]]



[[20240231667. Heterogeneous ML Accelerator Cluster with Flexible System Resource Balance_simplified_abstract_(google llc)]]

Inventor(s): Sheng Li of Cupertino CA (US) for google llc, Sridhar Lakshmanamurthy of Sunnyvale CA (US) for google llc, Norman Paul Jouppi of Palo Alto CA (US) for google llc, Martin Guy Dixon of Portland OR (US) for google llc, Daniel Stodolsky of Cambridge MA (US) for google llc, Quoc V. Le of Sunnyvale CA (US) for google llc, Liqun Cheng of Palo Alto CA (US) for google llc, Erik Karl Norden of San Jose CA (US) for google llc, Parthasarathy Ranganathan of San Jose CA (US) for google llc

IPC Code(s): G06F3/06

CPC Code(s): G06F3/0647




Abstract: aspects of the disclosure are directed to a heterogeneous machine learning accelerator system with compute and memory nodes connected by high speed chip-to-chip interconnects. while existing remote/disaggregated memory may require memory expansion via remote processing units, aspects of the disclosure add memory nodes into machine learning accelerator clusters via the chip-to-chip interconnects without needing assistance from remote processing units to achieve higher performance, simpler software stack, and/or lower cost. the memory nodes may support prefetch and intelligent compression to enable the use of low cost memory without performance degradation.[[Category:G06F3/06]][[Category:CPC_G06F3/0647]][[Category:google llc]]



[[20240231765. MACHINE-LEARNED MODELS FOR GENERATING CODE SNIPPETS WITH PREDICTED PLACEHOLDERS FOR OPTIMIZING SOFTWARE DEVELOPMENT_simplified_abstract_(google llc)]]

Inventor(s): Daniel Dun-ning Woo Johnson of Toronto  (CA) for google llc, Daniel Stefan Tarlow of Montréal  (CA) for google llc, Maxim Tabachnyk of Munich  (DE) for google llc, Marc Hatcher Rasi of Sunnyvale CA (US) for google llc, Jacob Austin of New York NY (US) for google llc, Hassan Abolhassani of Palo Alto CA (US) for google llc, Jacob Hanson Hegna of Minneapolis MN (US) for google llc

IPC Code(s): G06F8/33

CPC Code(s): G06F8/33




Abstract: systems and methods of the present disclosure are directed to a method for machine- learned code segment prediction for optimizing software development. the method includes obtaining an incomplete segment of code. the method includes processing the incomplete segment of code with a machine-learned code prediction model to obtain a sampled set of segment completion predictions that include code that completes the incomplete segment of code. the method includes determining an aggregated segment completion prediction from the sampled set of segment completion predictions. the method includes replacing a portion of the aggregated segment completion prediction with an input field, wherein the portion of the aggregated segment completion prediction is associated with a degree of certainty less than a threshold degree of certainty.[[Category:G06F8/33]][[Category:CPC_G06F8/33]][[Category:google llc]]



[[20240231819. NEURAL NETWORK COMPUTE TILE_simplified_abstract_(google llc)]]

Inventor(s): Olivier Temam of Antony  (FR) for google llc, Ravi Narayanaswami of San Jose CA (US) for google llc, Harshit Khaitan of San Jose CA (US) for google llc, Dong Hyuk Woo of San Jose CA (US) for google llc

IPC Code(s): G06F9/30, G06F9/38, G06F13/28, G06N3/04, G06N3/045, G06N3/063

CPC Code(s): G06F9/3001




Abstract: a computing unit is disclosed, comprising a first memory bank for storing input activations and a second memory bank for storing parameters used in performing computations. the computing unit includes at least one cell comprising at least one multiply accumulate (“mac”) operator that receives parameters from the second memory bank and performs computations. the computing unit further includes a first traversal unit that provides a control signal to the first memory bank to cause an input activation to be provided to a data bus accessible by the mac operator. the computing unit performs one or more computations associated with at least one element of a data array, the one or more computations being performed by the mac operator and comprising, in part, a multiply operation of the input activation received from the data bus and a parameter received from the second memory bank.[[Category:G06F9/30]][[Category:G06F9/38]][[Category:G06F13/28]][[Category:G06N3/04]][[Category:G06N3/045]][[Category:G06N3/063]][[Category:CPC_G06F9/3001]][[Category:google llc]]



[[20240231846. Managing Multi-Single-Tenant SaaS Services_simplified_abstract_(google llc)]]

Inventor(s): Roy Peterkofsky of Mountain View CA (US) for google llc, William Earl of Mountain View CA (US) for google llc, Martin Taillefer of Redmond WA (US) for google llc, Michael Dahlin of Bellevue WA (US) for google llc, Chandra Prasad of Mountain View CA (US) for google llc, Jaroslaw Kowalski of Redmond WA (US) for google llc, Anna Berenberg of Saratoga CA (US) for google llc, Kristian Kennaway of Buckhurst Hill Essex  (GB) for google llc, Alexander Mohr of Seattle WA (US) for google llc, Jaidev Haridas of Mountain View CA (US) for google llc

IPC Code(s): G06F9/445, G06F8/71, G06F9/48, G06F9/50

CPC Code(s): G06F9/44505




Abstract: a system comprises data processing hardware and memory hardware. the memory hardware is in communication with the data processing hardware, and stores instructions that, when executed on the data processing hardware, cause the data processing hardware to perform a plurality of operations. in some examples, one of the operations may include receiving instance management configuration data for a single-tenant software-as-a-service (saas) application. another operation may include further include receiving an image of the single-tenant saas application. yet another operation can include generating, by the control plane manager, a control plane based on the instance management configuration data. the control plane is configured to create multiple instances of the single-tenant saas application based on the received image, and to manage the instances of the single-tenant saas application based on the received instance management configuration data. another operation may include executing the control plane on the data processing hardware.[[Category:G06F9/445]][[Category:G06F8/71]][[Category:G06F9/48]][[Category:G06F9/50]][[Category:CPC_G06F9/44505]][[Category:google llc]]



[[20240231862. FACILITATING USER DEVICE AND/OR AGENT DEVICE ACTIONS DURING A COMMUNICATION SESSION_simplified_abstract_(google llc)]]

Inventor(s): Robin Dua of San Francisco CA (US) for google llc, Andrew Tomkins of Menlo Park CA (US) for google llc, Sujith Ravi of Santa Clara CA (US) for google llc

IPC Code(s): G06F9/451, G06F3/0481, G06F40/20, G06F40/30, G06N20/00, H04L67/60

CPC Code(s): G06F9/453




Abstract: facilitating user device and/or agent device actions during a communication session. an interactive communications system provides outputs that are tailored to enhance the functionality of the communication session, reduce the number of dialog “turns” of the communications session and/or the number of user inputs to devices involved in the session, and/or otherwise mitigate consumption of resources during the communication session. in various implementations, the communication session involves user device(s) of a user, agent device(s) of an agent, and the interactive system. the interactive communications system can analyze various communications from the user device(s) and/or agent device(s) during a communication session in which the user directs various communications to the agent, and in which the agent optionally directs various communications to the user. the interactive communications system provides action performance element(s) and/or other output(s) that are each specific to a corresponding current intent and corresponding current action of the communication session.[[Category:G06F9/451]][[Category:G06F3/0481]][[Category:G06F40/20]][[Category:G06F40/30]][[Category:G06N20/00]][[Category:H04L67/60]][[Category:CPC_G06F9/453]][[Category:google llc]]



[[20240231943. Bin Packing_simplified_abstract_(google llc)]]

Inventor(s): Md Ehtesamul Haque of Santa Clara CA (US) for google llc, Thomas John Chestna of Middleborough MA (US) for google llc, Samuel Justin Smith of Mountain View CA (US) for google llc, Pedro Daniel Valenzuela Salvatierra of Santa Clara CA (US) for google llc, Olivier Robert Sevin of Winston-Salem NC (US) for google llc

IPC Code(s): G06F9/50, G06F9/455

CPC Code(s): G06F9/5055




Abstract: a system and method for assigning a workload to one of a plurality of candidate host machines of a computing environment. the method may include receiving a request to schedule a workload, selecting a virtual machine type for executing the workload, for each candidate host machine of the plurality of candidate host machines, determining an expected waste score indicating a likelihood of resources at the candidate host machine remaining unused if the virtual machine type is assigned to the candidate host machine, selecting the candidate host machine for which the expected waste score is the lowest, and assigning the workload to the selected candidate host machine.[[Category:G06F9/50]][[Category:G06F9/455]][[Category:CPC_G06F9/5055]][[Category:google llc]]



[[20240231980. Memory Error Prevention by Proactive Memory Poison Recovery_simplified_abstract_(google llc)]]

Inventor(s): Jue Wang of Redmond WA (US) for google llc, Gregory Thelen of Mountain View CA (US) for google llc, Andrew Chirstopher Walton of Rocklin CA (US) for google llc, Yi Cao of San Jose CA (US) for google llc, James Houghton of Seattle WA (US) for google llc

IPC Code(s): G06F11/07, G06F9/455

CPC Code(s): G06F11/0712




Abstract: the disclosed technology provides techniques, systems, and apparatus for proactively detecting, containing, and recovering from uncorrectable memory errors in distributed computing environment. an aspect of the disclosed technology includes scanning, by a scanner of a host machine, memory of the host machine for errors. after the scanner detects an error, the scanner may generate an error notification. the scanner may transmit the error notification to one or more processors of the host machine to implement mitigation techniques.[[Category:G06F11/07]][[Category:G06F9/455]][[Category:CPC_G06F11/0712]][[Category:google llc]]



[[20240232177. Automatic Generation of Training and Testing Data for Machine-Learning Models_simplified_abstract_(google llc)]]

Inventor(s): Madhav Datt of Mountain View CA (US) for google llc, Sukriti Ramesh of Bengaluru  (IN) for google llc

IPC Code(s): G06F16/242, G06N20/00

CPC Code(s): G06F16/2423




Abstract: provided are computing systems, methods, and platforms for generating training and testing data for machine-learning models. the operations can include receiving signal extraction information that has instructions to query a data store. additionally, the operations can include accessing, using structured query language (sql) code generated based on the signal extraction information, raw data from the data store. moreover, the operations can include processing the raw data using signal configuration information to generate a plurality of signals. the signal configuration information can have instructions on how to generate the plurality of signals from the raw data. furthermore, the operations can include joining, using sql code, the plurality of signals with a first label source to generate training data and testing data. subsequently, the operations can include processing the training data and the testing data to generate the input data. the input data being an ingestible for a machine-learning pipeline.[[Category:G06F16/242]][[Category:G06N20/00]][[Category:CPC_G06F16/2423]][[Category:google llc]]



[[20240232253. Computing Systems and Methods for Cataloging, Retrieving, and Organizing User-Generated Content Associated with Objects_simplified_abstract_(google llc)]]

Inventor(s): Ariel Gilder of Fair Lawn NJ (US) for google llc, Piotr Zielinski of New Providence NJ (US) for google llc, Alexandros Panagopoulos of Leonia NJ (US) for google llc

IPC Code(s): G06F16/58, G06F16/55, G06F16/583, G06F16/587, G06N3/04

CPC Code(s): G06F16/5866




Abstract: the present disclosure provides computing systems and methods for cataloging, retrieving, and/or organizing user-generated content associated with objects. aspects of the disclosure are directed to a systems and methods which utilize computers to enable users to interact with libraries of user-generated content associated with cataloged objects. for example, a user can capture one or more images of a real-world object, label or otherwise annotate the object with various types of user-generated content and organize the object and its associated content into one or more libraries. the user-generated content can then be provided to other users upon the receipt of images of the same object or an object displaying similar features.[[Category:G06F16/58]][[Category:G06F16/55]][[Category:G06F16/583]][[Category:G06F16/587]][[Category:G06N3/04]][[Category:CPC_G06F16/5866]][[Category:google llc]]



[[20240232272. ANALYZING WEB PAGES TO FACILITATE AUTOMATIC NAVIGATION_simplified_abstract_(google llc)]]

Inventor(s): Gökhan Bakir of Zurich  (CH) for google llc, Andre Elisseeff of Basel  (CH) for google llc, Torsten Marek of Zurich  (CH) for google llc, João Paulo Pagaime da Silva of Adliswil  (CH) for google llc, Mathias Carlen of Zurich  (CH) for google llc, Dana Ritter of Horgen  (CH) for google llc, Lukasz Suder of Zurich  (CH) for google llc, Ernest Galbrun of Mulhouse Haut-Rhin  (FR) for google llc, Matthew Stokes of Langnau am Albis  (CH) for google llc, Marcin Nowak-Przygodzki of Bäch  (CH) for google llc, Mugurel-Ionut Andreica of Adliswil  (CH) for google llc, Marius Dumitran of Bucuresti  (RO) for google llc

IPC Code(s): G06F16/9535, G06F16/9032

CPC Code(s): G06F16/9535




Abstract: implementations are described herein for analyzing existing interactive web sites to facilitate automatic engagement with those web sites, e.g., by automated assistants or via other user interfaces, with minimal effort from the hosts of those websites. for example, in various implementations, techniques described herein may be used to abstract, validate, maintain, generalize, extend and/or distribute individual actions and “traces” of actions that are useable to navigate through various interactive websites. additionally, techniques are described herein for leveraging these actions and/or traces to automate aspects of interaction with a third party website. for example, in some implementations, techniques described herein may enable users to engage with an automated assistant (via a spoken or typed dialog session) to interact with the third party website without requiring the user to visually interact with the third party web site directly and without requiring the third party to implement their own third party agent.[[Category:G06F16/9535]][[Category:G06F16/9032]][[Category:CPC_G06F16/9535]][[Category:google llc]]



[[20240232273. Cluster Search on Maps Applications_simplified_abstract_(google llc)]]

Inventor(s): Ovidiu Aurelian Pana of Lakewood CO (US) for google llc

IPC Code(s): G06F16/9537, G06F16/28, G06F16/29

CPC Code(s): G06F16/9537




Abstract: methods, systems, and apparatus, including computer-readable storage media for clustering points of interest (pois). a method includes receiving, by one or more processors, a search query for a collection of pois. the one or more processors may identify one or more locations for each poi in the collection of pois. based on the one or more locations for each poi in the collection of pois, the one or more processors may determine the distances between each poi. based on the distances between each poi, the one or more processors may determine one or more clusters of pois. the one or more processors may transmit clustered location data, the clustered location data including, for each of the one or more clusters of pois, the location data of each poi in the cluster.[[Category:G06F16/9537]][[Category:G06F16/28]][[Category:G06F16/29]][[Category:CPC_G06F16/9537]][[Category:google llc]]



[[20240232322. VERIFYING DEVICE AND APPLICATION INTEGRITY_simplified_abstract_(google llc)]]

Inventor(s): Gang Wang of Frederick MD (US) for google llc, Marcel M. Moti Yung of New York NY (US) for google llc, David Bruce Tumer of Mountain View CA (US) for google llc

IPC Code(s): G06F21/44, G06F21/45, G06F21/56, H04L9/32

CPC Code(s): G06F21/44




Abstract: this disclosure relates to using trust tokens to verify the integrity of devices and applications from which data is received. in one aspect, a method includes receiving, from a client device, a request for one or more trust tokens. the request includes at least one of one or more device-level fraud detection signals obtained from the client device or data representing code of an application that initiated the request. the request also includes a respective nonce for each of the one or more trust tokens. a determination is made, based on at least one of the one or more device-level fraud signals or the data representing the code of the application, to issue the one or more trust tokens to the client device. each trust token is generated using the nonce for the trust token. the one or more trust tokens are provided to the client device.[[Category:G06F21/44]][[Category:G06F21/45]][[Category:G06F21/56]][[Category:H04L9/32]][[Category:CPC_G06F21/44]][[Category:google llc]]



[[20240232361. Securely Provisioning Secrets in Authentication Devices_simplified_abstract_(google llc)]]

Inventor(s): Vidya Satyamsetti of Bothell WA (US) for google llc

IPC Code(s): G06F21/57, G06F21/44

CPC Code(s): G06F21/572




Abstract: the present disclosure provides for increased security of root of trust (rot) chips by preventing malicious provisioning. unique device secrets (uds) can only be provisioned securely by trustworthy hardware or trustworthy firmware. entities other than the trustworthy hardware and trustworthy firmware do not have access to a composite device identifier (cdi) generated using the uds and firmware measurements.[[Category:G06F21/57]][[Category:G06F21/44]][[Category:CPC_G06F21/572]][[Category:google llc]]



[[20240232423. PRIVACY-PRESERVING CROSS-DOMAIN EXPERIMENTAL GROUP PARTITIONING AND MONITORING_simplified_abstract_(google llc)]]

Inventor(s): Gang Wang of Frederick MD (US) for google llc, Marcel M. Moti Yung of New York NY (US) for google llc, Timothy David Lambert of Ontario  (CA) for google llc

IPC Code(s): G06F21/62, G06F21/10

CPC Code(s): G06F21/6245




Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for privacy-preserving cross-domain experiment monitoring are described. in one aspect, a method includes receiving, by a first server of a mpc system, a request for digital content including a first secret share of an application instance identifier that identifies the application instance associated with the device. the first server conducts, in collaboration with a second server of the secure mpc system, a privacy-preserving selection process to select a winning digital component from a set of digital components. each digital component has a corresponding unique experiment identifier and unique control identifier. a first secret share representing the winning digital component is generated. a response is generated and includes the first secret share of the selection result and data representing whether the application is in the experiment group or a control group for each digital component.[[Category:G06F21/62]][[Category:G06F21/10]][[Category:CPC_G06F21/6245]][[Category:google llc]]



[[20240232438. Using Memory Protection Data_simplified_abstract_(google llc)]]

Inventor(s): Yanru Li of San Diego CA (US) for google llc, Deepti Vijayalakshmi Sriramagiri of San Jose CA (US) for google llc

IPC Code(s): G06F21/64

CPC Code(s): G06F21/64




Abstract: the present disclosure describes techniques and apparatuses that are directed to using memory protection data within a computing device. techniques include allocating regions of a memory for storing application data and protection data. techniques also include creating a bitmap having bit values corresponding to memory blocks within the allocated regions. the one or more bit values can be indicative of whether application data and/or protection data are present in a memory block. the techniques and apparatuses can enable memory protection, such as memory security (e.g., encryption) and memory safety (e.g., error correction code (ecc) usage), to be efficiently used while permitting discontiguous memory allocations and without substantial operating system modification.[[Category:G06F21/64]][[Category:CPC_G06F21/64]][[Category:google llc]]



[[20240232505. TEXT LAYOUT INTERPRETATION USING EYE GAZE DATA_simplified_abstract_(google llc)]]

Inventor(s): Alexander James Faaborg of Mountain View CA (US) for google llc, Brett Barros of San Mateo CA (US) for google llc

IPC Code(s): G06F40/106, G06F3/01, G06F18/21, G06F18/214, G06F40/30, G06V30/10, G06V30/412, G10L13/02

CPC Code(s): G06F40/106




Abstract: gaze data collected from eye gaze tracking performed while training text was read may be used to train at least one layout interpretation model. in this way, the at least one layout interpretation model may be trained to determine current text that includes words arranged according to a layout, process the current text with the at least one layout interpretation model to determine the layout, and output the current text with the words arranged according to the layout.[[Category:G06F40/106]][[Category:G06F3/01]][[Category:G06F18/21]][[Category:G06F18/214]][[Category:G06F40/30]][[Category:G06V30/10]][[Category:G06V30/412]][[Category:G10L13/02]][[Category:CPC_G06F40/106]][[Category:google llc]]



[[20240232546. METHOD FOR SPEECH-TO-SPEECH CONVERSION_simplified_abstract_(google llc)]]

Inventor(s): Oleg RYBAKOV of Redmond WA (US) for google llc, Fadi BIADSY of Sandyston NJ (US) for google llc

IPC Code(s): G06F40/58, G10L15/02, G10L15/30, G10L19/032

CPC Code(s): G06F40/58




Abstract: the present disclosure relates to a streaming speech-to-speech conversion model, where an encoder runs in real time while a user is speaking, then after the speaking stops, a decoder generates output audio in real time. a streaming-based approach produces an acceptable delay with minimal loss in conversion quality when compared to other non-streaming server-based models. a hybrid model approach for combines look-ahead in the encoder and a non-causal stacker with non-causal self-attention.[[Category:G06F40/58]][[Category:G10L15/02]][[Category:G10L15/30]][[Category:G10L19/032]][[Category:CPC_G06F40/58]][[Category:google llc]]



[[20240232572. NEURAL NETWORKS WITH ADAPTIVE STANDARDIZATION AND RESCALING_simplified_abstract_(google llc)]]

Inventor(s): Qifei WANG of   () for google llc, Junjie KE of   () for google llc, Feng YANG of   () for google llc, Boqing GONG of   () for google llc, Xinjie FAN of   () for google llc

IPC Code(s): G06N3/04, G06N3/094

CPC Code(s): G06N3/04




Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing a network input using a neural network to generate a network output. the neural network includes a normalization block that is between a first neural network layer and a second neural network layer in the neural network. processing the network input using the neural network comprises: receiving a first layer output from the first neural network layer; processing data derived from the first layer output using standardization neural network layers of the normalization block to generate one or more adaptive standardization values; standardizing the first layer output using the adaptive standardization values to generate a standardized first layer output; generating a normalization block output from the standardized first layer output; and providing the normalization block output as an input to the second neural network layer.[[Category:G06N3/04]][[Category:G06N3/094]][[Category:CPC_G06N3/04]][[Category:google llc]]



[[20240232589. Automated, Constraints-Dependent Machine Learning Model Thresholding Mechanisms_simplified_abstract_(google llc)]]

Inventor(s): Madhav Datt of Mountain View CA (US) for google llc, Surabhi Choudhary of Chennai  (IN) for google llc, Nikhil Shirish Ketkar of Bengaluru  (IN) for google llc

IPC Code(s): G06N3/048, G06N3/08

CPC Code(s): G06N3/048




Abstract: provided are computing systems, methods, and platforms for a discrete-valued output classification. the operations can include obtaining a candidate threshold value for a first slice in a plurality of data slices. additionally, the operations can include calculating, using a candidate machine-learned model and the candidate threshold value, a first performance value associated with a first risk tolerance value. moreover, the operations can include determining, based on the first performance value, that a safeguard criterion for the first slice has not been satisfied. in response to the determination that the safeguard criterion for the first slice has not been satisfied, the operations can include performing a tradeoff logic operation to determine the final threshold value. subsequently, the operations can include determining, using the candidate machine-learned model, whether input data is authentic based on the final threshold value.[[Category:G06N3/048]][[Category:G06N3/08]][[Category:CPC_G06N3/048]][[Category:google llc]]



[[20240232594. GENERATING AND GLOBALLY TUNING APPLICATION-SPECIFIC MACHINE LEARNING ACCELERATORS_simplified_abstract_(google llc)]]

Inventor(s): Yang Yang of Mountain View CA (US) for google llc, Claudionor Jose Nunes Coelho, Jr. of Redwood City CA (US) for google llc, Hao Zhuang of San Jose CA (US) for google llc, Aki Oskari Kuusela of Palo Alto CA (US) for google llc

IPC Code(s): G06N3/063, G06N3/0464, G06N3/082

CPC Code(s): G06N3/063




Abstract: methods, systems, and apparatus, including computer-readable media, are described for globally tuning and generating ml hardware accelerators. a design system selects an architecture representing a baseline processor configuration. an ml cost model of the system generates performance data about the architecture at least by modeling how the architecture executes computations of a neural network that includes multiple layers. based on the performance data, the architecture is dynamically tuned to satisfy a performance objective when the architecture implements the neural network and executes machine-learning computations for a target application. in response to dynamically tuning the architecture, the system generates a configuration of an ml accelerator that specifies customized hardware configurations for implementing each of the multiple layers of the neural network.[[Category:G06N3/063]][[Category:G06N3/0464]][[Category:G06N3/082]][[Category:CPC_G06N3/063]][[Category:google llc]]



[[20240232598. GENERAL PADDING SUPPORT FOR CONVOLUTION ON SYSTOLIC ARRAYS_simplified_abstract_(google llc)]]

Inventor(s): David Alexander Majnemer of Mountain View CA (US) for google llc, Blake Alan Hechtman of Mountain View CA (US) for google llc, Bjarke Hammersholt Roune of Mountain View CA (US) for google llc

IPC Code(s): G06N3/063, G06F17/15, G06F17/16, G06F30/18, G06F30/20, G06F30/27, G06F30/367, G06N3/045, G06N3/086, G06N3/10

CPC Code(s): G06N3/063




Abstract: methods and systems, including computer programs encoded on a computer storage medium. in one aspect, a method includes the actions of receiving a request to perform convolutional computations for a neural network on a hardware circuit having a matrix computation unit, the request specifying the convolutional computation to be performed on a feature tensor and a filter and padding applied to the feature tensor prior to performing the convolutional computation; and generating instructions that when executed by the hardware circuit cause the hardware circuit to perform operations comprising: transferring feature tensor data from a main memory of the hardware circuit to a scratchpad memory of the hardware circuit; and repeatedly performing the following operations: identifying a current subset of the feature tensor; and determining whether a memory view into the scratchpad memory for the current subset is consistent with a memory view of the current subset in the main memory.[[Category:G06N3/063]][[Category:G06F17/15]][[Category:G06F17/16]][[Category:G06F30/18]][[Category:G06F30/20]][[Category:G06F30/27]][[Category:G06F30/367]][[Category:G06N3/045]][[Category:G06N3/086]][[Category:G06N3/10]][[Category:CPC_G06N3/063]][[Category:google llc]]



[[20240232603. ACCELERATING NEURAL NETWORKS IN HARDWARE USING INTERCONNECTED CROSSBARS_simplified_abstract_(google llc)]]

Inventor(s): Pierre-Luc Cantin of Palo Alto CA (US) for google llc, Olivier Temam of Antony  (FR) for google llc

IPC Code(s): G06N3/065, G06F9/50, G06F17/16, G06N3/04, G06N3/08

CPC Code(s): G06N3/065




Abstract: a computing unit for accelerating a neural network is disclosed. the computing unit include an input unit that includes a digital-to-analog conversion unit and an analog-to-digital conversion unit that is configured to receive an analog signal from the output of a last interconnected analog crossbar circuit of a plurality of analog crossbar circuits and convert the second analog signal into a digital output vector, and a plurality of interconnected analog crossbar circuits that include the first interconnected analog crossbar circuit and the last interconnected crossbar circuits, wherein a second interconnected analog crossbar circuit of the plurality of interconnected analog crossbar circuits is configured to receive a third analog signal from another interconnected analog crossbar circuit of the plurality of interconnected crossbar circuits and perform one or more operations on the third analog signal based on the matrix weights stored by the crosspoints of the second interconnected analog crossbar.[[Category:G06N3/065]][[Category:G06F9/50]][[Category:G06F17/16]][[Category:G06N3/04]][[Category:G06N3/08]][[Category:CPC_G06N3/065]][[Category:google llc]]



[[20240232637. Method for Training Large Language Models to Perform Query Intent Classification_simplified_abstract_(google llc)]]

Inventor(s): Krishna Pragash Srinivasan of Union City CA (US) for google llc, Michael Bendersky of Cupertino CA (US) for google llc, Anupam Samanta of Mountain View CA (US) for google llc, Lingrui Liao of Foster City CA (US) for google llc, Luca Bertelli of Redwood City CA (US) for google llc, Ming-Wei Chang of Redmond WA (US) for google llc, Iftekhar Naim of San Jose CA (US) for google llc, Siddhartha Brahma of San Jose CA (US) for google llc, Siamak Shakeri of New York NY (US) for google llc, Hongkun Yu of Redwood City CA (US) for google llc, John Nham of Fremont CA (US) for google llc, Karthik Raman of Sunnyvale CA (US) for google llc, Raphael Dominik Hoffmann of Los Altos CA (US) for google llc

IPC Code(s): G06N3/0895, G06F16/903, G06F16/93, G06N3/0455

CPC Code(s): G06N3/0895




Abstract: provided are computing systems, methods, and platforms that train query processing models, such as large language models, to perform query intent classification tasks by using retrieval augmentation and multi-stage distillation. unlabeled training examples of queries may be obtained, and a set of the training examples may be augmented with additional feature annotations to generate augmented training examples. a first query processing model may annotate the retrieval augmented queries to generate inferred labels for the augmented training examples. a second query processing model may be trained on the inferred labels, distilling the query processing model that was trained with retrieval augmentation into a non-retrieval augmented query processing model. the second query processing model may annotate the entire set of unlabeled training examples. another stage of distillation may train a third query processing model using the entire set of unlabeled training examples without retrieval augmentation.[[Category:G06N3/0895]][[Category:G06F16/903]][[Category:G06F16/93]][[Category:G06N3/0455]][[Category:CPC_G06N3/0895]][[Category:google llc]]



[[20240232673. METHODS AND APPARATUS FOR PERFORMING PHASE OPERATIONS_simplified_abstract_(google llc)]]

Inventor(s): Craig Gidney of Goleta CA (US) for google llc

IPC Code(s): G06N10/20, G06N10/40, H03K19/20

CPC Code(s): G06N10/20




Abstract: methods, systems, and apparatus for performing phase operations. in one aspect, a method for performing a same phase operation on a first and second qubit using a third qubit prepared in a phased plus state includes: performing a first not operation on the third qubit; computing a controlled adder operation on the first, second and third qubit, comprising encoding the result of the controlled adder operation in a fourth qubit; performing a square of the phase operation on the fourth qubit; uncomputing the controlled adder operation on the first, second and third qubit; performing a cnot operation between the first qubit and the third qubit, wherein the first qubit acts as the control; performing a cnot operation between the second qubit and the third qubit, wherein the second qubit acts as the control; and performing a second not operation on the third qubit.[[Category:G06N10/20]][[Category:G06N10/40]][[Category:H03K19/20]][[Category:CPC_G06N10/20]][[Category:google llc]]



[[20240232686. Portion-Specific Model Compression for Optimization of Machine-Learned Models_simplified_abstract_(google llc)]]

Inventor(s): Yicheng Fan of Mountain View CA (US) for google llc, Jingyue Shen of Santa Clara CA (US) for google llc, Deqiang Chen of San Jose CA (US) for google llc, Peter Shaosen Young of Mountain View CA (US) for google llc, Dana Alon of Mountain View CA (US) for google llc, Erik Nathan Vee of San Mateo CA (US) for google llc, Shanmugasundaram Ravikumar of Piedmont CA (US) for google llc, Andrew Tomkins of Menlo Park CA (US) for google llc

IPC Code(s): G06N20/00

CPC Code(s): G06N20/00




Abstract: systems and methods of the present disclosure are directed to portion-specific compression and optimization of machine-learned models. for example, a method for portion-specific compression and optimization of machine-learned models includes obtaining data descriptive of one or more respective sets of compression schemes for one or more model portions of a plurality of model portions of a machine-learned model. the method includes evaluating a cost function to respectively select one or more candidate compression schemes from the one or more sets of compression schemes. the method includes respectively applying the one or more candidate compression schemes to the one or more model portions to obtain a compressed machine-learned model comprising one or more compressed model portions that correspond to the one or more model portions.[[Category:G06N20/00]][[Category:CPC_G06N20/00]][[Category:google llc]]



[[20240232913. Techniques for Generating Analytics Reports_simplified_abstract_(google llc)]]

Inventor(s): Matthew Thompson Walter of Ladera Ranch CA (US) for google llc, Michael Joseph Valenty of Carlsbad CA (US) for google llc, Sundardas Samuel Dorai-Raj of Campbell CA (US) for google llc, Moshe Lichman of Costa Mesa CA (US) for google llc, Manish Agrawal of Sunnyvale CA (US) for google llc, Joseph Kelly of Brooklyn NY (US) for google llc, Michael Andrew Wallace of San Francisco CA (US) for google llc, Stephen Paul Ganem of Huntington Beach CA (US) for google llc

IPC Code(s): G06Q30/0201, G06Q30/0601

CPC Code(s): G06Q30/0201




Abstract: techniques for generating a report for a website are presented herein. a method can include accessing, by one or more computing devices, a plurality of unidentified events. each event in the plurality of unidentified events can be associated with one or more properties. additionally, the method can calculate, using a machine-learned prediction model, a number of pseudo users associated with the plurality of unidentified events based on an event-to-user-ratio and a total number of unidentified events. moreover, the method can include assigning a first event from the plurality of unidentified events to a first pseudo user based on the one or more properties of the first event. furthermore, the method can include generating the report for the website. the report includes information derived from the first event being assigned to the first pseudo user.[[Category:G06Q30/0201]][[Category:G06Q30/0601]][[Category:CPC_G06Q30/0201]][[Category:google llc]]



[[20240232936. MODEL ORCHESTRATOR_simplified_abstract_(google llc)]]

Inventor(s): Francesco Nerieri of San Francisco CA (US) for google llc, Di-Fa Chang of Cupertino CA (US) for google llc, Lan Huang of Menlo Park CA (US) for google llc, Xinlong Bao of Los Altos CA (US) for google llc

IPC Code(s): G06Q30/0242

CPC Code(s): G06Q30/0244




Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining attributions for unattributed outcomes across different content channels. the method includes receiving, by a model orchestrator, outcome data representing a set of unattributed outcomes, where each unattributed outcome does not have an observed attribution to an exposure of a set of predetermined exposures. the attribution data representing a set of modeled attributions from each outcome model of a plurality of outcome models are received by the model orchestrator, where each set of modeled attribution includes a respective measure between one or more unattributed outcomes and one or more exposures. the respective measures are updated based on one or more criteria for determining one or more updated attributions, where each updated attribution indicates a new attribution of a respective outcome from the set of unattributed outcomes to a corresponding exposure of the set of predetermined exposures.[[Category:G06Q30/0242]][[Category:CPC_G06Q30/0244]][[Category:google llc]]



[[20240232945. MACHINE LEARNING-BASED AUTOMATED TARGETING EXPANSION SYSTEM_simplified_abstract_(google llc)]]

Inventor(s): Bharath Pattabiraman of Santa Clara CA (US) for google llc

IPC Code(s): G06Q30/0251, G06Q30/0201

CPC Code(s): G06Q30/0256




Abstract: systems and methods for assessing the relevancy of search queries are described. a computing device uses various machine learning techniques to determine whether a search query presents an opportunity to display content as well as determine a category for the search query and a content item associated with the category. the computing device additional analyzes an additional search query using a machine learning model with a relaxed threshold to assess relevancy. the computing device captures engagement data associated with a user engaging with the content item and update the machine learning model with the engagement data.[[Category:G06Q30/0251]][[Category:G06Q30/0201]][[Category:CPC_G06Q30/0256]][[Category:google llc]]



[[20240233343. Vector Map Verification_simplified_abstract_(google llc)]]

Inventor(s): Yan Mayster of Aurora CO (US) for google llc, Brian Daniel Shucker of Superior CO (US) for google llc

IPC Code(s): G06V10/776, G06T7/00, G06V10/40, G06V10/764, G06V10/77, G06V20/10

CPC Code(s): G06V10/776




Abstract: methods, systems, devices, and tangible non-transitory computer readable media for vector map verification are provided. the disclosed technology can include accessing image data including information associated with images of a region. the images can be associated with detections of features associated with the images. validation images can be selected from the images. the validation images can include the images that satisfy image quality criteria. for classes associated with the features and based on comparisons of the images to the validation images, confirmation scores can be determined for the images. the confirmation scores can be associated with an extent to which the detections of the images are similar to detections of the validation images. a user interface comprising indications can then be generated based on the confirmation scores. the indications can be associated with an accuracy of the detections of the features in the images.[[Category:G06V10/776]][[Category:G06T7/00]][[Category:G06V10/40]][[Category:G06V10/764]][[Category:G06V10/77]][[Category:G06V20/10]][[Category:CPC_G06V10/776]][[Category:google llc]]



[[20240233373. SYSTEMS, METHODS, AND APPARATUS FOR PROVIDING IMAGE SHORTCUTS FOR AN ASSISTANT APPLICATION_simplified_abstract_(google llc)]]

Inventor(s): Marcin Nowak-Przygodzki of Bäch  (CH) for google llc, Gökhan Bakir of Zurich  (CH) for google llc

IPC Code(s): G06V20/20, G06F3/00, G06F3/01, G06F3/03, G06F3/0481, G06F3/16, G06F9/451, G06F16/58, G06F16/9032, H04N23/63

CPC Code(s): G06V20/20




Abstract: generating and/or utilizing image shortcuts that cause one or more corresponding computer actions to be performed in response to determining that one or more features are present in image(s) from a camera of a computing device of a user (e.g., present in a real-time image feed from the camera). an image shortcut can be generated in response to user interface input, such as a spoken command. for example, the user interface input can direct the automated assistant to perform one or more actions in response to object(s) having certain feature(s) being present in a field of view of the camera. subsequently, when the user directs their camera at object(s) having such feature(s), the assistant application can cause the action(s) to be automatically performed. for example, the assistant application can cause data to be presented and/or can control a remote device in accordance with the image shortcut.[[Category:G06V20/20]][[Category:G06F3/00]][[Category:G06F3/01]][[Category:G06F3/03]][[Category:G06F3/0481]][[Category:G06F3/16]][[Category:G06F9/451]][[Category:G06F16/58]][[Category:G06F16/9032]][[Category:H04N23/63]][[Category:CPC_G06V20/20]][[Category:google llc]]



[[20240233437. Multi-Scale Model Ensemble for Classification of Objects_simplified_abstract_(google llc)]]

Inventor(s): Yaojie Liu of Mountain View CA (US) for google llc, Wen-Sheng Chu of Santa Clara CA (US) for google llc

IPC Code(s): G06V40/16, G06V10/26, G06V10/764

CPC Code(s): G06V40/172




Abstract: provided is a multi-scale model ensemble for detection of objects in images. the model ensemble can be applied, for example, in the context of performing object identification activities, such as positively identifying desired objects in image data or video data using a variety of different crop levels.[[Category:G06V40/16]][[Category:G06V10/26]][[Category:G06V10/764]][[Category:CPC_G06V40/172]][[Category:google llc]]



[[20240233569. Dynamically Adjusting Instructions in an Augmented-Reality Experience_simplified_abstract_(google llc)]]

Inventor(s): Jessica Lee of Brooklyn NY (US) for google llc, David Trotter Oleson of Ruschlikon  (CH) for google llc, Fabian Roth of Zurich  (CH) for google llc, Nils Grimsmo of Wollerau  (CH) for google llc

IPC Code(s): G09B7/04, G06F3/04845, G06F40/205, G06T11/60, G06V10/94, G06V20/70, G06V30/12, G06V30/19

CPC Code(s): G09B7/04




Abstract: systems and methods for augmented-reality tutoring can utilize optical character recognition, natural language processing, and/or augmented-reality rendering for providing real-time notifications for completing a determined task. the systems and methods can include utilizing one or more machine-learned models trained for quantitative reasoning and can include providing a plurality of different user interface elements at different times.[[Category:G09B7/04]][[Category:G06F3/04845]][[Category:G06F40/205]][[Category:G06T11/60]][[Category:G06V10/94]][[Category:G06V20/70]][[Category:G06V30/12]][[Category:G06V30/19]][[Category:CPC_G09B7/04]][[Category:google llc]]



[[20240233683. Smart Camera User Interface_simplified_abstract_(google llc)]]

Inventor(s): Teresa Ko of Los Angeles CA (US) for google llc, Hartwig Adam of Marina del Rey CA (US) for google llc, Mikkel Crone Koser of Copenhagen  (DK) for google llc, Alexei Masterov of Mountain View CA (US) for google llc, Andrews-Junior Kimbembe of San Francisco CA (US) for google llc, Matthew J. Bridges of New Providence NJ (US) for google llc, Paul Chang of New York NY (US) for google llc, David Petrou of Brooklyn NY (US) for google llc, Adam Berenzweig of Brooklyn NY (US) for google llc

IPC Code(s): G09G5/36, G06F3/04817, G06F3/04842, G06F3/14, G06V10/10, G06V20/20, G06V20/30, G06V20/70, H04N23/62, H04N23/63

CPC Code(s): G09G5/363




Abstract: implementations of the present disclosure include actions of receiving image data of an image capturing a scene, receiving data describing one or more entities determined from the scene, the one or more entities being determined from the scene, determining one or more actions based on the one or more entities, each action being provided at least partly based on search results from searching the one or more entities, and providing instructions to display an action interface comprising one or more action elements, each action element being to induce execution of a respective action, the action interface being displayed in a viewfinder.[[Category:G09G5/36]][[Category:G06F3/04817]][[Category:G06F3/04842]][[Category:G06F3/14]][[Category:G06V10/10]][[Category:G06V20/20]][[Category:G06V20/30]][[Category:G06V20/70]][[Category:H04N23/62]][[Category:H04N23/63]][[Category:CPC_G09G5/363]][[Category:google llc]]



[[20240233704. RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION_simplified_abstract_(google llc)]]

Inventor(s): Nobuyuki Morioka of Mountain View CA (US) for google llc, Byungha Chun of Tokyo  (JP) for google llc, Nanxin Chen of Mountain View CA (US) for google llc, Yu Zhang of Mountain View CA (US) for google llc, Yifan Ding of Mountain View CA (US) for google llc

IPC Code(s): G10L13/027

CPC Code(s): G10L13/027




Abstract: a method for residual adapters for few-shot text-to-speech speaker adaptation includes obtaining a text-to-speech (tts) model configured to convert text into representations of synthetic speech, the tts model pre-trained on an initial training data set. the method further includes augmenting the tts model with a stack of residual adapters. the method includes receiving an adaption training data set including one or more spoken utterances spoken by a target speaker, each spoken utterance in the adaptation training data set paired with corresponding input text associated with a transcription of the spoken utterance. the method also includes adapting, using the adaption training data set, the tts model augmented with the stack of residual adapters to learn how to synthesize speech in a voice of the target speaker by optimizing the stack of residual adapters while parameters of the tts model are frozen.[[Category:G10L13/027]][[Category:CPC_G10L13/027]][[Category:google llc]]



[[20240233707. Knowledge Distillation with Domain Mismatch For Speech Recognition_simplified_abstract_(google llc)]]

Inventor(s): Tien-Ju Yang of Mountain View CA (US) for google llc, You-Chi Cheng of Mountain View CA (US) for google llc, Shankar Kumar of New York NY (US) for google llc, Jared Lichtarge of Mountain View CA (US) for google llc, Ehsan Amid of Mountain View CA (US) for google llc, Yuxin Ding of Mountain View CA (US) for google llc, Rajiv Mathews of Sunnyvale CA (US) for google llc, Mingqing Chen of Saratoga CA (US) for google llc

IPC Code(s): G10L15/06, G10L15/197, G10L15/30

CPC Code(s): G10L15/063




Abstract: a method includes receiving distillation data including a plurality of out-of-domain training utterances. for each particular out-of-domain training utterance of the distillation data, the method includes generating a corresponding augmented out-of-domain training utterance, and generating, using a teacher asr model trained on training data corresponding to a target domain, a pseudo-label corresponding to the corresponding augmented out-of-domain training utterance. the method also includes distilling a student asr model from the teacher asr model by training the student asr model using the corresponding augmented out-of-domain training utterances paired with the corresponding pseudo-labels generated by the teacher asr model.[[Category:G10L15/06]][[Category:G10L15/197]][[Category:G10L15/30]][[Category:CPC_G10L15/063]][[Category:google llc]]



[[20240233712. Speech Recognition Biasing_simplified_abstract_(google llc)]]

Inventor(s): Olawale Abiri of Westfield NJ (US) for google llc, Qi Cao of Palo Alto CA (US) for google llc, Dharmeshkumar Jayantilal Mokani of Milpitas CA (US) for google llc

IPC Code(s): G10L15/16, G10L15/183, G10L15/26

CPC Code(s): G10L15/16




Abstract: a method for speech recognition biasing includes receiving, from an application executing on a user device, at a speech service interface, a speech recognition request requesting a transcription of an utterance. the speech recognition request includes audio data encoding the utterance and configuration parameters for biasing a speech recognition model based on context data. the method includes processing, using the speech recognition model, the audio data to generate speech recognition scores for speech elements and determining context scores for the speech elements based on the configuration parameters and the context data. the method includes biasing the speech recognition scores using the context scores. the method also includes determining the transcription for the utterance based on the biased speech recognition scores.[[Category:G10L15/16]][[Category:G10L15/183]][[Category:G10L15/26]][[Category:CPC_G10L15/16]][[Category:google llc]]



[[20240233713. GENERATING AUDIO USING AUTO-REGRESSIVE GENERATIVE NEURAL NETWORKS_simplified_abstract_(google llc)]]

Inventor(s): Andrea Agostinelli of Zurich  (CH) for google llc, Timo Immanuel Denk of Zurich  (CH) for google llc, Antoine Caillon of Paris  (FR) for google llc, Neil Zeghidour of Paris  (FR) for google llc, Jesse Engel of Orinda CA (US) for google llc, Mauro Verzetti of Dübendorf  (CH) for google llc, Christian Frank of Zurich  (CH) for google llc, Zalán Borsos of Zurich  (CH) for google llc, Matthew Sharifi of Kilchberg  (CH) for google llc, Adam Joseph Roberts of Durham NC (US) for google llc, Marco Tagliasacchi of Kilchberg  (CH) for google llc

IPC Code(s): G10L15/16, G06N3/0455, G06N3/0475, G10H1/00, G10L15/06, G10L15/18

CPC Code(s): G10L15/16




Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. one of the methods includes receiving a request to generate an audio signal conditioned on an input; processing the input using an embedding neural network to map the input to one or more embedding tokens; generating a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation and the embedding tokens, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.[[Category:G10L15/16]][[Category:G06N3/0455]][[Category:G06N3/0475]][[Category:G10H1/00]][[Category:G10L15/06]][[Category:G10L15/18]][[Category:CPC_G10L15/16]][[Category:google llc]]



[[20240233727. HOTWORD DETECTION ON MULTIPLE DEVICES_simplified_abstract_(google llc)]]

Inventor(s): Jakob Nicolaus FOERSTER of San Francisco CA (US) for google llc, Alexander H. Gruenstein of Mountain View CA (US) for google llc

IPC Code(s): G10L15/22, G10L15/02, G10L15/08, G10L15/26, G10L15/30, G10L25/03, G10L25/78, G10L25/87

CPC Code(s): G10L15/22




Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for hotword detection on multiple devices are disclosed. in one aspect, a method includes the actions of receiving, by a computing device, audio data that corresponds to an utterance. the actions further include determining a likelihood that the utterance includes a hotword. the actions further include determining a loudness score for the audio data. the actions further include based on the loudness score, determining an amount of delay time. the actions further include, after the amount of delay time has elapsed, transmitting a signal that indicates that the computing device will initiate speech recognition processing on the audio data.[[Category:G10L15/22]][[Category:G10L15/02]][[Category:G10L15/08]][[Category:G10L15/26]][[Category:G10L15/30]][[Category:G10L25/03]][[Category:G10L25/78]][[Category:G10L25/87]][[Category:CPC_G10L15/22]][[Category:google llc]]



[[20240233729. TRANSCRIPTION BASED ON SPEECH AND VISUAL INPUT_simplified_abstract_(google llc)]]

Inventor(s): Xavier Benavides Palos of Beverly Hills CA (US) for google llc

IPC Code(s): G10L15/26, G06V20/40, G10L15/18

CPC Code(s): G10L15/26




Abstract: a method can include receiving audio input of speech, receiving visual input while receiving the audio input, generating a semantic description based on the visual input, and presenting a transcription of the speech based on the audio input and the semantic description.[[Category:G10L15/26]][[Category:G06V20/40]][[Category:G10L15/18]][[Category:CPC_G10L15/26]][[Category:google llc]]



[[20240233732. ALPHANUMERIC SEQUENCE BIASING FOR AUTOMATIC SPEECH RECOGNITION_simplified_abstract_(google llc)]]

Inventor(s): Benjamin Haynor of New York NY (US) for google llc, Petar Aleksic of Jersey City NJ (US) for google llc

IPC Code(s): G10L15/26, G10L15/16, G10L15/193, G10L15/22, G10L15/30

CPC Code(s): G10L15/26




Abstract: speech processing techniques are disclosed that enable determining a text representation of alphanumeric sequences in captured audio data. various implementations include determining a contextual biasing finite state transducer (fst) based on contextual information corresponding to the captured audio data. additional or alternative implementations include modifying probabilities of one or more candidate recognitions of the alphanumeric sequence using the contextual biasing fst.[[Category:G10L15/26]][[Category:G10L15/16]][[Category:G10L15/193]][[Category:G10L15/22]][[Category:G10L15/30]][[Category:CPC_G10L15/26]][[Category:google llc]]



[[20240233735. DETECTING AND SUPPRESSING COMMANDS IN MEDIA THAT MAY TRIGGER ANOTHER AUTOMATED ASSISTANT_simplified_abstract_(google llc)]]

Inventor(s): Matthew Sharifi of Kitchberg  (CH) for google llc, Victor Carbune of Zurich  (CH) for google llc

IPC Code(s): G10L17/22, G06F3/16, G10L19/018, G10L25/51

CPC Code(s): G10L17/22




Abstract: techniques are described herein for detecting and suppressing commands in media that may trigger another automated assistant. a method includes: determining, for each of a plurality of automated assistant devices in an environment that are each executing at least one automated assistant, an active capability of the automated assistant device; initiating playback of digital media by an automated assistant; in response to initiating playback, processing the digital media to identify an audio segment in the digital media that, upon playback, is expected to trigger activation of at least one automated assistant executing on at least one of the plurality of automated assistant devices in the environment, based on the active capability of the at least one of the plurality of automated assistant devices; and in response to identifying the audio segment in the digital media, modifying the digital media to suppress the activation of the at least one automated assistant.[[Category:G10L17/22]][[Category:G06F3/16]][[Category:G10L19/018]][[Category:G10L25/51]][[Category:CPC_G10L17/22]][[Category:google llc]]



[[20240233768. METHODS AND SYSTEMS FOR PRESENTING MEDIA CONTENT WITH MULTIPLE MEDIA ELEMENTS IN AN EDITING ENVIRONMENT_simplified_abstract_(google llc)]]

Inventor(s): Ariel Braunstein of San Francisco CA (US) for google llc, Nicholas Matarese of San Francisco CA (US) for google llc

IPC Code(s): G11B27/031, G06F3/0482, G06F3/0485

CPC Code(s): G11B27/031




Abstract: methods, systems, and media for presenting media content with multiple media elements in an editing environment are provided. in some embodiments, the method comprises: receiving, using a computing device having a display, a request to modify a video content item containing a plurality of media elements; presenting a user interface that includes a video track representation of the video content item and a layered representation of the plurality of media elements occurring within the video content item, wherein each of the plurality of media elements is represented by a media overlay element that is positioned proximal to the video track representation and wherein the media overlay element has one or more visual characteristics; in response to receiving a selected time position within the video track representation, updating the layered representation within the user interface to present an expanded overlay list that includes media overlay elements corresponding to the subset of the plurality of media elements that occur at the selected time position within the video content item; and, in response to receiving a selected media overlay element from the expanded overlay list, updating a media window in the user interface to present a video frame corresponding to the selected time position and the media element applied to the video frame corresponding to the selected media overlay element.[[Category:G11B27/031]][[Category:G06F3/0482]][[Category:G06F3/0485]][[Category:CPC_G11B27/031]][[Category:google llc]]



[[20240235262. WIRELESS CHARGING SYSTEM WITH RECEIVER CONTROL_simplified_abstract_(google llc)]]

Inventor(s): Li Wang of Mountain View CA (US) for google llc, Liang Jia of Palo Alto CA (US) for google llc

IPC Code(s): H02J50/10, H02J50/40, H02M7/219

CPC Code(s): H02J50/10




Abstract: an example device () includes a wireless charging receive coil () configured to transduce, into an alternating current—ac—power signal, a magnetic field generated by a wireless charging transmit coil () of an external device (); an active rectifier () configured to convert the ac signal into a direct current—dc—power signal; and circuitry () configured to: obtain a target level of the dc power signal; and control the active rectifier () to output the dc power signal with the target level.[[Category:H02J50/10]][[Category:H02J50/40]][[Category:H02M7/219]][[Category:CPC_H02J50/10]][[Category:google llc]]



[[20240235618. Signal Adjustments in User Equipment-Coordination Set Joint Transmissions_simplified_abstract_(google llc)]]

Inventor(s): Jibing Wang of San Jose CA (US) for google llc, Erik Richard Stauffer of Sunnyvale CA (US) for google llc

IPC Code(s): H04B7/024, H04B7/06

CPC Code(s): H04B7/024




Abstract: techniques described herein describe aspects of signal adjustments in user equipment-coordination set, uecs, joint transmissions. a base station analyzes a first joint transmission from multiple user equipments, ues, participating in a uecs, where the multiple ues include a coordinating ue of the uecs and at least one non-coordinating ue participating in the uecs. the base station determines that the first joint transmission fails to meet a performance metric and directs the multiple ues participating in the uecs to add signal adjustments to a second joint transmission.[[Category:H04B7/024]][[Category:H04B7/06]][[Category:CPC_H04B7/024]][[Category:google llc]]



[[20240235679. COMMUNICATION METHODS AND SYSTEMS_simplified_abstract_(google llc)]]

Inventor(s): Ido Raveh of Waterloo  (CA) for google llc, Stuart James Myron Nicholson of Waterloo  (CA) for google llc

IPC Code(s): H04B10/114, G02B27/01, H04W76/10

CPC Code(s): H04B10/1143




Abstract: there is provided a method including receiving an incoming id at a first wearable heads-up display (whud), which incoming id is associated with a communicant device. the method also includes sending match data from the first whud to a match engine. the match data includes a first whud id and the incoming id. moreover, the method includes receiving a match indicator at the first whud from the match engine. the match indicator is to indicate a match event between the first whud and the communicant device based on the match data. furthermore, the method includes effecting communication between the first whud and the communicant device comprising at least one of sending a message from the first whud to the communicant device and receiving at the first whud a corresponding message from the communicant device. the first whud, and a method of operating the match engine are also described.[[Category:H04B10/114]][[Category:G02B27/01]][[Category:H04W76/10]][[Category:CPC_H04B10/1143]][[Category:google llc]]



[[20240235710. Mechanism for Precise Time Synchronization in a Datacenter Network_simplified_abstract_(google llc)]]

Inventor(s): Abhishek Agarwal of Santa Clara CA (US) for google llc, Ye Tang of Palo Alto CA (US) for google llc, Prashant R. Chandra of San Jose CA (US) for google llc, Simon Luigi Sabato of Saratoga CA (US) for google llc, Hema Hariharan of Cupertino CA (US) for google llc

IPC Code(s): H04J3/06

CPC Code(s): H04J3/0667




Abstract: aspects of the disclosure are directed to supporting time synchronization across a datacenter network with greater accuracy. the time synchronization includes both software based and hardware based time synchronization mechanisms to provide more precise time synchronization across various nodes in the datacenter network. the software based mechanism can provide the initial coarse time synchronization while the hardware based mechanism can provide the subsequent finer time synchronization.[[Category:H04J3/06]][[Category:CPC_H04J3/0667]][[Category:google llc]]



[[20240235881. ADAPTING TO DIFFERENCES IN DEVICE STATE REPORTING OF THIRD PARTY SERVERS_simplified_abstract_(google llc)]]

Inventor(s): Yuzhao Ni of Sunnyvale CA (US) for google llc

IPC Code(s): H04L12/28, G10L15/22, G10L15/30

CPC Code(s): H04L12/283




Abstract: implementations herein relate to information describing one or more internal states of a technical system. implementations herein are provided for characterizing reliability of various different third party servers, at least when reporting third party device statuses, as well as adapting protocols for device ecosystems affected by such reliability. latency can affect accuracy of device states represented by assistant devices. certain servers can be characterized as especially delayed when reporting an updated device state in response to a user request, and, as a result, the third party server can be correlated to a metric that characterizes the relative latency of the third party server. when the metric fails to satisfy a particular threshold, a server and/or client associated with the “ecosystem” of third party devices can affirmatively operate to retrieve device state updates, rather than passively await updates from a corresponding third party server.[[Category:H04L12/28]][[Category:G10L15/22]][[Category:G10L15/30]][[Category:CPC_H04L12/283]][[Category:google llc]]



[[20240236052. Pure Differentially Private Algorithms for Summation in the Shuffled Model_simplified_abstract_(google llc)]]

Inventor(s): Badih Ghazi of San Jose CA (US) for google llc, Noah Zeger Golowich of Lexington MA (US) for google llc, Shanmugasundaram Ravikumar of Piedmont CA (US) for google llc, Pasin Manurangsi of Mountain View CA (US) for google llc, Ameya Avinash Velingker of San Francisco CA (US) for google llc, Rasmus Pagh of Berkeley CA (US) for google llc

IPC Code(s): H04L9/40, G06N5/04, G06N20/00

CPC Code(s): H04L63/0428




Abstract: an encoding method for enabling privacy-preserving aggregation of private data can include obtaining private data including a private value, determining a probabilistic status defining one of a first condition and a second condition, producing a multiset including a plurality of multiset values, and providing the multiset for aggregation with a plurality of additional multisets respectively generated for a plurality of additional private values. in response to the probabilistic status having the first condition, the plurality of multiset values is based at least in part on the private value, and in response to the probabilistic status having the second condition, the plurality of multiset values is a noise message. the noise message is produced based at least in part on a noise distribution that comprises a discretization of a continuous unimodal distribution supported on a range from zero to a number of multiset values included in the plurality of multiset values.[[Category:H04L9/40]][[Category:G06N5/04]][[Category:G06N20/00]][[Category:CPC_H04L63/0428]][[Category:google llc]]



[[20240236152. ESTABLISHING INTERNET PROTOCOL MULTIMEDIA SUBSYSTEM SERVICES IN A CELLULAR NETWORK_simplified_abstract_(google llc)]]

Inventor(s): Chih-Hsiang Wu of Taoyuan City  (TW) for google llc, Chi-Wen Chung of Taoyuan City  (TW) for google llc, Han-Jung Chueh of New Taipei City  (TW) for google llc

IPC Code(s): H04L65/1016, H04L65/80

CPC Code(s): H04L65/1016




Abstract: a user equipment (ue) device of a wireless communication system is configured to establish an internet protocol multimedia subsystem (ims) service. the ue device initiates an ims service with an ims network. responsive to initiating the ims service, the ue device sends a message to the ims network specifying one or more preconditions for establishing the ims service. the ue device selectively transmits one or more service packets for the ims service over either a dedicated data radio bearer configured for the ims service or a default data radio bearer based on whether at least one of the one or more preconditions has not been satisfied.[[Category:H04L65/1016]][[Category:H04L65/80]][[Category:CPC_H04L65/1016]][[Category:google llc]]



[[20240236172. Capturing and Automatically Uploading Media Content_simplified_abstract_(google llc)]]

Inventor(s): Mark Wagner of Clyde Hill WA (US) for google llc, Thomas H. Taylor of Redmond WA (US) for google llc, David P. Conway of Los Altos CA (US) for google llc

IPC Code(s): H04L67/06, H04L67/02, H04L67/04, H04L67/62

CPC Code(s): H04L67/06




Abstract: a computer-implemented method for automatically uploading media content from a mobile device to an online service provider can include receiving, in the mobile device, identifying information corresponding to a user account associated with at least one of a plurality of online service providers; capturing media content with a media input component included in the mobile device; and after the media content is captured, automatically uploading to the at least one online service provider the captured media content and the identifying information, without receiving user input contemporaneous with the automatic uploading that specifies that the captured media content is to be uploaded. the mobile device can further include a wireless communication component configured to wirelessly send data to and wirelessly receive data from the plurality of online service providers, which can be external to the mobile device.[[Category:H04L67/06]][[Category:H04L67/02]][[Category:H04L67/04]][[Category:H04L67/62]][[Category:CPC_H04L67/06]][[Category:google llc]]



[[20240236272. Immersive Teleconferencing within Shared Scene Environments_simplified_abstract_(google llc)]]

Inventor(s): Stéphane Hervé Loïc Hulaud of Stockholm  (SE) for google llc

IPC Code(s): H04N7/15, G06F3/01, G06T7/70, G06V10/141, G06V10/26, G06V10/70

CPC Code(s): H04N7/157




Abstract: methods, systems, and apparatus are described for immersive videoconferencing teleconferencing streams from multiple endpoints within shared scene environment. the method includes receiving a plurality of streams for presentation at a teleconference, wherein each of the plurality of streams represents a participant of a respective plurality of participants of the teleconference. the method includes, determining scene data descriptive of a scene environment, the scene data comprising at least one of lighting characteristics, acoustic characteristics, or perspective characteristics of the scene environment. the method includes, for each of the plurality of participants of the teleconference, determining a position of the participant within the scene environment and, based at least in part on the scene data and the position of the participant within the scene environment, modifying the stream that represents the participant.[[Category:H04N7/15]][[Category:G06F3/01]][[Category:G06T7/70]][[Category:G06V10/141]][[Category:G06V10/26]][[Category:G06V10/70]][[Category:CPC_H04N7/157]][[Category:google llc]]



[[20240236553. COMBINED AUDIO AND HAPTIC ACTUATOR ASSEMBLY_simplified_abstract_(google llc)]]

Inventor(s): Jianxun Wang of Sunnyvale CA (US) for google llc, Debanjan Mukherjee of San Jose CA (US) for google llc, Che-Yuan Hsu of Taipei city  (TW) for google llc, Zhi Qiang Li of Suzhou  (CN) for google llc, Michael Kai Morishita of Belmont CA (US) for google llc

IPC Code(s): H04R1/28, H04R1/24

CPC Code(s): H04R1/28




Abstract: actuator assemblies, and electronic devices and wearable devices including actuator assemblies, are disclosed. an actuator assembly includes: a first housing enclosing an audio actuator and a first open volume; and a second housing enclosing a haptic actuator and a second open volume. the first open volume is fluidly connected with the second open volume by an aperture between the first housing and the second housing. a first wall of the first housing abuts a second wall of the second housing. the aperture includes a first opening in the first wall of the first housing and a second opening in the second wall of the second housing. the first housing includes a linking component configured to mechanically connect to the second housing. the linking component includes a fluid conduit extending from the first open volume to an opening in the second wall of the second housing.[[Category:H04R1/28]][[Category:H04R1/24]][[Category:CPC_H04R1/28]][[Category:google llc]]



[[20240236777. Managing Conditional Secondary Node Change_simplified_abstract_(google llc)]]

Inventor(s): Chih-Hsiang Wu of Taoyuan City  (TW) for google llc, Ching-Jung Hsieh of Mountain View CA (US) for google llc

IPC Code(s): H04W36/00, H04W36/38

CPC Code(s): H04W36/0069




Abstract: a base station can perform various methods for supporting a secondary node (sn) procedure. in one example, a first base station, operating as (i) a source sn. (ii) a target sn, or (iii) a candidate sn for the sn procedure, performs a method including: communicating a message with a second base station, operating as a master node (mn), to perform the sn procedure; and determining, based on whether the sn procedure is a conditional procedure or a non-conditional procedure, whether to start a timer in response to the communicating.[[Category:H04W36/00]][[Category:H04W36/38]][[Category:CPC_H04W36/0069]][[Category:google llc]]



[[20240236862. MANAGING ENERGY USAGE OF A USER EQUIPMENT DEVICE FOR WIRELESS COMMUNICATIONS_simplified_abstract_(google llc)]]

Inventor(s): Jibing Wang of San Jose CA (US) for google llc, Erik Stauffer of Mountain View CA (US) for google llc

IPC Code(s): H04W52/02

CPC Code(s): H04W52/0261




Abstract: techniques for improving, for a set of conditions at a ue, the usage of energy stored at a ue include determining a preferred or requested partitioning of the ue's stored energy usage during wireless data transfer between the ue and the base station (e.g., an amount or percentage of stored energy utilized by the ue for baseband signal processing with respect to the amount or percentage of energy utilized by the ue for radio interface signal processing tasks), and indicating the preferred partitioning to the base station or network. based on the indication, the base station/network may modify the baseband communication scheme, parameters, and/or values, and/or the radio interface communication scheme, parameters, and/or values utilized for the wireless transfers of data between the base station and the ue, thereby better managing (and in some cases, optimizing) the ue's stored energy usage and increasing battery life at the ue.[[Category:H04W52/02]][[Category:CPC_H04W52/0261]][[Category:google llc]]



[[20240237102. MANAGING A CELL GROUP IN DUAL CONNECTIVITY_simplified_abstract_(google llc)]]

Inventor(s): Chih-Hsiang Wu of Taoyuan City  (TW) for google llc

IPC Code(s): H04W76/15, H04W76/30

CPC Code(s): H04W76/15




Abstract: a network node of a radio access network (ran), communicating with a user equipment (ue) in dual connectivity (dc) with a master node (mn) and a secondary node (sn), can implement a method for managing deactivation of a secondary cell group (scg). the method includes detecting () that a condition for deactivating the scg is satisfied. the method further includes determining () whether the ue supports deactivating the scg, and causing () the sn to deactivate or to release the scg at the sn based on the determining.[[Category:H04W76/15]][[Category:H04W76/30]][[Category:CPC_H04W76/15]][[Category:google llc]]



[[20240237142. EARLY DATA COMMUNICATION WITH CONFIGURED RESOURCES_simplified_abstract_(google llc)]]

Inventor(s): Chih-Hsiang Wu of Taoyuan City  (TW) for google llc

IPC Code(s): H04W76/34, H04W72/232, H04W76/27

CPC Code(s): H04W76/34




Abstract: a distributed unit (du) of a distributed base station, where the distributed base station that includes the du and a central unit (cu), can implement a method for managing early data communication with a user equipment (ue). the method includes: communicating () with the ue using a first temporary identifier of the ue; receiving (), from the cu, a cu-to-du message requesting a configured resources configuration for the ue; transmitting (), to the cu, a du-to-cu message including the configured resources configuration and a second temporary identifier for the ue; and communicating () with the ue in accordance with the configured resources configuration while a radio resource control connection between the ue and the distributed base station is not active.[[Category:H04W76/34]][[Category:H04W72/232]][[Category:H04W76/27]][[Category:CPC_H04W76/34]][[Category:google llc]]



[[GOOGLE LLC patent applications on July 11th, 2024]]



[[Category:GOOGLE LLC]] [[Category:G06F3/01]] [[Category:G10L15/30]] [[Category:G10L15/26]] [[Category:G06N3/04]] [[Category:G06N20/00]]