Google LLC patent applications on March 6th, 2025
Patent Applications by Google LLC on March 6th, 2025
Google LLC: 70 patent applications
Google LLC has applied for patents in the areas of G06N20/00 (6), G06F21/62 (4), G06F1/16 (4), G10L15/06 (4), G10L15/30 (4) G10L15/063 (3), G10L13/08 (2), G06F16/1805 (2), G06F16/9535 (2), G06N20/00 (2)
With keywords such as: data, device, user, based, digital, computer, particular, training, determining, and generating in patent application abstracts.
Patent Applications by Google LLC
20250072570. Quick Release Band/Lug Mechanism for Smartwatch_simplified_abstract_(google llc)
Inventor(s): Peter Michael Cazalet of Los Gatos CA (US) for google llc, Christoph Gredler of Mountain View CA (US) for google llc, Eric Dayringer of Sunnyvale CA (US) for google llc
IPC Code(s): A44C5/14
CPC Code(s): A44C5/14
Abstract: a watch system may include a watchband including a flexible member configured to be mounted onto a wrist of a user, and a puck including watch functionality. the watchband may have an end that has a concave curved shape. the puck may have a connection interface that has a convex curved shape. the connection interface may be to be removably coupled to the end of the watchband. the watchband and the puck may have corresponding locking features that are configured to rotationally and translationally fix the puck to the watchband. the corresponding locking features may be configured to be engaged when the watchband is translated relative to the puck and rotated relative to the puck by a predetermined rotation angle.
20250072798. Score Indicative of Mindfulness of a User_simplified_abstract_(google llc)
Inventor(s): Aravind Natarajan of San Francisco CA (US) for google llc
IPC Code(s): A61B5/16, A61B5/00, A61B5/024
CPC Code(s): A61B5/165
Abstract: a computer-implemented method for determining a score indicative of mindfulness of a user is provided. the method includes obtaining heart rate variability data of the user during a guided breathing exercise in which the user inhales and exhales to mimic a respiration rate associated with mindfulness. the method includes filtering the heart rate variability data to generate filtered heart rate variability. the method includes determining a first standard deviation of interbeat intervals indicative of respiratory sinus arrythmia and included in a first segment of the filtered heart rate variability data that spans a discrete interval of time. the method includes determining a second standard deviation of all interbeat intervals included in the first segment of the filtered heart rate variability data. the method includes determining a score indicative of mindfulness of the user based on the first standard deviation and the second standard deviation.
Inventor(s): Yan Mayster of () for google llc, Tom Weng of () for google llc
IPC Code(s): G01C21/34, G01C21/36
CPC Code(s): G01C21/3476
Abstract: to provide navigation directions in response to a request for viewing a particular type of geographical feature on a route to a destination, a computing device receives a request for navigation directions from a starting location to a destination that specifies a particular type of geographical feature for viewing on a route to the destination. the computing device identifies at least one candidate route for navigating to the destination location that includes a road segment from which the particular type of geographical feature can be viewed, and selects a route from the set of candidate routes based at least in part on an extent to which the particular type of geographical feature can be viewed from each candidate route. then the computing device provides a set of navigation directions for presentation on a client device for navigating to the destination location via the selected route.
20250076380. HIGH-THROUGHPUT SCAN ARCHITECTURE_simplified_abstract_(google llc)
Inventor(s): Syed Shakir Iqbal of Bengaluru (IN) for google llc
IPC Code(s): G01R31/3185
CPC Code(s): G01R31/318555
Abstract: methods, systems, and apparatus, for a high throughput scan architecture. the scan architecture can include a clock controller, a decompressor, a scan chain, and a compressor. in some implementations, a set of values that represents a particular data pattern is received. a first data signal is generated using at least a portion of the values in the set of values, where the first data signal has a first frequency. a first series of latches and a second series of latches are used to extract alternating values of the at least portion of values from the first data signal, where the first series and second series of latches extract the alternating values at a second frequency that is a fraction of the first frequency. outputs of the first and second series of latches are combined to generate a second data signal, where the second data signal has the first frequency.
Inventor(s): Joseph Daniel Lowney of Tucson AZ (US) for google llc, Wei Jin of Saratoga CA (US) for google llc
IPC Code(s): G02B5/18, G02B6/12, G02B6/34, G02B27/01
CPC Code(s): G02B5/1857
Abstract: the present disclosure provides techniques to reduce or eliminate contaminants in a waveguide resulting from the waveguide fabrication process. a blocking layer is deposited on a grating layer to protect the grating layer from contamination, e.g., via diffusion, from a hardmask layer that is used to pattern the grating layer. accordingly, the optical gratings resulting from the patterning of the grating layer have little or no contamination from the hardmask layer, thereby increasing the optical performance of the final waveguide product.
20250076563. CASTING FABRICATION OF REFLECTIVE POLYMER WAVEGUIDE_simplified_abstract_(google llc)
Inventor(s): Kang Luo of San Jose CA (US) for google llc, Thomas Mercier of Weston FL (US) for google llc, Christophe Peroz of Zurich (CH) for google llc
IPC Code(s): F21V8/00, G02B6/36
CPC Code(s): G02B6/0055
Abstract: a fabrication process uses casting to form portions of a waveguide having ultra-flat surfaces. a casting resin is coated between a prism mold and a top flat mold via inkjet, slot die, spray coating, etc. the top flat mold is lowered to conform with the casting resin and the casting resin is then cured to form a bottom prism array. after curing, the bottom prism array is demolded from the prism mold and the top flat mold is used as a carrier wafer to support the bottom prism array. the bottom prism array is selectively coated with a reflective coating and a second casting process is performed by coating the bottom prism array with casting resin to form a reflective waveguide.
Inventor(s): Ozan Cakmakci of Sunnyvale CA (US) for google llc, Eliezer Glik of San Francisco CA (US) for google llc
IPC Code(s): G02B27/01
CPC Code(s): G02B27/0172
Abstract: a non-planar lightguide directs a display light from an incoupler surface towards an eye of a user via a reduced number of internal reflective interactions with a world-facing surface of the non-planar lightguide and an eye-facing lens surface of the non-planar lightguide.
20250076819. Symbiotic Smartwatch Displays_simplified_abstract_(google llc)
Inventor(s): Alex Olwal of San Francisco CA (US) for google llc
IPC Code(s): G04C17/00, G04C3/00, G04G9/00, G06F1/16
CPC Code(s): G04C17/0091
Abstract: aspects of the technology provide a symbiotic graphical display on a client device such as a smartwatch. the system includes at least one emissive display element and at least one non-emissive display element. the display elements are arrayed in layers or other configurations such that content or other information is concurrently aligned across the respective display surfaces of the different elements. a first set of content is rendered using the non-emissive display element while a second set of content is rendered using the emissive display element. depending on characteristics or features of a given content item, that item may be rendered by one or both of the display elements. certain content may be transitioned from the emissive display element to the non-emissive display element according to a time threshold or other criteria.
Inventor(s): Shih Wei Hsiang of New Taipei City (TW) for google llc, Po-Kai Lai of New Taipei City (TW) for google llc, Jengn Wen Lin of New Taipei City (TW) for google llc, Hung-Wei Wang of New Taipei City (TW) for google llc
IPC Code(s): G06F1/16
CPC Code(s): G06F1/1681
Abstract: an example computing device includes a flexible display coupled to a housing that includes a support plate having a first joint coupled to a first end of the support plate and a second joint coupled to a second end of the support plate. a slide module has a slot that guides a slide movement of the second joint along a path of movement within the slot as the support plate pivots about the first joint, where the support plate moves according to the first joint and the second joint to support at least the portion of the flexible display when the flexible display is unfolded and moves according to the first joint and the second joint to create a gap between at least a portion of the support plate and at least the portion of the flexible display when the flexible display is folded.
20250076940. Modular Liquid Cooling Architecture For Liquid Cooling_simplified_abstract_(google llc)
Inventor(s): Jerry Chiu of Pacifica CA (US) for google llc, Reza H. Khiabani of San Mateo CA (US) for google llc, Xiaojin Wei of Dublin CA (US) for google llc, Madhusudan Krishnan Lyengar of Foster City CA (US) for google llc
IPC Code(s): G06F1/20, F28F1/00
CPC Code(s): G06F1/20
Abstract: a heat exchanger includes a first manifold having an inlet opening and a second manifold having an outlet opening. a group of conduits fluidly connect the first manifold and the second manifold to one another such that a flow path is established for liquid to flow from the inlet opening to the outlet opening. the flow path includes a select portion that extends through all conduits within the group of conduits. valves are located in the first manifold and the second manifold. the valves are operable to change the select portion of the flow path from between a first state, wherein the conduits within group of conduits are fluidly connected in parallel with one another, and a second state, wherein the conduits within the group of conduits are fluidly connected in series with one another.
20250077009. TOUCH-SENSITIVE LED DISPLAY_simplified_abstract_(google llc)
Inventor(s): Dongeek Shin of Mountain View CA (US) for google llc, Paul Joseph Silberschatz of San Francisco CA (US) for google llc
IPC Code(s): G06F3/041, G06F3/042
CPC Code(s): G06F3/0412
Abstract: methods, systems, and apparatus, for sensing a touch on a display panel including an array of pixels. a method includes controlling first pixels to operate in an illumination state; controlling second pixels to repeatedly switch between operating in the illumination state and operating in a sensing state; generating sensing signals indicative of levels of light detected by the second pixels; detecting a touch input to the display panel based on the generated sensing signals; and in response to detecting the touch input to the display panel, changing at least one of (i) a frequency at which the second pixels switch between operating in the illumination state and in the sensing state, (ii) a duty cycle for operating the second pixels in the sensing state, or (iii) which of the pixels in the array of pixels are controlled to switch between operating in the illumination state and in the sensing state.
20250077276. EXPLICIT SCHEDULING OF ON-CHIP OPERATIONS_simplified_abstract_(google llc)
Inventor(s): Michial Allen Gunter of San Francisco CA (US) for google llc, Charles Henry Leichner, IV of Palo Alto CA (US) for google llc
IPC Code(s): G06F9/48, G06F9/30, G06F9/32, G06F9/38, G06F15/78, G06N3/063
CPC Code(s): G06F9/4881
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a first schedule, for a first hardware block of an integrated circuit device, where the first schedule identifies a first set of operations to be performed by the first hardware block. obtaining a second schedule for a second hardware block of the integrated circuit device, where the second schedule identifies a second set of operations to be performed by the second hardware block and where operations of the second schedule are coordinated with operations of the first schedule such that the first schedule triggers the first hardware block to send data to the second block at a first pre-scheduled value of a counter, and the second schedule triggers the second hardware block to accept the data at an input at a second pre-scheduled value of the counter that is after the first pre-scheduled value. performing, by the first hardware block, the first set of operations according to the first schedule, and performing, by the second hardware block, the second set of operations according to the second schedule.
20250077441. CACHING USING MACHINE LEARNED PREDICTIONS_simplified_abstract_(google llc)
Inventor(s): Sergei Vassilvitskii of San Francisco CA (US) for google llc, Theodoros Lykouris of Ithaca NY (US) for google llc
IPC Code(s): G06F12/121, G06F12/127, G06N3/044, G06N20/00
CPC Code(s): G06F12/121
Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for evicting cache data using machine learning. one of the methods includes determining that particular data is not stored in a cache that is full; determining, using information for the particular data, a predicted eviction accuracy of a machine learning system; determining whether the predicted eviction accuracy of the machine learning system satisfies a threshold eviction accuracy; and in response to determining that the predicted eviction accuracy of the machine learning system satisfies the threshold eviction accuracy: sending, to the machine learning system, a request for an identifier for data stored in the cache; receiving, from the machine learning system, an identifier for data stored in the cache; evicting the data referenced by identifier from a location in the cache; and storing the particular data at the location in the cache.
Inventor(s): Yasushi Saito of Mountain View CA (US) for google llc, Sanjay Ghemawat of Mountain View CA (US) for google llc, Jeffrey Adgate Dean of Palo Alto CA (US) for google llc
IPC Code(s): G06F16/16, G06F16/11, G06F16/174, G06F16/182, G06F16/215
CPC Code(s): G06F16/162
Abstract: a method for deleting obsolete files from a file system is provided. the method includes receiving a request to delete a reference to a first target file of a plurality of target files stored in a file system, the first target file having a first target file name. a first reference file whose file name includes the first target file name is identified. the first reference file is deleted from the file system. the method further includes determining whether the file system includes at least one reference file, distinct from the first reference file, whose file name includes the first target file name. in accordance with a determination that the file system does not include the at least one reference file, the first target file is deleted from the file system.
20250077477. Managed Tables for Data Lakes_simplified_abstract_(google llc)
Inventor(s): Thibaud Hottelier of Seattle WA (US) for google llc, Anoop Kochummen Johnson of Fremont CA (US) for google llc, Justin Levandoski of Seattle WA (US) for google llc, Gaurav Saxena of Bothell WA (US) for google llc, Yuri Volobuev of Walnut Creek CA (US) for google llc
IPC Code(s): G06F16/18, G06F16/22, G06F16/23, G06F16/28
CPC Code(s): G06F16/1805
Abstract: aspects of the disclosure are directed to merging data lake openness with scalable metadata for managed tables in a cloud database platform, allowing for atomicity, consistency, isolation, and durability (acid) transactions, performant data manipulation language (dml), higher throughput stream ingestion, data consistency, schema evolution, time travel, clustering, fine-grained security, and/or automatic storage optimization. table data is stored in various open-source file formats in cloud storage while physical metadata of the table data is stored in a scalable metadata storage system.
20250077478. Managed Tables for Data Lakes_simplified_abstract_(google llc)
Inventor(s): Victor Sergeyevich Agababov of Seattle WA (US) for google llc, Shuang Guan of Sunnyvale CA (US) for google llc, Thibaud Hottelier of Seattle WA (US) for google llc, Anoop Kochummen Johnson of Fremont CA (US) for google llc, Justin Levandoski of Seattle WA (US) for google llc, Bigang Li of Redmond WA (US) for google llc, Yuri Volobuev of Walnut Creek CA (US) for google llc
IPC Code(s): G06F16/18, G06F12/02, G06F16/23, G06F16/28
CPC Code(s): G06F16/1805
Abstract: aspects of the disclosure are directed to merging data lake openness with scalable metadata for managed tables in a cloud database platform, allowing for atomicity, consistency, isolation, and durability (acid) transactions, performant data manipulation language (dml), higher throughput stream ingestion, data consistency, schema evolution, time travel, clustering, fine-grained security, and/or automatic storage optimization. table data is stored in various open-source file formats in cloud storage while physical metadata of the table data is stored in a scalable metadata storage system.
20250077508. Refining a Search Using Physiological Information_simplified_abstract_(google llc)
Inventor(s): Ivan Poupyrev of Sunnyvale CA (US) for google llc, Gaetano Roberto Aiello of Bend OR (US) for google llc
IPC Code(s): G06F16/242, G06F3/01, G06F16/2457, G06F16/248, G06F16/29, G06F16/9537
CPC Code(s): G06F16/242
Abstract: this document describes techniques and devices for a radar recognition-aided search. through use of a radar-based recognition system, gestures made by, and physiological information about, persons can be determined. in the case of physiological information, the techniques can use this information to refine a search. for example, if a person requests a search for a coffee shop, the techniques may refine the search to coffee shops in the direction that the person is walking. in the case of a gesture, the techniques may refine or base a search solely on the gesture. thus, a search for information about a store, car, or tree can be made responsive to a gesture pointing at the store, car, or tree with or without explicit entry of a search query.
Inventor(s): Suzhen Lin of Sunnyvale CA (US) for google llc
IPC Code(s): G06F16/2455
CPC Code(s): G06F16/2455
Abstract: a method for a statistics collection framework includes receiving a schema defining a relational database for storing a plurality of statistics corresponding to a query, the relational database including a plurality of data tables relationally connected according to the schema, each data table of the plurality of data tables corresponding to a respective statistic. the method includes receiving a query corresponding to data at a data store. the method also includes executing the query. during execution of the query, the method includes collecting, from a query execution database, the plurality of statistics related to the query, each statistic of the plurality of statistics corresponding to a respective data table of the plurality of data tables of the relational database and, for each statistic of the plurality of statistics, storing the respective statistic at the respective data table according to the schema.
20250077594. PRIVACY PRESERVING RECOMMENDATION SYSTEM_simplified_abstract_(google llc)
Inventor(s): Rishav Anand of Mountain View CA (US) for google llc, Steven Guy Avery of Santa Clara CA (US) for google llc, Sittichai Jiampojamarn of San Jose CA (US) for google llc
IPC Code(s): G06F16/9535, G06F16/955, G06F21/62
CPC Code(s): G06F16/9535
Abstract: methods, systems, and apparatus, including computer programs encoded on computer-storage media, for privacy preserving digital component provider. in some implementations, a method includes providing, by a user device and during a browsing session of content page at the user device, (1) a request for a digital component and (2) contextual data representing a context within which the content page is provided for display on the user device; obtaining an embedding vector that represents the contextual data as a set of features and the digital component; generating one or more adjusted embedding vectors for a first interest group, wherein the collection includes the embedding vector adjusted by one or more values; and providing the one or more adjusted embedding vectors to a server for generating a model for the first interest group.
20250077597. STATE-DEPENDENT QUERY RESPONSE_simplified_abstract_(google llc)
Inventor(s): John Nicholas Jitkoff of Palo Alto CA (US) for google llc, Michael J. Lebeau of New York NY (US) for google llc, William J. Byrne of Davis CA (US) for google llc, David P. Singleton of San Francisco CA (US) for google llc
IPC Code(s): G06F16/9535, G06F3/16, G06F16/248, G06F16/332, G06F16/338, G06F16/638, G06F16/951, G06F16/9538, G06F40/186, G06F40/20, G06F40/58, G10L13/00, G10L15/22, G10L15/30, H04M1/60, H04M1/72454, H04R29/00
CPC Code(s): G06F16/9535
Abstract: in general, the subject matter described in this specification can be embodied in methods, systems, and program products for receiving user input that defines a search query, and providing the search query to a server system. information that a search engine system determined was responsive to the search query is received at a computing device. the computing device is identified as in a first state, and a first output mode for audibly outputting at least a portion of the information is selected. the first output mode is selected from a collection of the first output mode and a second output mode. the second output mode is selected in response to the computing device being in a second state and is for visually outputting at least the portion of the information and not audibly outputting the at least portion of the information. at least the portion of information is audibly output.
Inventor(s): Victor Carbune of Zurich (CH) for google llc, Mathew Sharifi of Kilchberg (CH) for google llc
IPC Code(s): G06F16/9537, G06F16/2457, G06F16/29, G06F16/9535, G06F16/9538
CPC Code(s): G06F16/9537
Abstract: the present disclosure provides a computing device and method for providing personal specific information based on semantic queries. the semantic queries may be input in a natural language form, and may include user specific context, such as by referring to prior or future events related to a place the user is searching for. with the user's authorization, data associated with prior or planned activities of the user may be accessed and information from the accessed data may be identified, wherein the information is correlated with the user specific context. one or more query results are determined based on the identified information and provided for output to the user.
20250077603. AUTOMATIC NAVIGATION OF INTERACTIVE WEB DOCUMENTS_simplified_abstract_(google llc)
Inventor(s): Aleksandra Faust of Palo Alto CA (US) for google llc, Dilek Hakkani-Tur of Los Altos CA (US) for google llc, Izzeddin Gur of Goleta CA (US) for google llc, Ulrich Rueckert of San Francisco CA (US) for google llc
IPC Code(s): G06F16/954, G06F16/953, G06N3/04
CPC Code(s): G06F16/954
Abstract: the present disclosure is generally directed to methods, apparatus, and computer-readable media (transitory and non-transitory) for learning to automatically navigate interactive web documents and/or websites. more particularly, various approaches are presented for training various deep q network (dqn) agents to perform various tasks associated with reinforcement learning, including hierarchical reinforcement learning, in challenging web navigation environments with sparse rewards and large state and action spaces. these agents include a web navigation agent that can use learned value function(s) to automatically navigate through interactive web documents, as well as a training agent, referred to herein as a “meta-trainer,” that can be trained to generate synthetic training examples. some approaches described herein may be implemented when expert demonstrations are available. other approaches described herein may be implemented when expert demonstrations are not available. in either case, dense, potential-based rewards may be used to augment the training.
Inventor(s): Lukas Zilka of Zurich (CH) for google llc
IPC Code(s): G06F18/214, G06F17/18, G06F18/2415, G06N3/02, G06N20/00, G06V10/46
CPC Code(s): G06F18/2148
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboratively training an interaction prediction machine learning model using a plurality of user devices in a manner that respects user privacy. in one aspect, the machine learning model is configured to process an input comprising: (i) a search query, and (ii) a data element, to generate an output which characterizes a likelihood that a given user would interact with the data element if the data element were presented to the given user on a webpage identified by a search result responsive to the search query.
20250077643. SECURE WORKFLOWS THAT ENHANCE DATA SECURITY_simplified_abstract_(google llc)
Inventor(s): Gang Wang of Frederick MD (US) for google llc, Nikolaus Rath of Harpenden (GB) for google llc
IPC Code(s): G06F21/53
CPC Code(s): G06F21/53
Abstract: methods, systems, and apparatus, including medium-encoded computer program products, for secure workflows that enhance data security are described. in one aspect, a digital component request is received. in response to receiving the digital component request, a multi-stage workflow for selecting a digital component is identified, and can include customizable stages. the execution of workflow stages includes: (a) identifying a given customizable stage; (b) for the stage: (i) identifying, a customization specific to the stage that generates an output for use in selecting the digital component; (ii) initiating an isolated execution environment for each customization; (iii) executing, within each isolated execution environment, the customization for which the isolated execution environment was initiated; and (iv) obtaining the output generated by the code of each isolated execution environment; and (c) executing a final stage to select a digital component based on the outputs. the selected digital component is sent to the client device.
Inventor(s): Gang Wang of Frederick MD (US) for google llc, Chin-Yet Lin of San Jose CA (US) for google llc, Rishav Anand of Mountain View CA (US) for google llc, Shruti Murali of Sunnyvale CA (US) for google llc, Tenghui Liu of Sunnyvale CA (US) for google llc
IPC Code(s): G06F21/62
CPC Code(s): G06F21/6245
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for distributing digital component while securing user data are described. in one aspect, a method includes receiving, by a multi-platform server and from a client device, a request for a digital component for presentation by the client device. the request for the digital component includes (i) request data that is opaque to the multi-platform server, and (ii) sensitive user data that is managed by the client device. in response to receiving the request for the digital component, the multi-platform server transmits, to a first content platform, a contextual request that includes the request data from the client device and that does not include the sensitive user data. after transmitting the contextual request, the multi-platform server receives, from the first content platform, a contextual response that includes a selection data unit for a first repository of digital components.
Inventor(s): Alessandro Epasto of New York NY (US) for google llc, Hossein Esfandiari of Jersey City NJ (US) for google llc, Vahab Seyed Mirrokni of Hoboken NJ (US) for google llc, Andres Munoz Medina of Brooklyn NY (US) for google llc, Umar Syed of Rahway NJ (US) for google llc, Sergei Vassilvitskii of New York NY (US) for google llc
IPC Code(s): G06F21/62, G06F16/28, G06N20/00
CPC Code(s): G06F21/6254
Abstract: a computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. the computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. the computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. the computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.
Inventor(s): Gang Wang of Frederick MD (US) for google llc, Wenchao Tong of San Jose CA (US) for google llc
IPC Code(s): G06F21/62, G06F40/14
CPC Code(s): G06F21/6263
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating dynamic digital content in privacy preserving ways are described. in one aspect, a method includes receiving, by a trusted server and from multiple content platforms, digital component data for digital components. the server received, from each content platform, dynamic content selection logic for selecting discrete content elements for digital components of the content platform. the server selects, from digital components for which digital component data is stored in a digital component repository, candidate digital components based at least on user data included in a digital component request. for each candidate digital component, the server executes the dynamic content selection logic of the content platform that provided the digital component data for the candidate digital component, the executing resulting in selection of a particular layout and a particular subset of content elements for the digital component.
Inventor(s): Geoffrey Anderson of Toronto (CA) for google llc, Roman Arcea of Velserbroek (NL) for google llc
IPC Code(s): G06F40/284, G06F16/953, G06F40/30, G06F40/40
CPC Code(s): G06F40/284
Abstract: a method for golden prompt generation based on authoritative publications includes receiving an initial authoritative publication associated with a specific topic. the method includes retrieving, using the initial authoritative publication additional authoritative publications associated with the specific topic. the method includes generating, using natural language processing, a set of golden prompts from the set of authoritative publications. each golden prompt of the set of golden prompts includes text from the set of authoritative publications. the method includes fine-tuning a pre-trained model using the set of authoritative publications. the method includes generating, using the fine-tuned model and the set of golden prompts, a set of predictions. the method includes determining, using the set of predictions and the set of authoritative publications, an error rate of the fine-tuned model. the error rate indicates a similarity between the set of predictions and the set of authoritative publications.
20250077833. HARDWARE-OPTIMIZED NEURAL ARCHITECTURE SEARCH_simplified_abstract_(google llc)
Inventor(s): Sheng Li of Cupertino CA (US) for google llc, Norman Paul Jouppi of Palo Alto CA (US) for google llc, Quoc V. Le of Sunnyvale CA (US) for google llc, Mingxing Tan of Newark CA (US) for google llc, Ruoming Pang of New York NY (US) for google llc, Liqun Cheng of Foster City CA (US) for google llc, Andrew Li of Sunnyvale CA (US) for google llc
IPC Code(s): G06N3/04, G06N3/08
CPC Code(s): G06N3/04
Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining an architecture for a task neural network that is configured to perform a particular machine learning task on a target set of hardware resources. when deployed on a target set of hardware, such as a collection of datacenter accelerators, the task neural network may be capable of performing the particular machine learning task with enhanced accuracy and speed.
Inventor(s): Mara Finkelstein of Los Altos Hills CA (US) for google llc, Qijun Tan of Los Gatos CA (US) for google llc, Markus Freitag of Los Altos CA (US) for google llc, Apurva Pradip Shah of Burlingame CA (US) for google llc
IPC Code(s): G06N3/0475
CPC Code(s): G06N3/0475
Abstract: implementations disclose utilizing a less computationally efficient decoding method in automatically generating corresponding single generative content predictions for training instances and fine-tuning a student generative model based on those automatically generated training instances. those implementations are further directed to then utilizing, in an inference time environment, the fine-tuned student generative model and a more computationally efficient decoding method in generating generative predictions—and without any utilization of the less computationally efficient decoding method in generating the generative predictions.
20250077871. PRIVACY-SENSITIVE NEURAL NETWORK TRAINING_simplified_abstract_(google llc)
Inventor(s): Devora Berlowitz of Seattle WA (US) for google llc, Steve Shaw-Tang Chien of San Carlos CA (US) for google llc, Yunqi Xue of Mountain View CA (US) for google llc, Lin Ning of San Jose CA (US) for google llc, Shuang Song of Cupertino CA (US) for google llc, Mei Chen of Sunnyvale CA (US) for google llc
IPC Code(s): G06N3/084
CPC Code(s): G06N3/084
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for privacy-sensitive training of a neural network. in one aspect, a system comprises a central memory configured to store current values of a set of neural network parameters and one or more computers that are configured to implement a plurality of worker computing units, where each worker computing unit is configured to repeatedly perform operations comprising obtaining current values of the set of neural network parameters from the central memory, sampling a batch of network inputs from a set of training data, determining a respective gradient corresponding to each network input, determining an aggregated gradient based on the gradients, identifying a subset of a set of gradient values as target values, generating a noisy gradient by combining random noise with the target gradient values, and updating the current values of the set of neural network parameters.
Inventor(s): Xinyi Wang of New York NY (US) for google llc, John Frederick Wieting of New York NY (US) for google llc, Jonathan Hudson Clark of Seattle WA (US) for google llc
IPC Code(s): G06N3/0985, G06N3/045
CPC Code(s): G06N3/0985
Abstract: methods, systems, and apparatuses, including computer programs encoded on computer storage media, for configuring a set of language model neural networks, e.g., a first large language model and a second smaller-sized language model, and performing a machine learning task on new inputs using the set of language model neural networks. configuring the language model neural networks and performing a machine learning task can include leveraging the ability of a first large language model to follow prompt-engineered instructions and perform chain-of-thought reasoning, while also fine-tuning a second, smaller language model neural network to optimize the machine learning task performance.
Inventor(s): Masoud Mohseni of Calabasas CA (US) for google llc, Hartmut Neven of Malibu CA (US) for google llc
IPC Code(s): G06N10/40, G06F15/82, G06F17/11, G06N7/01, G06N10/20, G06N10/60, G06N20/00, H10N60/10, H10N60/12, H10N60/80
CPC Code(s): G06N10/40
Abstract: among other things, an apparatus comprises quantum units; and couplers among the quantum units. each coupler is configured to couple a pair of quantum units according to a quantum hamiltonian characterizing the quantum units and the couplers. the quantum hamiltonian includes quantum annealer hamiltonian and a quantum governor hamiltonian. the quantum annealer hamiltonian includes information bearing degrees of freedom. the quantum governor hamiltonian includes non-information bearing degrees of freedom that are engineered to steer the dissipative dynamics of information bearing degrees of freedom.
20250077934. MACHINE LEARNING RANKING DISTILLATION_simplified_abstract_(google llc)
Inventor(s): Gil Shamir of Sewickley PA (US) for google llc, Zhuoshu Li of Pittsburgh PA (US) for google llc
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium for training and using distilled machine learning models. in one aspect, a method includes obtaining a first input that includes training example sets that each include one or more feature values and, for each item, an outcome label that represents whether the item had a positive outcome. a first machine learning model is trained using the first input and is configured to generate a set of scores that represents whether the item will have a positive outcome when presented in the context of the training example set and with each other item in the example set. a distilled machine learning model is trained using the set of scores for each example set. the distilled machine learning model is configured to generate a distilled score.
20250077963. ACCOUNT AGGREGATION USING MACHINE LEARNING_simplified_abstract_(google llc)
Inventor(s): Dongeek SHIN of Mountain View CA (US) for google llc
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: methods, systems, and apparatus, including medium-encoded computer program products, for aggregating accounts using machine learning. user interaction data can be obtained for a user and can describe interactions by the user with a given account of multiple different accounts assigned to the user on one or more computer systems. an input that includes the user interaction data is processed using a machine learning model that is configured to produce a result that includes a first account embedding that differs from the user interaction data. from at least the first account embedding, an account group is determined that corresponds to the user interaction data. a first action is performed based on the account group, wherein the first action differs from a second action that would have been performed based on a different account group that is not the account group.
Inventor(s): Rohit Thomas Aggarwala of New York NY (US) for google llc, Sandra Rothbard of New York NY (US) for google llc, Corinna Li of Toronto (CA) for google llc, Willa Ng of New York NY (US) for google llc, Jiten Manglani of New York NY (US) for google llc, Landry Doyle Wiese of New York NY (US) for google llc, Nerissa Moray of Brooklyn NY (US) for google llc
IPC Code(s): G06Q10/0832, A47G29/14, G06F16/901, G06Q10/0631, G06Q10/083, G06Q10/0834, G06Q10/087, G07C9/00, G07C9/22, G07C9/27
CPC Code(s): G06Q10/0832
Abstract: container devices and delivery systems for using the same are provided. in accordance with some embodiments of the disclosed subject matter, a method for delivering packages is provided that includes: receiving, at a delivery hub, a first package to be delivered to a recipient; causing the first package to be placed in a container to be delivered to the recipient; associating an identifier of the first package and an identifier of the container with the recipient; determining, at a first time point, whether the container is ready to be delivered to the recipient; in response to determining that the container is not ready to be delivered to the recipient, waiting for a second package to be delivered to the recipient; receiving the second package to be delivered to the recipient; causing the second package to be placed in the container; associating an identifier of the second package with the identifier of the container; determining, at a second time point, whether the container is ready to be delivered to the recipient; and, in response to determining that the container is ready to be delivered to the recipient, causing the container to be loaded onto a delivery vehicle.
Inventor(s): Leonard Guangyong Chan of Singapore (SG) for google llc, Yohan Jonathan Launay of Singapore (SG) for google llc
IPC Code(s): G06T11/60
CPC Code(s): G06T11/60
Abstract: the technology described herein is directed to artificial intelligence (ai) powered tools that can generate, enhance, and evaluate digital imagery. for example, the ai-powered tools can be used to generate personalized digital illustrations based on user profile information. in some examples, the tools can modify the personalized digital illustrations, such as by modifying a facial expression of a person depicted in the personalized digital illustration to correspond to a mood or tone of the illustration.
Inventor(s): Pradyumna Narayana of Bothell WA (US) for google llc, Garima Pruthi of San Jose CA (US) for google llc
IPC Code(s): G06T11/60, G06F40/279, G06F40/40, G06T7/194
CPC Code(s): G06T11/60
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enabling artificial intelligence to generate new images based on contextual data and to generate digital components based on the images. in one aspect, a method includes receiving one or more queries from a client device of a user. a digital component is selected based on the one or more queries. a customized digital component is generated by obtaining an image of an object corresponding to the selected digital component and generating, using a language model, an image editing prompt for editing the image based on digital component data related to the digital component and query data including the one or more queries and contextual data. the image and the image editing prompt are provided to an image editing model. an edited image is received and used to generate the customized digital component.
20250078397. PRIOR FOR HIGH-RESOLUTION IMAGE SYNTHESIS_simplified_abstract_(google llc)
Inventor(s): Abhimitra Meka of Redwood City CA (US) for google llc, Marcel Bühler of Truttikon (CH) for google llc, Kripasindhu Sarkar of Zurich (CH) for google llc, Tanmay Shah of San Ramon CA (US) for google llc, Gengyan Li of Volketswil (CH) for google llc, Daoye Wang of Zurich (CH) for google llc, Leonhard Helminger of Zurich (CH) for google llc, Sergio Orts Escolano of San Francisco CA (US) for google llc, Dmitry Lagun of San Jose CA (US) for google llc, Thabo Beeler of Egg (CH) for google llc
IPC Code(s): G06T15/20, G06T5/60
CPC Code(s): G06T15/205
Abstract: a method including determining a viewpoint, generating a first image using an image generator, the first image including an object in a first orientation based on the viewpoint, modifying the image generator based on a second orientation of the object, and generating a second image based on the first image using the modified image generator.
20250078484. MULTIMODAL EMBEDDINGS_simplified_abstract_(google llc)
Inventor(s): Tuan Nguyen of San Jose CA (US) for google llc, Sergei Volnov of London (GB) for google llc, Yunfan Ye of Sunnyvale CA (US) for google llc, Alexey Galata of San Jose CA (US) for google llc, William A. Truong of San Jose CA (US) for google llc, Tzu-Chan Chuang of San Francisco CA (US) for google llc, Liang-yu Chen of Sunnyvale CA (US) for google llc, Qiong Huang of San Jose CA (US) for google llc, Krunal Shah of Mountain View CA (US) for google llc, Sai Aditya Chitturu of Sunnyvale CA (US) for google llc, Sana Mithani of Plantation FL (US) for google llc
IPC Code(s): G06V10/80, G06V40/16, G10L15/183, G10L15/30
CPC Code(s): G06V10/80
Abstract: implementations relate to generating and using multimodal embeddings. in various implementations, first modality data may be obtained and encoded into first modality embedding(s) using a trained first modality encoder that is stored in memory of edge-based client device(s). second modality data may be obtained and encoded into second modality embedding(s) using a trained second modality encoder that is also stored in the memory of the edge-based client device(s). the first and second modality embeddings may be processed using an edge-based multimodal llm that is also stored locally in memory of the edge-based client device(s) to generate a multimodal contextual embedding, which may be provided to a remote server that hosts a central llm, e.g., in conjunction with a natural language input provided by the user. information generated using the central llm, responsive to the natural language input, may be received from the remote server.
20250078494. Methods and Systems for Encoding Images_simplified_abstract_(google llc)
Inventor(s): Shumeet Baluja of Leesburg VA (US) for google llc, Rahul Sukthankar of Orlando FL (US) for google llc
IPC Code(s): G06V10/94, G06V10/774, G06V10/82
CPC Code(s): G06V10/95
Abstract: the present disclosure is directed to encoding images. in particular, one or more computing devices can receive data representing one or more machine learning (ml) models configured, at least in part, to encode images comprising objects of a particular type. the computing device(s) can receive data representing an image comprising one or more objects of the particular type. the computing device(s) can generate, based at least in part on the data representing the image and the data representing the ml model(s), data representing an encoded version of the image that alters at least a portion of the image comprising the object(s) such that when the encoded version of the image is decoded, the object(s) are unrecognizable as being of the particular type by one or more object-recognition ml models based at least in part upon which the ml model(s) configured to encode the images were trained.
Inventor(s): Firas Sammoura of Dublin CA (US) for google llc, James Brooks Miller of Sunnyvale CA (US) for google llc
IPC Code(s): G06V40/13, G06V40/12
CPC Code(s): G06V40/1318
Abstract: this document describes methods and systems of adaptive fingerprint-enrollment to finger characteristics using local under-display fingerprint sensors, udfps, in an electronic device. the electronic device includes an adaptive-enrollment module that determines characteristics of a fingerprint based on information corresponding to a touch input detected by a touch-display device, including size and shape of an area of the touch input. based on the fingerprint characteristics, a number and location of enrollment touches used for completing enrollment of the fingerprint are adjusted to minimize the number of enrollment touches required to complete the enrollment, minimize the amount of time needed to complete the enrollment, and maximize coverage of the fingerprint. the adaptive-enrollment module also provides visual guidance to guide the user to touch the adjusted locations of the enrollment touches and, if needed, feedback to instruct the user to adjust the location of their finger to align with the visual guidance.
Inventor(s): Andrew M Rosenberg of Brooklyn NY (US) for google llc, Takaaki Saeki of Mountain View CA (US) for google llc, Francoise Beaufays of Mountain View CA (US) for google llc, Bhuvana Ramabhadran of Mt. Kisoo NY (US) for google llc
IPC Code(s): G10L13/02, G10L25/30
CPC Code(s): G10L13/02
Abstract: a method includes receiving training data that includes a plurality of sets of training utterances each associated with a respective language. each training utterance includes a corresponding reference speech representation paired with a corresponding input text sequence. for each training utterance, the method includes generating a corresponding encoded textual representation for the corresponding input text sequence, generating a corresponding speech encoding for the corresponding reference speech representation, generating a shared encoder output, and determining a text-to-speech (tts) loss based on the corresponding encoded textual representation, the corresponding speech encoding, and the shared encoder output. the method also includes training a tts model based on the tts losses determined for the training utterances in each set of the training utterances to teach the tts model to learn how to synthesize speech in each of the respective languages.
20250078807. Injecting Text in Self-Supervised Speech Pre-training_simplified_abstract_(google llc)
Inventor(s): Zhehuai Chen of Jersey City NJ (US) for google llc, Bhuvana Ramabhadran of Mt. Kisco NY (US) for google llc, Andrew M. Rosenberg of Brooklyn NY (US) for google llc, Yu Zhang of Mountain View CA (US) for google llc, Pedro J. Moreno Mengibar of Jersey City NJ (US) for google llc
IPC Code(s): G10L13/047, G10L13/08
CPC Code(s): G10L13/047
Abstract: a method includes receiving training data that includes unspoken text utterances and un-transcribed non-synthetic speech utterances. each unspoken text utterance is not paired with any corresponding spoken utterance of non-synthetic speech. each un-transcribed non-synthetic speech utterance is not paired with a corresponding transcription. the method also includes generating a corresponding synthetic speech representation for each unspoken textual utterance of the received training data using a text-to-speech model. the method also includes pre-training an audio encoder on the synthetic speech representations generated for the unspoken textual utterances and the un-transcribed non-synthetic speech utterances to teach the audio encoder to jointly learn shared speech and text representations.
Inventor(s): Lev Finkelstein of Mountain View CA (US) for google llc, Chun-an Chan of Mountain View CA (US) for google llc, Byungha Chun of Tokyo (JP) for google llc, Norman Casagrande of London (GB) for google llc, Yu Zhang of Mountain View CA (US) for google llc, Robert Andrew James Clark of Hertfordshire (GB) for google llc, Vincent Wan of London (GB) for google llc
IPC Code(s): G10L13/08, G10L13/047
CPC Code(s): G10L13/08
Abstract: a method includes obtaining training data including a plurality of training audio signals and corresponding transcripts. each training audio signal is spoken by a target speaker in a first accent/dialect. for each training audio signal of the training data, the method includes generating a training synthesized speech representation spoken by the target speaker in a second accent/dialect different than the first accent/dialect and training a text-to-speech (tts) system based on the corresponding transcript and the training synthesized speech representation. the method also includes receiving an input text utterance to be synthesized into speech in the second accent/dialect. the method also includes obtaining conditioning inputs that include a speaker embedding and an accent/dialect identifier that identifies the second accent/dialect. the method also includes generating an output audio waveform corresponding to a synthesized speech representation of the input text sequence that clones the voice of the target speaker in the second accent/dialect.
20250078809. END-TO-END TEXT-TO-SPEECH CONVERSION_simplified_abstract_(google llc)
Inventor(s): Samuel Bengio of Los Altos CA (US) for google llc, Yuxuan Wang of Sunnyvale CA (US) for google llc, Zongheng Yang of Berkeley CA (US) for google llc, Zhifeng Chen of Sunnyvale CA (US) for google llc, Yonghui Wu of Fremont CA (US) for google llc, Ioannis Agiomyrgiannakis of London (GB) for google llc, Ron J. Weiss of New York NY (US) for google llc, Navdeep Jaitly of Mountain View CA (US) for google llc, Ryan M. Rifkin of Oakland CA (US) for google llc, Robert Andrew James Clark of Hertfordshire (GB) for google llc, Quoc V. Le of Sunnyvale CA (US) for google llc, Russell J. Ryan of Mountain View CA (US) for google llc, Ying Xiao of San Bruno CA (US) for google llc
IPC Code(s): G10L13/08, G06N3/045, G06N3/08, G06N3/084, G10L13/04, G10L15/16, G10L25/18, G10L25/30
CPC Code(s): G10L13/08
Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating speech from text. one of the systems includes one or more computers and one or more storage devices storing instructions that when executed by one or more computers cause the one or more computers to implement: a sequence-to-sequence recurrent neural network configured to: receive a sequence of characters in a particular natural language, and process the sequence of characters to generate a spectrogram of a verbal utterance of the sequence of characters in the particular natural language; and a subsystem configured to: receive the sequence of characters in the particular natural language, and provide the sequence of characters as input to the sequence-to-sequence recurrent neural network to obtain as output the spectrogram of the verbal utterance of the sequence of characters in the particular natural language.
Inventor(s): Yonghui Xiao of Sunnyvale CA (US) for google llc, Françoise Beaufays of Mountain View CA (US) for google llc, Yuxin Ding of San Francisco CA (US) for google llc
IPC Code(s): G10L15/06, G06N3/098, G10L15/183, G10L15/30
CPC Code(s): G10L15/063
Abstract: implementations described herein are directed to a framework for decentralized learning of large global machine learning (ml) model(s). in various implementations, remote processor(s) of a remote system can identify a global ml model, select client devices to participate in a given round of decentralized learning of the global ml model, and transmit, to each of the client devices, a processed version of the global ml model that is of a reduced transferrable size. further, client device processor(s) of a client device can receive the processed version of the global ml model, obtain corresponding client data, perform partial model training, based on processing the corresponding client data, for the processed version of the global ml model to generate a corresponding update, and transmit the corresponding update back to the remote system. moreover, the remote processor(s) can update, based on at least the corresponding update, the global ml model.
Inventor(s): Kartik Audhkhasi of Mountain View CA (US) for google llc, Gowtham Ramesh of Mountain View CA (US) for google llc, Bhuvana Ramabhadran of Mt. Kisco NY (US) for google llc
IPC Code(s): G10L15/06
CPC Code(s): G10L15/063
Abstract: a method includes training, using an un-supervised learning technique, an auxiliary asr model based on a first set of un-transcribed source task speech utterances to determine a first task vector, training, using the un-supervised learning technique, the auxiliary asr model based on a second set of un-transcribed speech utterances to determine a second task vector, and training, using the un-supervised learning technique, the auxiliary asr model based on un-transcribed target task speech utterances to determine a target task vector. the method also includes determining a first correlation between the first and target task vectors, determining a second correlation between the second and target task vectors, and adapting parameters of a trained primary asr model based on the first and second source task vectors and the first and second correlations to teach the primary asr model to learn how to recognize speech associated with the target task.
Inventor(s): Shaojin Ding of Mountain View CA (US) for google llc, David Qiu of Fremont CA (US) for google llc, David Rim of Mountain View CA (US) for google llc, Amir Yazdanbakhsh of Mountain View CA (US) for google llc, Yanzhang He of Mountain View CA (US) for google llc, Zhonglin Han of Mountain View CA (US) for google llc, Rohit Prakash Prabhavalkar of Santa Clara CA (US) for google llc, Weiran Wang of Iowa City IA (US) for google llc, Bo Li of Fremont CA (US) for google llc, Jian Li of Mountain View CA (US) for google llc, Tara N. Sainath of Jersey City NJ (US) for google llc, Shivani Agrawal of Mountain View CA (US) for google llc, Oleg Rybakov of Mountain View CA (US) for google llc
IPC Code(s): G10L15/06
CPC Code(s): G10L15/063
Abstract: a method includes obtaining a plurality of training samples that each include a respective speech utterance and a respective textual utterance representing a transcription of the respective speech utterance. the method also includes fine-tuning, using quantization and sparsity aware training with native integer operations, a pre-trained automatic speech recognition (asr) model on the plurality of training samples. here, the pre-trained asr model includes a plurality of weights and the fine-tuning includes pruning one or more weights of the plurality of weights using a sparsity mask and quantizing each weight of the plurality of weights based on an integer with a fixed-bit width. the method also includes providing the fine-tuned asr model to a user device.
20250078820. Scalable High-Accuracy Transactional Agents_simplified_abstract_(google llc)
Inventor(s): Aishwariya Pattabiraman of Fremont CA (US) for google llc, Scott Bradley Huffman of Portola Valley CA (US) for google llc, Siddhartha Reddy Jonnalagadda of Sunnyvale CA (US) for google llc, Ashwin Ram of Los Altos CA (US) for google llc, Lee Boonstra of Amstelveen (NL) for google llc, Erick Armbrust of San Francisco CA (US) for google llc, Jack Fales of San Francisco CA (US) for google llc, Yingchao Huang of Fremont CA (US) for google llc, Adrian Otto of Mountain View CA (US) for google llc, Matthew O'Connor of Los Gatos CA (US) for google llc
IPC Code(s): G10L15/18, G10L15/22
CPC Code(s): G10L15/1815
Abstract: aspects of the disclosure are directed to a transactional agent for user interactions. the agent can seamlessly respond to user requests in a conversational manner while maintaining the conversational state. the agent can include a multi-stage modular model architecture, including a semantic understander and a semantic matcher. the semantic understander can be configured to understand common conversation conventions and/or patterns to produce a structure representation of a user request. the semantic matcher can be configured to map items and modifiers to product entries for a particular domain.
Inventor(s): Junwen Bai of Mountain View CA (US) for google llc, Bo Li of Fremont CA (US) for google llc, Qiujia Li of Mountain View CA (US) for google llc, Tara N. Sainath of Jersey City NJ (US) for google llc, Trevor Strohman of Mountain View CA (US) for google llc
IPC Code(s): G10L15/197, G10L15/00, G10L15/02, G10L15/06, G10L15/30
CPC Code(s): G10L15/197
Abstract: a method includes receiving a sequence of acoustic frames characterizing a spoken utterance in a particular native language. the method also includes generating a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames by a causal encoder that includes an initial stack of multi-head attention layers. the method also includes generating a second higher order feature representation for a corresponding first higher order feature representation by a non-causal encoder that includes a final stack of multi-head attention layers. the method also includes receiving, as input at each corresponding language-dependent adapter (lda) module, a language id vector identifying the particular native language to activate corresponding language-dependent weights specific to the particular native language. the method also includes generating a first probability distribution over possible speech recognition hypotheses by a decoder.
20250078840. TARGET SPEAKER KEYWORD SPOTTING_simplified_abstract_(google llc)
Inventor(s): Pai Zhu of New York City NY (US) for google llc, Beltrán Labrador Serrano of Madrid (ES) for google llc, Guanlong Zhao of Long Island City NY (US) for google llc, Angelo Alfredo Scorza Scarpati of New York NY (US) for google llc, Quan Wang of Hoboken NJ (US) for google llc, Alex Seungryong Park of Mountain View CA (US) for google llc, Ignacio Lopez Moreno of Jersey City NJ (US) for google llc
IPC Code(s): G10L17/02, G10L17/04, G10L17/22
CPC Code(s): G10L17/02
Abstract: a method includes receiving audio data corresponding to an utterance spoken by a particular user and captured in streaming audio by a user device. the method also includes performing speaker identification on the audio data to identify an identity of the particular user that spoke the utterance. the method also includes obtaining a keyword detection model personalized for the particular user based on the identity of the particular user that spoke the utterance. the keyword detection model is conditioned on speaker characteristic information associated with the particular user to adapt the keyword detection model to detect a presence of a keyword in audio for the particular user. the method also includes determining that the utterance includes the keyword using the keyword detection model personalized for the particular user.
Inventor(s): Yunpeng Li of Zurich (CH) for google llc, Marco Tagliasacchi of Ruvigliana (TI) (CH) for google llc, Dominik Roblek of Meilen (CH) for google llc, Félix de Chaumont Quitry of Zurich (CH) for google llc, Beat Gfeller of Dubendorf (CH) for google llc, Hannah Raphaelle Muckenhirn of Zurich (CH) for google llc, Victor Ungureanu of Thalwil (CH) for google llc, Oleg Rybakov of Redmond WA (US) for google llc, Karolis Misiunas of Zurich (CH) for google llc, Zalán Borsos of Zurich (CH) for google llc
IPC Code(s): G10L19/022, G06N3/045
CPC Code(s): G10L19/022
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing an input audio waveform using a generator neural network to generate an output audio waveform. in one aspect, a method comprises: receiving an input audio waveform; processing the input audio waveform using an encoder neural network to generate a set of feature vectors representing the input audio waveform; and processing the set of feature vectors representing the input audio waveform using a decoder neural network to generate an output audio waveform that comprises a respective output audio sample for each of a plurality of output time steps.
Inventor(s): Stefan Lindmark of Östhammar (SE) for google llc
IPC Code(s): G11B27/02
CPC Code(s): G11B27/02
Abstract: systems and methods for generating a 3d effect for a video stream are provided. a first video stream from a first client device of a first participant of a virtual meeting and a second video stream from a second client device of a second participant of the virtual meeting is identified. a background and a foreground layer of the first video stream is determined. a first and a second eye position of the second participant of the second video stream are determined. a presentation position of the background layer relative to the foreground layer is determined based on movement between the first and the second eye position of the second participant of the second video stream. a ui presenting the first video stream reflecting the determined presentation position of the background layer relative to the foreground layer is provided for display on the second client device.
20250079692. HINGE INCLUDING ANTENNA_simplified_abstract_(google llc)
Inventor(s): Matthew Thomas Valente of Sunnyvale CA (US) for google llc, Srivatsan Ravindran of San Jose CA (US) for google llc, Sajeev Alakkatt Paleri of Santa Clara CA (US) for google llc
IPC Code(s): H01Q1/27, G06F1/16, H01Q21/00
CPC Code(s): H01Q1/273
Abstract: a hinge for a computing device includes at least one antenna extending from a body portion of the hinge into a body portion of the computing device. when incorporated into a head mounted wearable device, the hinge couples an arm portion to a front frame portion of the device, with the at least one antenna extending into an installation area defined by the front frame portion. the at least one antenna does not occupy installation space in the arm portions, providing additional space for other electrical components of the device, and provides for modularity in coupling a variety of arm portions housing different arrangements and/or combinations of electrical components to a variety of different front frames.
Inventor(s): Xiaohu Zhou of Sunnyvale CA (US) for google llc, Dayu Qu of Cupertino CA (US) for google llc
IPC Code(s): H02J3/01, H02J1/00, H02J3/00, H02J3/14, H04N7/18
CPC Code(s): H02J3/01
Abstract: this document describes systems for and techniques of alternating-current (ac) power harmonic-based circuit state detection. in various aspects, a system includes a component, a bypass circuit for the component, and a controller with an ac power harmonic-based circuit state detector that can determine a state of the bypass circuit. the ac power harmonic-based circuit state detector may convert an ac voltage of the ac power to a direct current (dc) voltage, filter the dc voltage to obtain a voltage of a harmonic of the ac power, and compare the voltage of the harmonic to a threshold to determine that the bypass circuit is in a fault state (blown fuse). by so doing, the controller of the system can notify a user that the bypass circuit needs to be reset or replaced to reenable operation of the system and avoid poor user experience typically associated with a non- or mis-functioning system.
Inventor(s): Gang Wang of Frederick MD (US) for google llc, Marcel M. Moti Yung of New York NY (US) for google llc, Sheldon I. Walfish of Palo Alto CA (US) for google llc
IPC Code(s): H04L9/32, G06F21/53
CPC Code(s): H04L9/3213
Abstract: disclosed herein are systems, methods, and computer-readable media for enabling more secure multi-party computations (mpcs) using a trusted execution environment (tee). in one aspect, a method includes executing, by a first mpc computer, a secure mpc protocol in a first tee of the first mpc computer. the first mpc computer generates a request to a second mpc computer executing the secure mpc protocol in a second tee of the second mpc computer. the first tee determines that one or more attestation conditions are met by the first mpc computer executing the secure mpc protocol in the first tee. in response to determining that the one or more attestation conditions are met, the first tee generates an attestation token including one or more digital signatures for the secure mpc protocol executing in the first tee. the first mpc computer sends the attestation token with the request to the second mpc computer.
Inventor(s): Omer Berkman of Tel Aviv (IL) for google llc, Marcel M.M. Yung of New York NY (US) for google llc
IPC Code(s): H04L9/40, H04L9/14, H04L9/32
CPC Code(s): H04L63/0428
Abstract: provided are computer systems which demonstrate improved cryptographic agility via inclusion of multiple cryptographic operating modes. in one example, one or more devices included within a computing system are designed to include multiple cryptographic operating modes from the outset (e.g., prior to deployment of the system, “by design”). additionally or alternatively, one or more devices included within the computing system (e.g., a gateway computing device) can be updated to include the multiple cryptographic operating modes after deployment of the system (e.g., in an “ad hoc” fashion). a system can also include both device(s) that have multiple operating modes by design and device(s) that have multiple operating modes introduced in an ad hoc fashion. inclusion of the multiple cryptographic operating modes can serve to enhance the security of at least the communications of the computing system that are performed by or follow the updated device(s).
20250080640. HIERARCHICAL MOBILE APPLICATION LAUNCH_simplified_abstract_(google llc)
Inventor(s): Noel Billig of Mountain View CA (US) for google llc, Rachel Been of Mountain View CA (US) for google llc, Sameer Bansal of Mountain View CA (US) for google llc, Michelle Alvarez of Mountain View CA (US) for google llc, Clarke Harris of Sarasota FL (US) for google llc, Christopher Conover of San Carlos CA (US) for google llc
IPC Code(s): H04M1/72415, G05B15/02
CPC Code(s): H04M1/72415
Abstract: various arrangements are presented for performing a hierarchical application launch of an application. a requests to register one or more smart home devices can be received in association with a user account. the smart home devices can be mapped to the user account based on receiving the requests. an application that is mapped to the user account may be launched; the application can analyze the one or more smart home devices registered to the user account and a user interface hierarchy. based on analyzing the one or more smart home devices registered to the user account and the user interface hierarchy, an initial launch interface can be selected and output for presentation.
Inventor(s): Danny Hong of New York NY (US) for google llc, Zhuo Chen of Menlo Park CA (US) for google llc
IPC Code(s): H04N19/115, G06T7/50, H04N19/124
CPC Code(s): H04N19/115
Abstract: a cloud-based extended reality (xr) system includes a server configured to encode a set of frames each associated with an xr scene to be displayed. to encode the set of frames, the server estimates a total number of encoded output bits for the set of frames based on a set of quantization parameters (qps). the set of qps includes a corresponding qp for each frame of the set of frames and one or more predetermined relationships between the corresponding qps. the server then compares the estimated total number of encoded output bits to a target frame size threshold. based on the estimated total number of encoded bits being outside the target frame size threshold, the server updates the set of qps so as to maintain the predetermined relationships between the qps.
Inventor(s): James Bankoski of Los Gatos CA (US) for google llc, Yaowu Xu of Saratoga CA (US) for google llc, Paul Wilkins of Cambridge (GB) for google llc
IPC Code(s): H04N19/80, H04N19/105, H04N19/107, H04N19/117, H04N19/127, H04N19/139, H04N19/172, H04N19/176, H04N19/179, H04N19/23, H04N19/527, H04N19/61
CPC Code(s): H04N19/80
Abstract: video coding using constructed reference frames may include generating, by a processor in response to instructions stored on a non-transitory computer readable medium, a reconstructed video. generating the reconstructed video may include receiving an encoded bitstream. video coding using constructed reference frames may include generating a reconstructed non-showable reference frame. generating the reconstructed non-showable reference frame may include decoding a first encoded frame from the encoded bitstream. video coding using constructed reference frames may include generating a reconstructed frame. generating the reconstructed frame may include decoding a second encoded frame from the encoded bitstream using the reconstructed non-showable reference frame as a reference frame. video coding using constructed reference frames may include including the reconstructed frame in the reconstructed video and outputting the reconstructed video.
20250080856. Computational Photography Under Low-Light Conditions_simplified_abstract_(google llc)
Inventor(s): Jinglun Gao of San Mateo CA (US) for google llc, Ruben Manuel Velarde of Chula Vista CA (US) for google llc, Szepo Robert Hung of Austin TX (US) for google llc
IPC Code(s): H04N23/71, H04N23/60, H04N23/65
CPC Code(s): H04N23/71
Abstract: this document describes techniques and apparatuses for computational photography under low-light conditions for an image-capture device on a mobile computing device. in aspects, described are techniques and apparatuses for an image-capture device to utilize sensor data in determining whether to enable flash photography or capture multiple images of the scene without use of a flash under low-light conditions. in other aspects, an image-capture device may utilize device data in determining whether to enable flash photography or capture multiple images of the scene without use of a flash under low-light conditions. the disclosed techniques and apparatuses may provide improved computational photography under low-light conditions for an image-capture device on a mobile computing device.
Inventor(s): Artem Dementyev of Boston MA (US) for google llc, Richard Francis Lyon of Los Altos CA (US) for google llc, Pascal Tom Getreuer of San Francisco CA (US) for google llc, Alex Olwal of Santa Cruz CA (US) for google llc, Dmitrii Nikolayevitch Votintcev of San Francisco CA (US) for google llc
IPC Code(s): H04R1/40
CPC Code(s): H04R1/406
Abstract: the present disclosure provides computer-implemented methods, systems, and devices for capturing spatial sound for an environment. a computing system captures, using two or more microphones, audio data from an environment around a mobile device. the computing system analyzes the audio data to identify a plurality of sound sources in the environment around the mobile device based on the audio data. the computing system determines, based on characteristics of the audio data and data produced by one or more movement sensors, an estimated location for each respective sound source in the plurality of sound sources. the computing system generates a spatial sound recording of the audio data based, at least in part, on the estimated location of each respective sound source in the plurality of sound sources.
Inventor(s): Dongeek Shin of San Jose CA (US) for google llc, Gabriel Slotnick of Los Altos CA (US) for google llc
IPC Code(s): H04R5/04, H04R5/02, H04R27/00, H04R29/00, H04S7/00
CPC Code(s): H04R5/04
Abstract: methods, systems, and media for identifying a plurality of sets of coordinates for a plurality of devices are provided. in some embodiments, the method comprises: identifying each device in a plurality of devices associated with a user account; instructing the plurality of devices to perform an audio sequence; receiving a plurality of transit times from the plurality of devices; determining a plurality of distances based on the plurality of transit times; determining a plurality of sets of coordinates based on the plurality of distances; associating to each of the plurality of devices a corresponding unique one of the plurality of sets of coordinates; and causing at least one of the plurality of devices to play spatial audio determined from the plurality of sets of coordinates.
20250081047. Managing Quality of Experience Reporting_simplified_abstract_(google llc)
Inventor(s): Chih-Hsiang Wu of Taoyuan City (TW) for google llc, Teming Chen of Taoyuan City (TW) for google llc
IPC Code(s): H04W36/00
CPC Code(s): H04W36/0044
Abstract: a radio access network (ran); a core network (cn) or operations, administration, and management (oam) node; and a user equipment (ue) can implement a method for managing quality of experience (qoe) reporting from the ue. the method includes: facilitating qoe reporting to a qe node for the ue; determining to perform a handover for the ue from a source node of the ran to a target node of the ran; and pausing the qoe reporting. the method may further providing information to identify a qoe configuration of multiple configurations. the information may include qoe configurations, reference identifiers, qoe configuration identifiers, and information about associations between the reference identifiers and the qoe configuration identifiers.
Inventor(s): Amol Tuli of Dublin CA (US) for google llc, Pavan Santhana Krishna Nuggehalli of San Carlos CA (US) for google llc, Sooraj Sasindran of San Diego CA (US) for google llc, Jean-Francois Vincent-Girard of Cupertino CA (US) for google llc, Saravanaraj Duraisamy of San Francisco CA (US) for google llc
IPC Code(s): H04W64/00, H04W76/10, H04W84/06
CPC Code(s): H04W64/003
Abstract: a computer-implemented method is provided. the method includes determining, by a user device, that the user device is within range of a particular satellite. the method also includes sending, by the user device, a connection request to the particular satellite in response to determining that the user device is within range of the particular satellite. the connection request is forwarded to a network device, via the particular satellite, to indicate to the network device that the user device is available to receive messages via the particular satellite. the method also includes receiving, by the user device and via the particular satellite, a message from the network device in response to sending the connection request.
20250081280. MANAGING MEASUREMENT IN SMALL DATA TRANSMISSION_simplified_abstract_(google llc)
Inventor(s): Chih-Hsiang Wu of Taoyuan City (TW) for google llc
IPC Code(s): H04W76/27
CPC Code(s): H04W76/27
Abstract: one or more nodes of a radio access network (ran) perform a method of configuring or reconfiguring a user equipment (ue). the method includes communicating () data with the ue while the ue is in an inactive state and configured for small data transmission (sdt) operation, and determining () to configure or reconfigure one or more radio resources for the ue while the ue is in the inactive state and configured for sdt operation. the method also includes, transmitting (), in response to the determining and while the ue is in the inactive state and configured for sdt operation, a message to the ue to configure or reconfigure the one or more radio resources for the ue.
20250081290. MANAGING PAGING FOR MULTICAST AND BROADCAST SERVICES_simplified_abstract_(google llc)
Inventor(s): Chih-Hsiang Wu of Taoyuan City (TW) for google llc
IPC Code(s): H04W76/40, H04W68/02
CPC Code(s): H04W76/40
Abstract: a central node (cn) and a radio access network (ran) can implement a method for managing paging for multicast and broadcast services (mbs). the method includes: receiving, from an mbs network, an identifier for an mbs session; transmitting, to a ran, a message including the identifier for the mbs session; transmitting, to the ran, one or more parameters associated with the mbs session; and transmitting, to the ran, one or more mbs data packets to be broadcast to a user equipment (ue) in accordance with the one or more parameters.
20250081369. FOLDING PORTABLE DISPLAY DEVICE_simplified_abstract_(google llc)
Inventor(s): Tsung-Yuan Ou of New Taipei City (TW) for google llc
IPC Code(s): H05K5/02, E05D3/12, E05D3/18, G06F1/16
CPC Code(s): H05K5/0226
Abstract: an example folding device includes a hinge assembly that is coplanar with the continuous display of the device in order to decrease the thickness of the device. the hinge assembly includes torque members that increase the amount of force needed to rotate the assemblies. in this way, the torque members may provide the device with a more rigid feel. also in this way, the torque members may enable the device to hold intermediate positions between fully open and fully closed.
20250081397. Integrated Vapor Chamber for Electronic Devices_simplified_abstract_(google llc)
Inventor(s): Eric Chuang of New Taipei City (TW) for google llc, Victor Cheng of New Taipei City (TW) for google llc, Cheng-Lin Wang of Wujie Township (TW) for google llc
IPC Code(s): H05K7/20, H04M1/02
CPC Code(s): H05K7/20309
Abstract: this document describes a vapor chamber within an electronic device. in aspects, an electronic device includes a middle frame that provides mechanical support for the electronic device, a middle plate affixed to the middle frame to define an inner layer of a chassis, and a vapor chamber disposed inside the middle plate. the vapor chamber includes a first region proximate to a heat source and a second region opposite the first region. a coolant is evaporated in a first mode at the first region by heat absorbed from the heat source and is condensed in a second mode in the second region. this vapor chamber permits cooling of elements within the electronic device at lower cost and/or smaller size than many conventional cooling systems.
- Google LLC
- A44C5/14
- CPC A44C5/14
- Google llc
- A61B5/16
- A61B5/00
- A61B5/024
- CPC A61B5/165
- G01C21/34
- G01C21/36
- CPC G01C21/3476
- G01R31/3185
- CPC G01R31/318555
- G02B5/18
- G02B6/12
- G02B6/34
- G02B27/01
- CPC G02B5/1857
- F21V8/00
- G02B6/36
- CPC G02B6/0055
- CPC G02B27/0172
- G04C17/00
- G04C3/00
- G04G9/00
- G06F1/16
- CPC G04C17/0091
- CPC G06F1/1681
- G06F1/20
- F28F1/00
- CPC G06F1/20
- G06F3/041
- G06F3/042
- CPC G06F3/0412
- G06F9/48
- G06F9/30
- G06F9/32
- G06F9/38
- G06F15/78
- G06N3/063
- CPC G06F9/4881
- G06F12/121
- G06F12/127
- G06N3/044
- G06N20/00
- CPC G06F12/121
- G06F16/16
- G06F16/11
- G06F16/174
- G06F16/182
- G06F16/215
- CPC G06F16/162
- G06F16/18
- G06F16/22
- G06F16/23
- G06F16/28
- CPC G06F16/1805
- G06F12/02
- G06F16/242
- G06F3/01
- G06F16/2457
- G06F16/248
- G06F16/29
- G06F16/9537
- CPC G06F16/242
- G06F16/2455
- CPC G06F16/2455
- G06F16/9535
- G06F16/955
- G06F21/62
- CPC G06F16/9535
- G06F3/16
- G06F16/332
- G06F16/338
- G06F16/638
- G06F16/951
- G06F16/9538
- G06F40/186
- G06F40/20
- G06F40/58
- G10L13/00
- G10L15/22
- G10L15/30
- H04M1/60
- H04M1/72454
- H04R29/00
- CPC G06F16/9537
- G06F16/954
- G06F16/953
- G06N3/04
- CPC G06F16/954
- G06F18/214
- G06F17/18
- G06F18/2415
- G06N3/02
- G06V10/46
- CPC G06F18/2148
- G06F21/53
- CPC G06F21/53
- CPC G06F21/6245
- CPC G06F21/6254
- G06F40/14
- CPC G06F21/6263
- G06F40/284
- G06F40/30
- G06F40/40
- CPC G06F40/284
- G06N3/08
- CPC G06N3/04
- G06N3/0475
- CPC G06N3/0475
- G06N3/084
- CPC G06N3/084
- G06N3/0985
- G06N3/045
- CPC G06N3/0985
- G06N10/40
- G06F15/82
- G06F17/11
- G06N7/01
- G06N10/20
- G06N10/60
- H10N60/10
- H10N60/12
- H10N60/80
- CPC G06N10/40
- CPC G06N20/00
- G06Q10/0832
- A47G29/14
- G06F16/901
- G06Q10/0631
- G06Q10/083
- G06Q10/0834
- G06Q10/087
- G07C9/00
- G07C9/22
- G07C9/27
- CPC G06Q10/0832
- G06T11/60
- CPC G06T11/60
- G06F40/279
- G06T7/194
- G06T15/20
- G06T5/60
- CPC G06T15/205
- G06V10/80
- G06V40/16
- G10L15/183
- CPC G06V10/80
- G06V10/94
- G06V10/774
- G06V10/82
- CPC G06V10/95
- G06V40/13
- G06V40/12
- CPC G06V40/1318
- G10L13/02
- G10L25/30
- CPC G10L13/02
- G10L13/047
- G10L13/08
- CPC G10L13/047
- CPC G10L13/08
- G10L13/04
- G10L15/16
- G10L25/18
- G10L15/06
- G06N3/098
- CPC G10L15/063
- G10L15/18
- CPC G10L15/1815
- G10L15/197
- G10L15/00
- G10L15/02
- CPC G10L15/197
- G10L17/02
- G10L17/04
- G10L17/22
- CPC G10L17/02
- G10L19/022
- CPC G10L19/022
- G11B27/02
- CPC G11B27/02
- H01Q1/27
- H01Q21/00
- CPC H01Q1/273
- H02J3/01
- H02J1/00
- H02J3/00
- H02J3/14
- H04N7/18
- CPC H02J3/01
- H04L9/32
- CPC H04L9/3213
- H04L9/40
- H04L9/14
- CPC H04L63/0428
- H04M1/72415
- G05B15/02
- CPC H04M1/72415
- H04N19/115
- G06T7/50
- H04N19/124
- CPC H04N19/115
- H04N19/80
- H04N19/105
- H04N19/107
- H04N19/117
- H04N19/127
- H04N19/139
- H04N19/172
- H04N19/176
- H04N19/179
- H04N19/23
- H04N19/527
- H04N19/61
- CPC H04N19/80
- H04N23/71
- H04N23/60
- H04N23/65
- CPC H04N23/71
- H04R1/40
- CPC H04R1/406
- H04R5/04
- H04R5/02
- H04R27/00
- H04S7/00
- CPC H04R5/04
- H04W36/00
- CPC H04W36/0044
- H04W64/00
- H04W76/10
- H04W84/06
- CPC H04W64/003
- H04W76/27
- CPC H04W76/27
- H04W76/40
- H04W68/02
- CPC H04W76/40
- H05K5/02
- E05D3/12
- E05D3/18
- CPC H05K5/0226
- H05K7/20
- H04M1/02
- CPC H05K7/20309