GOOGLE LLC patent applications on April 17th, 2025
Patent Applications by GOOGLE LLC on April 17th, 2025
GOOGLE LLC: 37 patent applications
GOOGLE LLC has applied for patents in the areas of G10L15/22 (3), G06T7/70 (2), G06F40/40 (2), G06F16/532 (2), G06T3/40 (2) G10L15/22 (2), G06N3/0475 (2), G06F16/338 (2), G06F16/532 (2), G01C21/3881 (1)
With keywords such as: user, device, based, image, data, input, computing, assistant, processing, and include in patent application abstracts.
Patent Applications by GOOGLE LLC
Inventor(s): Victor Carbune of Zurich CH for google llc, Kevin Allekotte of Mountain View CA US for google llc
IPC Code(s): G01C21/00
CPC Code(s): G01C21/3881
Abstract: to provide dynamic generation and suggestion of map tiles, a server device receives from a user device a request for map data for a particular geographic region. the server device obtains a set of user contextual data and a set of candidate map tiles associated with the particular geographic region. the server device then selects one or more of the set of candidate map tiles based on the set of user contextual data, and transmits the one or more selected map tile to the user device for display.
Inventor(s): David Martin of Oakland CA US for google llc, Li-Ping Wang of Fremont CA US for google llc
IPC Code(s): G01S17/89, G01S17/86, G01S17/931, H04N23/60, H04N23/90
CPC Code(s): G01S17/89
Abstract: an image capture system includes a light detection and ranging (lidar) device which captures images of an environment while rotating and a plurality of cameras, disposed separately from the lidar device, each including a rolling shutter sensor and each capturing images of the environment. the plurality of cameras include a first camera disposed to face in a first direction and a second camera disposed to face in a second direction. a time at which the first camera captures a first image is synchronized with a time at which the lidar device captures a second image when the lidar device rotates to face in the first direction, and a time at which the second camera captures a third image is synchronized with a time at which the lidar device captures a fourth image when the lidar device rotates to face in the second direction.
20250123539. CAMERA ALIGNMENT_simplified_abstract_(google llc)
Inventor(s): Joseph Allore of Mundelein IL US for google llc, Ding Ran Dai of Chicago IL US for google llc, Michael J. Lombardi of South Barrington IL US for google llc, David Kyungtag Lim of Glenview IL US for google llc
IPC Code(s): G03B17/02
CPC Code(s): G03B17/02
Abstract: a computing device may include camera modules and a camera enclosure configured to house the camera modules. the computing device may further include alignment wedges that each define a sloped surface configured to mechanically contact at least one of the camera enclosure or the camera modules. the alignment wedges may be configured to reposition the respective camera module of the plurality of camera modules with respect to the camera enclosure.
Inventor(s): Ananya Simlai of London GB for google llc, Ming Wen of Santa Clara CA US for google llc, Ian Kenneth Coolidge of San Diego CA US for google llc, Santanu Dasgupta of Fremont CA US for google llc
IPC Code(s): G06F1/329
CPC Code(s): G06F1/329
Abstract: the presently disclosed technology provides methods and systems for optimally allocating power among workloads executing on a computer system through use of a power management algorithm. for example, according to the present technology a plurality of cpus within a server can be divided into multiple groups according to application workloads. workloads can be distributed to the cpus as needed by a workload scheduler, and the workload scheduler can provide the cpu ids to a power manager, enabling the power manager to optimize power settings. each group of cpus can be assigned an optimal power profile tailored to its respective situation.
20250124026. TEXT EMBEDDING GENERATION AND APPLICATIONS_simplified_abstract_(google llc)
Inventor(s): Xi Cheng of Kirkland WA US for google llc, Wen Zhang of Bellevue WA US for google llc, Jiashang Liu of Kirkland WA US for google llc, Mingge Deng of Kirkland WA US for google llc, Amir Hormati of Mountain View CA US for google llc, Omid Fatemieh of Bellevue WA US for google llc
IPC Code(s): G06F16/2452, G06F16/242, G06F40/40
CPC Code(s): G06F16/24522
Abstract: a method includes receiving a text embedding generation query from a user requesting generation of a text embedding for one or more data elements stored at a data warehouse. in response, the method includes selecting, using the text embedding generation query, a text embedding model from a plurality of different text embedding models. the method includes generating, using the selected text embedding model, the text embedding for the one or more data elements and storing the text embeddings at the data warehouse. the method includes receiving a machine learning model training query from the user device requesting training of a machine learning model using the text embeddings. in response to receiving the machine learning model training query, the method includes training the machine learning model using the text embeddings. the method includes providing, to the user device, a notification indicating that training of the machine learning model is complete.
Inventor(s): Bin Song of Fremont CA US for google llc, Dani Suleman of Fremont CA US for google llc, Kiran Kumar Gunda of Sunnyvale CA US for google llc
IPC Code(s): G06F16/27
CPC Code(s): G06F16/27
Abstract: techniques for generating replication data from a data source to be stored in a target location are described herein. a computing system can receive, from a client device, client requirements associated with a dataflow from the data source to the target location. the client requirements can include an expected data freshness value and an expected data query latency value. additionally, the computing system can process the expected data freshness value with one or more machine-learned models to generate an extraction framework for extracting data from the data source. moreover, the computing system can process the expected data query latency value with the one or more machine-learned models to generate a loading framework for loading data to the target location. furthermore, the computing system can copy the replication data from the data source to the target location based on the extraction framework and the loading framework.
Inventor(s): Zhen Qin of Jersey City NJ US for google llc, Rolf Jagerman of Diemen NL for google llc, Kai Hui of Mountain View CA US for google llc, Honglei Zhuang of Santa Clara CA US for google llc, Junru Wu of Jersey City NJ US for google llc, Jiaming Shen of Jersey City NJ US for google llc, Tianqi Liu of Jersey City NJ US for google llc, Jialu Liu of Jersey City NJ US for google llc, Donald Arthur Metzler, JR. of Sunnyvale CA US for google llc, Xuanhui Wang of Cupertino CA US for google llc, Michael Bendersky of Cupertino CA US for google llc
IPC Code(s): G06F16/338
CPC Code(s): G06F16/338
Abstract: provided are computing systems, methods, and platforms that rank text with pairwise ranking prompting using a generative sequence processing model. a prompt comprising a query and sets of text associated with candidate results can be generated. the generative sequence processing model can be prompted with the prompt and perform pairwise comparisons between the sets of text in the prompt based on the query in the prompt. an output can be generated that ranks the sets of text in response to the query.
[[20250124068. GENERATING A PERSONAL DATABASE ENTRY FOR A USER BASED ON NATURAL LANGUAGE USER INTERFACE INPUT OF THE USER AND GENERATING OUTPUT BASED ON THE ENTRY IN RESPONSE TO FURTHER NATURAL LANGUAGE USER INTERFACE INPUT OF THE USER_simplified_abstract_(google llc)]]
Inventor(s): Maryam Garrett of Cambridge MA US for google llc, Wan Fen Nicole Quah of Cambridge MA US for google llc, Bryan Horling of Roxbury Crossing MA US for google llc, Ruijie He of Roxbury Crossing MA US for google llc
IPC Code(s): G06F16/338, G06F16/334, G06F16/907, G06F40/103, G06F40/169, G06F40/30, H04W4/02, H04W4/029
CPC Code(s): G06F16/338
Abstract: some implementations are directed to generating a personal database entry for a user based on free-form natural language input formulated by the user via one or more user interface input devices of a computing device of the user. the generated personal database entry may include one or more terms of the natural language input and descriptive metadata determined based on one or more terms of the natural language input and/or based on contextual features associated with receiving the natural language input. some implementations are directed to generating, based on one or more personal database entries of a user, output that is responsive to further free-form natural language input of the user. for example, one or more entries that are responsive to further natural language input of the user can be identified based on matching content of those entries to one or more search parameters determined based on the further input.
Inventor(s): Harshit Kharbanda of Pleasanton CA US for google llc, Christopher James Kelley of Orinda CA US for google llc, Pendar Yousefi of Sunnyvale CA US for google llc
IPC Code(s): G06F16/532, G06F16/538, G06F16/54
CPC Code(s): G06F16/532
Abstract: systems and methods for textual replacement can include the determination of a visual intent, which can trigger an interface for selecting an image to replace visual descriptors. the visually descriptive terms can be identified, and an indicator can be provided to indicate the text replacement option may be initiated. an image can then be selected by a user to replace the visually descriptive terms.
20250124076. Query Categorization Based on Image Results_simplified_abstract_(google llc)
Inventor(s): Anna Majkowska of San Francisco CA US for google llc, Cristian Tapus of Santa Clara CA US for google llc
IPC Code(s): G06F16/532, G06N20/00
CPC Code(s): G06F16/532
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for query categorization based on image results. in one aspect, a method includes receiving images from image results responsive to a query, wherein each of the images is associated with an order in the image results and respective user behavior data for the image as a search result for the first query, and associating one or more of the first images with a plurality of annotations based on analysis of the selected first images' content.
Inventor(s): Jeffrey Thomas Andersen of Kirkland WA US for google llc, Marius Paul Michiel Schilder of Sunnyvale CA US for google llc
IPC Code(s): G06F21/60, G06F21/57, G06F21/64
CPC Code(s): G06F21/602
Abstract: example embodiments of the present disclosure provide for an example method including maintaining a current version info list including version info tuples for software layers. the example method includes, upon receipt of a request for a registered version key, performing a comparison algorithm to authenticate a requested version info list including a number of version info tuples associated with software layers. the tuples can include a security version number (svn) and a security context string for each software layer. the requested version info list can be authenticated using the comparison algorithm to determine that the requested version info list includes version info tuples with higher svns than the current version info list. responsive to authenticating the requested version info list, the method include providing a portion of the requested version info list as input into a key derivation function (kdf) and obtaining a device requested version key as output.
Inventor(s): Christopher Aaron Clark of Madison WI US for google llc, Sameer Agarwal of Seattle WA US for google llc, Craig Citro of Lopez Island WA US for google llc, Rasmus Munk Larsen of San Jose CA US for google llc
IPC Code(s): G06F30/30, G06F9/30
CPC Code(s): G06F30/30
Abstract: designing a circuit to perform a floating point arithmetic operation by identifying a multiple of parameters that characterize circuits for performing the floating point arithmetic operation and an equation relating the plurality of parameters to a maximum relative backward error parameter, the circuits respectively corresponding to combinations of values for the parameters; specifying a target maximum relative backward error for the floating point arithmetic operation; computing a maximum relative backward error for each of one or more of the combinations of values based on the equation; and when the maximum relative backward error for a respective combination of values is less than the target maximum relative backward error, identifying the circuit corresponding to the maximum relative backward error as a circuit operable to perform the floating point arithmetic operation at a desirable output accuracy.
Inventor(s): Ebrahim Songhori of San Jose CA US for google llc, Wenjie Jiang of Mountain View CA US for google llc, Sergio Guadarrama Cotado of Berkeley CA US for google llc, Young-Joon Lee of San Jose CA US for google llc, Azalia Mirhoseini of Mountain View CA US for google llc, Anna Darling Goldie of San Francisco CA US for google llc, Roger David Carpenter of San Francisco CA US for google llc, Yuting Yue of San Francisco CA US for google llc, Kuang-Huei Lee of San Francisco CA US for google llc, James Laudon of Madison WI US for google llc, Toby James Boyd of Lewis Center OH US for google llc, Quoc V. Le of Sunnyvale CA US for google llc
IPC Code(s): G06F30/392, G06F30/27, G06F30/394
CPC Code(s): G06F30/392
Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. one of the methods includes training, through reinforcement learning, a node placement neural network that is configured to, at each of a plurality of time steps, receive an input representation comprising data representing a current state of a placement of a netlist of nodes on a surface of an integrated circuit chip as of the time step and process the input representation to generate a score distribution over a plurality of positions on the surface of the integrated circuit chip.
Inventor(s): Rishabh Agarwal of Montreal CA for google llc, Nino Jean Vieillard of Paris FR for google llc, Matthieu Florent Geist of Ancy-Dornot FR for google llc, Olivier FrĂŠdĂŠric Bachem of Zurich CH for google llc
IPC Code(s): G06N3/0455, G06N3/092
CPC Code(s): G06N3/0455
Abstract: an example method is provided for training a machine-learned student sequence processing model, the method comprising: obtaining a respective input; obtaining, from the student machine-learned sequence processing model, a respective output corresponding to the respective input; generating a multiscale refinement objective configured to jointly distill knowledge from a teacher machine-learned sequence processing model and reinforce preferred behavior of the student machine-learned sequence processing model, wherein the multiscale refinement objective comprises: a first component based on a divergence metric characterizing, for the respective input, a comparison of a plurality of predictions of the student machine-learned sequence processing model to a plurality of predictions of the teacher machine-learned sequence processing model; and a second component based on a reinforcement learning signal associated with the respective output; and updating the machine-learned student sequence processing model based on the multiscale refinement objective.
Inventor(s): Ibrahim Badr of New York NY US for google llc
IPC Code(s): G06N3/0475, G06N3/08
CPC Code(s): G06N3/0475
Abstract: systems and methods for user-specific content generation can leverage parameter tuning based on user feedback data to tune a set of parameters for conditioning a machine-learned content generation model for the content generation. the set of parameters can be processed with the machine-learned content generation model to generate a model-generated content item that is associated with user tastes and interests. the parameter tuning can include processing user interactions associated with the model-generated content item to adjust the set of parameters.
Inventor(s): David M. Wang of Long Island City NY US for google llc, Gaurav Gupta of Dublin CA US for google llc, Gokhan Mergen of Los Gatos CA US for google llc, Baixu Chen of Sunnyvale CA US for google llc, Kumar Avinava Dubey of Palo Alto CA US for google llc, Amr Ahmed of Mountain View CA US for google llc
IPC Code(s): G06N3/0475, G06F40/166, G06F40/284, G06F40/30, G06F40/40, G06N3/096
CPC Code(s): G06N3/0475
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating descriptions of digital components. in one aspect, a method includes receiving data indicating a query received from a client device of a user. an initial digital component is obtained. search history data that includes a set of related past queries received from the user is obtained. updated text related to the first resource is generated by conditioning a language model with one or more contextual inputs that cause the language model to generate one or more outputs that include the updated text, the one or more contextual inputs characterizing one or more of the first query, data related to the initial digital component, the sequence of related past queries, or one or more tasks to be performed by the language model. an updated digital component that depicts the updated text is generated and provided.
Inventor(s): Srividya Pranavi Potharaju of Union City CA US for google llc, Shyam Upadhyay of El Paso TX US for google llc, Aman Madaan of Sunnyvale CA US for google llc, Ankit Anand of Montreal CA for google llc, Manaal Faruqui of Brooklyn NY US for google llc
IPC Code(s): G06N7/01
CPC Code(s): G06N7/01
Abstract: various implementations are directed towards generating, based on processing language model (lm) input using a first lm, an initial response that is predicted to be responsive to natural language (nl) based input, where the lm input includes at least the nl based input. additionally or alternatively, the system can determine whether to generate an additional response based on processing the lm input using a second lm, where determining whether to generate the additional response includes processing at least the lm input and initial response using at least one verifier to generate a verification score. in many implementations, the verification score can be processed using a meta-verifier to determine whether to render output based on the initial response or the additional response.
20250124537. Multi-scale Transformer for Image Analysis_simplified_abstract_(google llc)
Inventor(s): Junjie Ke of San Jose CA US for google llc, Feng Yang of Sunnyvale CA US for google llc, Qifei Wang of Mountain View CA US for google llc, Yilin Wang of Sunnyvale CA US for google llc, Peyman Milanfar of Menlo Park CA US for google llc
IPC Code(s): G06T3/04, G06T3/40, G06T7/00
CPC Code(s): G06T3/04
Abstract: the technology employs a patch-based multi-scale transformer () that is usable with various imaging applications. this avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. a native resolution image () is transformed into a multi-scale representation (), enabling the transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. spatial embedding () is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. a separate scale embedding () is employed to distinguish patches coming from different scales in the multiscale representation. self-attention () is performed to create a final image representation. in some instances, prior to performing self-attention, the system may prepend a learnable classification token () to the set of input tokens.
Inventor(s): Jyrki Alakuijala of Wollerau, Schwyz CH for google llc, Moritz Joachim Firsching of Rheinfelden DE for google llc
IPC Code(s): G06T3/40, G06F3/01, G06T5/70, G06V10/25, G06V10/46, G06V10/70, H04N19/136, H04N19/162, H04N19/167, H04N23/61, H04N23/63
CPC Code(s): G06T3/40
Abstract: a computer-implemented method is provided. the method includes receiving, via a computing device, a plurality of bytes of an encoded image, wherein the encoded image comprises a salient portion. the method further includes determining a bounding region for the encoded image, wherein the bounding region is indicative of a location of the salient portion in the encoded image. the method also includes progressively rendering a decoded version of the encoded image, wherein the progressively rendering comprises rendering a high resolution version of the bounding region, and a low resolution version of a portion outside the bounding region.
Inventor(s): Alejandro Jose Troccoli of San Jose CA US for google llc, Andrew Block of San Francisco CA US for google llc, Vineet Vijay Bhatawadekar of Seattle WA US for google llc, Alexander William Hake of Seattle WA US for google llc
IPC Code(s): G06T7/70, H04N7/15
CPC Code(s): G06T7/70
Abstract: techniques include a calibration assembly for a telepresence system that includes a stereoscopic display and a set of cameras. the calibration assembly may include at least one chart having chart markers, a mirror having mirror markers, and a processor. an example calibration assembly has three charts and the mirror attached to one of the charts. during calibration, the display is configured to display a set of display markers that are imaged in the mirror. each camera forms a respective image of the set of chart markers, the set of mirror markers, and the set of display markers. the processing circuitry then determines the poses of the cameras with respect to the display based on the images of the set of chart markers, the set of mirror markers, and the set of display markers.
20250124634. THREE-DIMENSIONAL HAND AND OBJECT MOTION SYNTHESIS_simplified_abstract_(google llc)
Inventor(s): Thabo Beeler of Egg CH for google llc, Jan BednarĂk of Zurich CH for google llc, Bardia Doosti of Toronto CA for google llc, Bernd Bickel of Zurich CH for google llc, Danhang Tang of West Hollywood CA US for google llc, Jonathan James Taylor of New York City NY US for google llc, Soshi Shimada of SaarbrĂźcken DE for google llc, Franziska MĂźller of Oberlunkhofen CH for google llc
IPC Code(s): G06T13/40, G06T5/60, G06T5/70
CPC Code(s): G06T13/40
Abstract: a method includes determining a trajectory of an object based on a mass of the object, and determining a motion of a hand based on the mass of the object and the trajectory of the object. the method can further include generating an animation of the hand interacting with the object based on the trajectory of the object and the motion of the hand.
Inventor(s): Berkin Akin of Burlingame CA US for google llc, Suyog Gupta of Sunnyvale CA US for google llc, Cao Gao of Sunnyvale CA US for google llc, Ping Zhou of Santa Clara CA US for google llc, Gabriel Mintzer Bender of Mountain View CA US for google llc, Hanxiao Liu of Santa Clara CA US for google llc
IPC Code(s): G06V10/82, G06V10/77, G06V10/94
CPC Code(s): G06V10/82
Abstract: methods, systems, and apparatus, including computer-readable media, are described for processing an input image using a convolutional neural network (cnn). the cnn includes a sequence of layer blocks. each of a first subset of the layer blocks in the sequence is configured to perform operations that include: i) receiving an input feature map for the layer block, ii) generating an expanded feature map from the input feature map using a group convolution, and iii) generating a reduced feature map from the expanded feature map. the input feature map is an h w feature map with c1 channels. the expanded feature map is an h w feature map with c2 channels, whereas the reduced feature map is an h w feature map with c1 channels. c2 is greater than c1. an output feature map is generated for the layer block from the reduced feature map.
Inventor(s): Marcin Nowak-Przygodzki of Bäch CH for google llc, GÜkhan Bakir of Zurich CH for google llc
IPC Code(s): G06V20/20, G06F3/01, G06F3/0482, G06F3/04886, G06F16/487, G06F16/9032, G06Q30/02, G06V20/68, H04N23/63, H04N23/667
CPC Code(s): G06V20/20
Abstract: techniques described herein enable a user to interact with an automated assistant and obtain relevant output from the automated assistant without requiring arduous typed input to be provided by the user and/or without requiring the user to provide spoken input that could cause privacy concerns (e.g., if other individuals are nearby). the assistant application can operate in multiple different image conversation modes in which the assistant application is responsive to various objects in a field of view of the camera. the image conversation modes can be suggested to the user when a particular object is detected in the field of view of the camera. when the user selects an image conversation mode, the assistant application can thereafter provide output, for presentation, that is based on the selected image conversation mode and that is based on object(s) captured by image(s) of the camera.
Inventor(s): Shen Yan of Seattle WA US for google llc, Tao Zhu of Los Altos CA US for google llc, Zirui Wang of Mountain View CA US for google llc, Yuan Cao of Mountain View CA US for google llc, Jiahui Yu of Seattle WA US for google llc
IPC Code(s): G06V20/40, G06F16/583
CPC Code(s): G06V20/41
Abstract: provided is an efficient approach to establish a foundational video-text model for tasks including open-vocabulary video classification, text-to-video retrieval, video captioning and video question-answering. some example implementations include a model which can be referred to as videococa. example implementations reuse a pretrained image-text contrastive captioner (coca) model and adapt it to video-text tasks with little or minimal extra training. while previous works adapt image-text models with various cross-frame fusion modules (for example, cross-frame attention layer or perceiver resampler) and finetune the modified architecture on video-text data, aspects of the present disclosure leverage findings that the generative attentional pooling and contrastive attentional pooling layers in the image-text coca design are instantly adaptable to âflattened frame embeddingsâ, yielding a strong zero-shot transfer baseline for many video-text tasks.
Inventor(s): Bo Li of Folsom CA US for google llc, Edwin Lyle Hudson of San Jose CA US for google llc
IPC Code(s): G09G3/32, G09G3/20
CPC Code(s): G09G3/32
Abstract: a word line regulator that can control an sram cell during a write process is disclosed. the sram cell is coupled to bit lines that are driven using a reduced voltage in order to conserve power. the sram cell includes a latch that is powered by a power supply in a power domain that is different from the bit lines. the word line regulator is configured to output a voltage that is based on the different power domains to ensure the proper operation of transistors in the sram cell during the write process.
Inventor(s): Liyang Rui of Irvine CA US for google llc, Guohua Sun of Santa Clara CA US for google llc, Lyle Nitta of Westminster CO US for google llc
IPC Code(s): G10K11/175, H04R1/10
CPC Code(s): G10K11/175
Abstract: adaptive howling suppression is provided for active noise control (anc) systems of wearable audio components (wacs), such as earbuds, based on detecting whether the wacs are presently being worn. upon first detecting howling, embodiments can pre-suppress the howling audio signature by reducing anc output. then, embodiments detect whether the wac is presently on-ear (worn) or off-ear and attempt to fully suppress the howling using different parameters, based on this detection. for example, if on-ear, embodiments use on-ear tuning settings to cycle through a set of suppression stages until anc output can be restored without howling; if off-ear, embodiments use off-ear tuning settings to cycle through the set of suppression stages until anc output can be restored without howling. once anc output can be restored without howling, the system can be released to a normal idle state.
Inventor(s): Amit Singhal of Sunnyvale CA US for google llc, Jordan Jozwiak of Belmont CA US for google llc
IPC Code(s): G10L15/22, B60W40/09, G06F40/279
CPC Code(s): G10L15/22
Abstract: implementations described herein relate to determining how an automated assistant should respond to a given spoken utterance received in a vehicular environment to enforce consistency between value(s) and/or unit(s) that are displayed at a given display device of an in-vehicle computing device and value(s) and/or unit(s) that are utilized in executing a given vehicular command or that are provided for presentation to a user in response to a given vehicular request. for example, implementations can receive the given spoken utterance, identify the given vehicular command/request based on processing the given spoken utterance, and determine an original equipment manufacturer (oem) query based on the given vehicular command/request included in the spoken utterance, and transmit the oem query to a given oem component. further, implementations can determine how the automated assistant should respond to the given spoken utterance based on responsive content that is received from the given oem component.
Inventor(s): Vikram Aggarwal of Palo Alto CA US for google llc, Vinod Krishnan of Santa Clara CA US for google llc
IPC Code(s): G10L15/22, G10L15/183, G10L15/26
CPC Code(s): G10L15/22
Abstract: implementations set forth herein relate to phasing-out of vehicle computing device versions while ensuring useful responsiveness of any vehicle computing device versions that are still in operation. certain features of updated computing devices may not be available to prior versions of computing devices because of hardware limitations. the implementations set forth herein eliminate crashes and wasteful data transmissions caused by prior versions of computing devices that have not been, or cannot be, upgraded. a server device can be responsive to a particular intent request provided to a vehicle computing device, despite the intent request being associated with an action that a particular version of the vehicle computing device cannot execute. in response, the server device can elect to provide speech to text data, and/or natural language understanding data, in furtherance of allowing the vehicle computing device to continue leveraging resources at the server device.
Inventor(s): Matthew Sharifi of Kilchberg CH for google llc, Victor Carbune of Zurich CH for google llc
IPC Code(s): G10L15/30, G10L15/00, G10L15/06, G10L15/22
CPC Code(s): G10L15/30
Abstract: implementations are directed to dynamically adapting which assistant on-device model(s) are locally stored at assistant devices of an assistant device group and/or dynamically adapting the assistant processing role(s) of the assistant device(s) of the assistant device group. in some of those implementations, the corresponding on-device model(s) and/or corresponding processing role(s), for each of the assistant devices of the group, is determined based on collectively considering individual processing capabilities of the assistant devices of the group. implementations are additionally or alternatively directed to cooperatively utilizing assistant devices of a group, and their associated post-adaptation on-device model(s) and/or post-adaptation processing role(s), in cooperatively processing assistant requests that are directed to any one of the assistant devices of the group.
Inventor(s): Wei Lu of San Jose CA US for google llc, Wangmuge Qin of Santa Clara CA US for google llc, Suleyman Yurekli of Zurich CH for google llc, Jeffrey Caesar of Campbell CA US for google llc, Mikhail Turilin of Sunnyvale CA US for google llc
IPC Code(s): H04L51/02, G06F3/16
CPC Code(s): H04L51/02
Abstract: implementations can receive user input during a dialog session between a user and an automated assistant at a client device of the user and via an automated assistant platform, and in response to determining that the user input requires a user interaction with a non-assistant platform: store a state of the dialog session between the user and the automated assistant, transmit a request to initiate the user interaction to the non-assistant platform that causes an additional client device of the user to render a prompt for completing the user interaction, and receive a token associated with the user interaction from the non-assistant platform. in response to receiving the token associated with the user interaction, implementations can cause the dialog session between the user and the automated assistant to be resumed based on the stored state of the dialog session and based on the token associated with the user interaction.
Inventor(s): Vinh Quoc Ly of Sunnyvale CA US for google llc, Yan Zhong of Sunnyvale CA US for google llc, Ashrith Sheshan of San Jose CA US for google llc, Xiaobin Yu of San Jose CA US for google llc
IPC Code(s): H04L51/58, G10L17/22, H04W4/12
CPC Code(s): H04L51/58
Abstract: techniques are described herein for using a smart device such as a standalone assistant-centric interactive speaker and/or a standalone assistant-centric interactive display with speaker(s) to send a message using a messaging application on a client device such as a smartphone. a method includes: receiving, by a first device, a request from a first user to send a message to a second user; determining that a messaging application corresponding to the request is unavailable on the first device; and in response to determining that the messaging application corresponding to the request is unavailable on the first device: selecting a second device on which the messaging application corresponding to the request is available; and sending, to the second device, a command that causes the second device to send the message from the first user to the second user using the messaging application on the second device.
Inventor(s): Dongeek Shin of San Jose CA US for google llc
IPC Code(s): H04L9/40
CPC Code(s): H04L63/102
Abstract: an example method is provided. unverified interaction data descriptive of interactions of a user with the computing device can be obtained. feature values that embed characteristics of the unverified interaction data can be generated using a machine-learned embedding network of a machine-learned verification pipeline. a user account associated with the unverified interaction data can be determined using a verification model of the machine-learned verification pipeline.
Inventor(s): Andrey Ryabtsev of Seattle WA US for google llc, Rahul Garg of Sunnyvale CA US for google llc, Amelio VĂĄzquez-Reina of Palo Alto CA US for google llc, Wonsik Kim of Menlo Park CA US for google llc, Robert Anderson of Bristol GB for google llc, Weijuan Xi of Cupertino CA US for google llc, Desai Fan of Snohomish WA US for google llc, Fangda Li of Mountain View CA US for google llc, Chun-Ting Liu of Issaquah WA US for google llc
IPC Code(s): H04N7/15, G06V10/26, G06V40/10
CPC Code(s): H04N7/157
Abstract: a first video stream comprising a first image of a first participant of a virtual meeting, a second image of a second participant, and a third image of a third participant are received from a first client device connected to a virtual meeting platform. it is determined whether an image combining condition is satisfied. responsive to determining that the image combining condition is satisfied with respect to the first image and the second image, a first screen tile comprising the first image and the second image is generated. a first size of the first screen tile is defined based on a number of images comprised by the first screen tile. a second screen tile comprising the third image is generated. a virtual meeting user interface comprising the first screen tile and the second screen tile is provided for presentation on a second client device connected to the virtual meeting platform.
Inventor(s): Jaclyn Konzelmann of Mountain View CA US for google llc, Tuan Nguyen of San Jose CA US for google llc, Vinay Bettadapura of San Jose CA US for google llc, Andrew Gallagher of Fremont CA US for google llc, Utsav Prabhu of Pittsburgh PA US for google llc, Caroline Pantofaru of San Carlos CA US for google llc
IPC Code(s): H04N21/442, G06T7/70, H04N21/258, H04N21/41, H04W12/64
CPC Code(s): H04N21/44218
Abstract: implementations relate to an automated assistant that provides and manages output from one or more elements of output hardware of a computing device. the automated assistant manages dynamic adjustment of access permissions to the computing device according to, for example, a detected presence of one or more users. an active-user queue can be established each time a unique user enters a viewing window of a camera of the computing device when, up to that point, no user was considered active. multiple image frames can be captured via the camera and processed to determine whether an initial user remains in the viewing window and/or whether another user has entered the viewing window. the initial user can be considered active as long as they are exclusively detected in the viewing window. restricted content associated with the user may be rendered by the computing device whilst the user is active.
20250126374. Random Modulation of Charge-Pump Noise Phases_simplified_abstract_(google llc)
Inventor(s): Qingfei Chen of Mountain View CA US for google llc, Kwang Oh Kim of San Jose CA US for google llc
IPC Code(s): H04N25/616, H04N25/78
CPC Code(s): H04N25/616
Abstract: techniques and apparatuses are described that implement random modulation of charge-pump noise phases to reduce structured noise induced by the charge pump. in an example aspect, a correlated double sampling (cds) circuit is coupled to a pixel array including at least one pixel circuit. the cds circuit receives an input signal generated by the at least one pixel circuit from the pixel array and samples a reset component of the input signal during a first sampling time to generate a reset component sample. the first sampling time is at a first offset from a reset control signal and prior to a settling time of the at least one pixel circuit. the cds circuit samples a signal component of the input signal during a second sampling time to generate a signal component sample and determines an output signal based on the reset component sample and the signal component sample.
20250126630. MANAGING MULTICAST CONFIGURATIONS_simplified_abstract_(google llc)
Inventor(s): Chih-Hsiang Wu of Taoyuan City TW for google llc
IPC Code(s): H04W72/30, H04W72/543
CPC Code(s): H04W72/30
Abstract: a base station can implement a method for configuring a multicast radio bearer (mrb) for a multicast and/or broadcast services (mbs) session. the method may include determining () that the mbs session or the mrb requires uplink resources. the method may also include, based on the determining, selecting () a configuration for the mrb, the configuration including configuration parameters for a user equipment (ue) to receive the mbs session. the method may further include transmitting () the selected configuration to the ue.
20250126674. Managing Radio Functions in the Inactive State_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: a central unit (cu) of a distributed base station, the distributed base station including the cu and a distributed unit (du), can implement a method for managing a radio function for communicating with a ue. the method may include determining () to transmit a cu-to-du message, related to control of data communication with the ue, to the du, and determining () whether the data communication requires the du to perform the radio function. the method further includes, based on whether the data communication requires the du to perform the radio function, determining () whether to include an indication in the cu-to-du message to enable the radio function at the du. the method also includes transmitting () the cu-to-du message to the du.
- GOOGLE LLC
- G01C21/00
- CPC G01C21/3881
- Google llc
- G01S17/89
- G01S17/86
- G01S17/931
- H04N23/60
- H04N23/90
- CPC G01S17/89
- G03B17/02
- CPC G03B17/02
- G06F1/329
- CPC G06F1/329
- G06F16/2452
- G06F16/242
- G06F40/40
- CPC G06F16/24522
- G06F16/27
- CPC G06F16/27
- G06F16/338
- CPC G06F16/338
- G06F16/334
- G06F16/907
- G06F40/103
- G06F40/169
- G06F40/30
- H04W4/02
- H04W4/029
- G06F16/532
- G06F16/538
- G06F16/54
- CPC G06F16/532
- G06N20/00
- G06F21/60
- G06F21/57
- G06F21/64
- CPC G06F21/602
- G06F30/30
- G06F9/30
- CPC G06F30/30
- G06F30/392
- G06F30/27
- G06F30/394
- CPC G06F30/392
- G06N3/0455
- G06N3/092
- CPC G06N3/0455
- G06N3/0475
- G06N3/08
- CPC G06N3/0475
- G06F40/166
- G06F40/284
- G06N3/096
- G06N7/01
- CPC G06N7/01
- G06T3/04
- G06T3/40
- G06T7/00
- CPC G06T3/04
- G06F3/01
- G06T5/70
- G06V10/25
- G06V10/46
- G06V10/70
- H04N19/136
- H04N19/162
- H04N19/167
- H04N23/61
- H04N23/63
- CPC G06T3/40
- G06T7/70
- H04N7/15
- CPC G06T7/70
- G06T13/40
- G06T5/60
- CPC G06T13/40
- G06V10/82
- G06V10/77
- G06V10/94
- CPC G06V10/82
- G06V20/20
- G06F3/0482
- G06F3/04886
- G06F16/487
- G06F16/9032
- G06Q30/02
- G06V20/68
- H04N23/667
- CPC G06V20/20
- G06V20/40
- G06F16/583
- CPC G06V20/41
- G09G3/32
- G09G3/20
- CPC G09G3/32
- G10K11/175
- H04R1/10
- CPC G10K11/175
- G10L15/22
- B60W40/09
- G06F40/279
- CPC G10L15/22
- G10L15/183
- G10L15/26
- G10L15/30
- G10L15/00
- G10L15/06
- CPC G10L15/30
- H04L51/02
- G06F3/16
- CPC H04L51/02
- H04L51/58
- G10L17/22
- H04W4/12
- CPC H04L51/58
- H04L9/40
- CPC H04L63/102
- G06V10/26
- G06V40/10
- CPC H04N7/157
- H04N21/442
- H04N21/258
- H04N21/41
- H04W12/64
- CPC H04N21/44218
- H04N25/616
- H04N25/78
- CPC H04N25/616
- H04W72/30
- H04W72/543
- CPC H04W72/30
- H04W76/27
- CPC H04W76/27
(Ad) Transform your business with AI in minutes, not months
Trusted by 1,000+ companies worldwide
