GOOGLE LLC patent applications on April 3rd, 2025
Patent Applications by GOOGLE LLC on April 3rd, 2025
GOOGLE LLC: 35 patent applications
GOOGLE LLC has applied for patents in the areas of G06V20/40 (4), G02B27/01 (3), G06F40/284 (3), G06N20/00 (3), G06F3/01 (2) G06N20/00 (3), G06F3/0418 (2), G02B27/0172 (1), G06N3/088 (1), H04R1/025 (1)
With keywords such as: user, based, media, data, code, device, including, keyword, configured, and machine in patent application abstracts.
Patent Applications by GOOGLE LLC
Inventor(s): Alexander Koshelev of San Jose CA US for google llc, Christophe Peroz of Zollikon CH for google llc
IPC Code(s): G02B27/01
CPC Code(s): G02B27/0172
Abstract: a lightguide for a head-wearable display or near-eye display includes an incoupler configured to direct display light representative of an image to the eye of a user. to direct the display light, the lightguide includes an incoupler that has reflectors configured to first direct the display light into the lightguide such that the display light propagates through the body lightguide and is received at a combined exit pupil expansion and outcoupling structure of the lightguide. this combined exit pupil expansion and outcoupling structure includes an array of louver retroreflectors that expands the eyebox of the image and also directs at least a portion of the display light out of the lightguide and toward the eye of the user. the louver retroreflectors in the array of louver retroreflectors each includes a first reflective surface and a second reflective surface arranged substantially orthogonal to each other.
20250110345. COMPACT XCUBE LIGHT ENGINE ASSEMBLY_simplified_abstract_(google llc)
Inventor(s): Matthew Thomas Valente of Sunnyvale CA US for google llc, Daniel J. Effinger of Hamilton CA for google llc
IPC Code(s): G02B27/14, G02B27/01
CPC Code(s): G02B27/149
Abstract: a light engine includes a housing, a lens barrel disposed at a first surface of the housing opposite a second surface of the housing, a dichroic combiner, and first, second, and third microled panel assemblies, each having a microled panel coupled to a connector interface via a flex cable. the first microled panel assembly is disposed at a third surface of the housing and arranged such that the flex cable extends first toward the barrel assembly and then turns back toward the second surface. the second microled panel assembly is disposed at a fourth surface of the housing and arranged such that the flex cable extends first toward the barrel assembly and then turns back toward the second surface. the third microled panel assembly is disposed at the second surface of the housing and arranged such that the flex cable extends toward either the third surface or the fourth surface.
20250110591. False-Input Suppression at Touch-Sensitive Displays_simplified_abstract_(google llc)
Inventor(s): Leonardo Blanger of Taipei City TW for google llc, Chiayun Kuan of San Jose CA US for google llc, Stephen Mathew Pfetsch of Sunnyvale CA US for google llc
IPC Code(s): G06F3/041, G06F3/044, G06F18/2415
CPC Code(s): G06F3/0418
Abstract: this document describes systems and techniques for false-input suppression at touch-sensitive displays. in aspects, an electronic device with a touch-sensitive display generates a touch frame having a heatmap matrix based on touch input received at the touch-sensitive display. the electronic device further obtains contextual data to determine if the contextual data satisfies contextual conditions. if the contextual conditions are satisfied, a machine-learned model analyzes the touch frame to generate a confidence score for a likelihood that one or more hotspots within the heatmap matrix are indicative of touch inputs from machine-learned entities. based on the confidence score being above a threshold, the electronic device suppresses touch inputs to prevent user interface interactions.
20250110592. Screen Protector Presence Detection_simplified_abstract_(google llc)
Inventor(s): Mark Chang of Brisbane City AU for google llc, Chiayun Kuan of San Jose CA US for google llc
IPC Code(s): G06F3/041, G06F3/044
CPC Code(s): G06F3/0418
Abstract: techniques and apparatuses are described that perform screen protector presence detection. in example aspects, an electronic device detects the presence (or absence) of a screen protector based on touch screen data provided by a touch screen during a time period that a user performs a touch-based gesture. with the touch screen data, screen protector presence detection can be performed without the need for additional sensors and without placing manufacturing requirements on the screen protector. furthermore, this technique can support the detection of a variety of different screen protectors, including screen protectors with different types of materials and thicknesses.
20250110612. WEARABLE DEVICE MIXED GAZE TRACKING_simplified_abstract_(google llc)
Inventor(s): Jochen Weber of San Francisco CA US for google llc, Tobias Toft of San Mateo CA US for google llc, Ryan Alexander West of Carleton Place CA for google llc, Cosmo Rettig of Mill Valley CA US for google llc, Prasanthi Gurumurthy of San Jose CA US for google llc, Joost Korngold of Morgan Hill CA US for google llc, Tarik Hany Abdel-Gawad of San Francisco CA US for google llc, Samuel Legge of Waterloo CA for google llc, Jingying Hu of Menlo Park CA US for google llc
IPC Code(s): G06F3/0482, G02B27/01, G06F3/01
CPC Code(s): G06F3/0482
Abstract: a method including determining an eye-gaze characteristic of a user of a wearable device, determining a head movement of the user, determining a gesture based on the eye-gaze characteristic, the head movement, and a correlation between the eye-gaze characteristic and the head movement, selecting a user interface (ui) element of a head-locked ui operating on the wearable device based on the gesture, and triggering a ui operation based on the selected ui element.
20250110650. ORPHAN BUCKET SCANNER_simplified_abstract_(google llc)
Inventor(s): Alankrit Kharbanda of Seattle WA US for google llc, Joshua Sosa of Durham NC US for google llc, Xiangqian Yu of Fremont CA US for google llc
IPC Code(s): G06F3/06
CPC Code(s): G06F3/0631
Abstract: a method for an orphan bucket scanner includes obtaining a directory including a plurality of storage buckets deployed in a container-based environment. the method includes for each respective storage bucket of the plurality of storage buckets, identifying a resource associated with the respective storage bucket of the plurality of storage buckets. the method also includes for at least one storage bucket from the plurality of storage buckets determining that the resource has been deleted from the container-based environment and adding the at least one storage bucket corresponding to the deleted resource to a subset of storage buckets. the method also includes generating an alert including the subset of storage buckets.
Inventor(s): Shu Lu of Mosman AU for google llc
IPC Code(s): G06F8/71
CPC Code(s): G06F8/71
Abstract: a request for configuring an application feature for an instance of an application provided by a platform is received from a client device of a user. an electronic document associated with the application feature is identified from a set of electronic documents. each of the set of electronic documents is associated with a respective application feature provided by the platform and includes elements that enable users to configure settings pertaining to the respective application feature. the electronic document is provided for presentation to the user. configuration data associated with settings pertaining to the application feature is obtained based on user interaction with elements of the electronic document. the application feature is configured for the application instance based on the electronic document and the configuration data. the user is provided with access to the configured application feature in accordance with the request.
Inventor(s): Mateusz Lewko of Adliswil CH for google llc, Marko Ivankovic of Obfelden CH for google llc, Luka Rimanic of Zurich CH for google llc
IPC Code(s): G06F8/72, G06F8/51
CPC Code(s): G06F8/72
Abstract: a computer-implemented method includes receiving an original code snapshot corresponding to original code from a first file of a plurality of files. the method also includes receiving a modified code snapshot corresponding to modified code that includes a code modification modifying the original code. the method also includes generating, using a large language model (llm), refactoring code based on the original code snapshot and the modified code snapshot. the refactoring code is configured to apply the code modification to code from other files of the plurality of files associated with the original code. the method also includes identifying target code from a second file of the plurality of files where the target code is associated with the original code. the method also includes applying the code modification to the identified target code using the refactoring code.
Inventor(s): Tyler Christian Gore of Cupertino CA US for google llc, Anuradha Sampath Kumar of San Diego CA US for google llc, Steven B. Diamond of San Francisco CA US for google llc, Timothy Warren Kryger of San Francisco CA US for google llc
IPC Code(s): G06F9/50
CPC Code(s): G06F9/5027
Abstract: while operating in a low-power operating domain that utilizes a low-power processor device, a mobile computing device causes display of a low-power operating domain interface. the mobile computing device makes a determination that a user intends a particular type of interaction that requires the mobile computing device to operate in a high-performance operating domain that utilizes a high-performance processor device. the mobile computing device performs initial process(es) of a plurality of domain-switching processes that are performed to switch the mobile computing device from the low-power operating domain to the high-performance operating domain. responsive to the determination, the mobile computing device causes display of a high-performance operating domain interface.
20250110850. Evaluating Computer-Readable Code_simplified_abstract_(google llc)
Inventor(s): Indu Ramamurthi of Los Altos CA US for google llc, Ryan Kam Wang Tai of Sunnyvale CA US for google llc, Karen Chia Lin Yao of Los Altos CA US for google llc
IPC Code(s): G06F11/36
CPC Code(s): G06F11/3608
Abstract: techniques are described for evaluating computer-readable code. in example aspects, a machine-learned model is trained to evaluate computer-readable code and/or its corresponding code description. as part of the evaluation, the machine-learned model can determine a level of agreement between the code description and the computer-readable code. additionally or alternatively, the machine-learned model can determine that a prohibited feature is absent from (or present in) the computer-readable code. if present, the prohibited feature can compromise a security of a device that executes the computer-readable code, a safety of a user operating the device, and/or the user's privacy. additionally or alternatively, the prohibited feature can violate a policy of a manufacturer of the device. with this machine-learned model, the manufacturer can efficiently evaluate computer-readable code and code descriptions that are provided by a third-party developer and can determine whether to make the third-party software available to users via a digital distribution platform.
Inventor(s): Xin Yang Yak of Brooklyn NY US for google llc, Sercan Omer Arik of San Francisco CA US for google llc, Yihe Dong of Princeton NJ US for google llc, Javier Gonzalvo Fructuoso of New York NY US for google llc
IPC Code(s): G06F16/22, G06N3/08
CPC Code(s): G06F16/2282
Abstract: methods, systems, and apparatuses, including computer programs encoded on computer storage media, for implementing a neural network that can perform one or more machine learning tasks on an input that includes data that represents a given data structure. in particular, implementing a language model to encode the data and a foundation neural network with an attention-based architecture to generate the task output. because of how language model generated embeddings are defined and cached, the described techniques demonstrate significant improvements in required computational resources for training and inference while also exceeding prediction performance on a variety of prediction tasks over conventional approaches.
20250110962. Automated Keyword Generation Based on Similarity Score_simplified_abstract_(google llc)
Inventor(s): Abhinav Khandelwal of Bengaluru IN for google llc, Aravindan Raghuveer of Bangalore IN for google llc, Snehal Sunilkumar Motarwar of Bengaluru IN for google llc, Rishi Saket of Bangalore IN for google llc
IPC Code(s): G06F16/2457, G06F16/9538
CPC Code(s): G06F16/24578
Abstract: methods, computing systems, and technology for generating keywords using machine-learned techniques. the system can receive, from a user device, a first keyword associated with a content item of a first content provider. additionally, the system can access from a keyword database, a plurality of keywords. moreover, the system can select, using the machine-learned model, a subset of keywords from the plurality of keywords based on the content item. furthermore, the system can process, using a machine-learned model, the first keyword and a subset of keywords to calculate a similarity score for each keyword in the subset of keywords and the first keyword. the system can determine a suggested keyword from the subset of keywords based on the similarity score for each keyword in the subset of keywords and the first keyword. subsequently, the system can cause, on a display of the user device, a presentation of the suggested keyword.
20250110974. SESSION-BASED USER AWARENESS IN LARGE LANGUAGE MODELS_simplified_abstract_(google llc)
Inventor(s): Ramprasad Sedouram of Bangalore IN for google llc, Dharma Teja of Warsaw PL for google llc
IPC Code(s): G06F16/332, G06F3/01, G06F3/04842, G06F40/242
CPC Code(s): G06F16/3329
Abstract: implementations are described herein for using the information about user engagement with large language model (llm) output as a subsequent input into an llm, so that the llm is able to provide, for rendition on one or more output devices, a subsequent output that is tailored to the user. in various implementations, based on one or more input device signals, a user engagement event with an element of a first llm output generated using a llm and rendered using one or more output devices may be detected. additional information about the element of the first llm output may be captured and used to generate at least part of a subsequent input prompt for the llm. the subsequent input prompt may be processed using the llm to generate a subsequent llm output for rendition on one or more of the output devices.
Inventor(s): Abhinav Khandelwal of Bengaluru IN for google llc, Manoj Kumar Sure of Bangalore IN for google llc, Aravindan Raghuveer of Bangalore IN for google llc, Saachi Grover of Bangalore IN for google llc, Snehal Sunilkumar Motarwar of Bengaluru IN for google llc, Rishi Saket of Bangalore IN for google llc
IPC Code(s): G06F16/33, G06F16/35, G06Q30/0241
CPC Code(s): G06F16/3334
Abstract: methods, computing systems, and technology for using machine-learned techniques for determining a keyword for a web resource, and automating content presentation for the web resource. the system can receive, from a user device of a first content provider, a request associated with a web resource having a plurality of assets. additionally, the system can determine, based on the plurality of assets, a first keyword associated with the web resource. moreover, the system can determine, based on a first keyword cluster associated with the first keyword, the first keyword being associated with a first query cluster having a query performance metric. furthermore, the system can process, using a machine-learned forecasting model, the first keyword and the first query cluster to generate a keyword performance metric for the first keyword. subsequently, the system can perform an action based on the keyword performance metric associated with the first keyword.
20250111157. ANALYZING EMBEDDING SPACES USING LARGE LANGUAGE MODELS_simplified_abstract_(google llc)
Inventor(s): Guy Tennenholtz of Menlo Park CA US for google llc, Yinlam Chow of San Carlos CA US for google llc, Chih-wei Hsu of Cupertino CA US for google llc, Jihwan Jeong of Toronto CA for google llc, Lior Shani of Haifa IL for google llc, Deepak Ramachandran of Sunnyvale CA US for google llc, Martin Mirolyubov Mladenov of Carapicuíba BR for google llc, Craig Edgar Boutilier of Palo Alto CA US for google llc
IPC Code(s): G06F40/284, G06F40/40, G06N3/0455
CPC Code(s): G06F40/284
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for analyzing embedding spaces using large language models. in one aspect, a method performed by one or more computers for analyzing a target embedding space using a neural network configured to perform a set of machine learning tasks is described. the method includes: obtaining, for each of one or more entities, a respective domain embedding representing the entity in the target embedding space; receiving a text prompt including a sequence of input tokens describing a particular machine learning task in the set to be performed on the one or more entities; preparing, for the neural network, an input sequence including each input token in the text prompt and each domain embedding; and processing the input sequence, using the neural network, to generate a sequence of output tokens describing a result of the particular machine learning task.
20250111161. GUIDED TEXT GENERATION FOR TASK-ORIENTED DIALOGUE_simplified_abstract_(google llc)
Inventor(s): Abhinav Rastogi of Sunnyvale CA US for google llc, Mihir Sanjay Kale of Mountain View CA US for google llc
IPC Code(s): G06F40/30, G06F3/16
CPC Code(s): G06F40/30
Abstract: systems and methods for guided text generation in task-based dialogue. in some aspects of the technology, an automated assistant system is configured to receive a user request, call multiple apis, generate dialogue acts based on data received from each api, replace any slot names in the dialogue acts with natural language descriptions of the slots, concatenate the modified dialogue acts, and pass the concatenated result to an nlg model for generation of a natural language response. in some aspects of the technology, the automated assistant may be configured to generate simple templated responses based on the data received from each api, concatenate the simple templated responses, and pass the concatenated sequence to an nlg model trained as a sequence-to-sequence transformer for generation of a final natural language response.
Inventor(s): Yaqing Wang of Jersey City NJ US for google llc, Jialin Wu of Culver City CA US for google llc
IPC Code(s): G06N3/045, G06F40/284, G06V10/26, G06V10/32, G06V10/82
CPC Code(s): G06N3/045
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task. one of the methods includes obtaining data specifying a pre-trained neural network; obtaining a plurality of training examples for one or more new machine learning tasks; and generating a new neural network for the one or more new machine learning tasks.
Inventor(s): Chong You of Jersey City NJ US for google llc, Guru Guruganesh of Mountain View CA US for google llc, Joshua Timothy Ainslie of Saratoga CA US for google llc, Manzil Zaheer of Mountain View CA US for google llc, Sanjiv Kumar of Jericho NY US for google llc, Santiago Ontañón of Mountain View CA US for google llc, Shanda Li of Pittsburgh PA US for google llc, Venkata Sesha Pavana Srinadh Bhojanapalli of New York NY US for google llc, Sumit Sanghai of Sunnyvale CA US for google llc
IPC Code(s): G06N3/0475
CPC Code(s): G06N3/0475
Abstract: systems and methods for processing inputs using attention neural networks. in particular, one or more of the attention layers within the attention neural network compute relative position biases using functional interpolation.
20250111235. RELATIVE MARGIN FOR CONTRASTIVE LEARNING_simplified_abstract_(google llc)
Inventor(s): Siyuan Qiao of Santa Clara CA US for google llc, Chenxi Liu of Santa Clara CA US for google llc, Jiahui Yu of Bellevue WA US for google llc, Yonghui Wu of Fremont CA US for google llc
IPC Code(s): G06N3/088
CPC Code(s): G06N3/088
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks through contrastive learning. in particular, the contrastive learning is modified to use a relative margin to adjust a training pair's contribution to optimization.
Inventor(s): Matthew James McEwen of Santa Barbara CA US for google llc
IPC Code(s): G06N10/40
CPC Code(s): G06N10/40
Abstract: the technology provides fluorescent readout of microwave-type qubits using a paracoupler architecture. a quantum computing system comprises a set of qubits configured to be responsive to one or more microwave signals and a control apparatus configured to apply the one or more microwave signals to the set of qubits. the control apparatus includes a set of control lines configured to transmit the one or more microwave signals to corresponding ones of the set of qubits. the system also includes a readout apparatus configured to perform qubit measurements. the readout apparatus including a readout line operatively coupled to the qubits. a paracoupler operatively arranged between the set of qubits and the readout apparatus is configured to enable parametric fluorescent readout of the qubits via the readout apparatus. when the paracoupler is not driven it prevents coupling of the qubits with the readout line.
Inventor(s): Wei Huang of Mountain View CA US for google llc, Zhenyu Liu of Mountain View CA US for google llc, Liang Wang of Mountain View CA US for google llc, Kumar Rishabh of 1600 Amphitheatre Parkway CA 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 distributing digital contents to client devices are described. the system obtains, for each user in a set of users, user attribute data and, for a subset of the users, consent data for controlling usage of the user attribute data. the system partitions, based at least on the consent data for the subset of users, the set of users into a first group of users and a second group of users. the system generates a respective training dataset based on the data for each group of user, and uses the datasets to train a machine learning model configured to predict information about one or more users. in particular, the system applies differential privacy to the second training dataset without applying differential privacy to the first training dataset during training.
20250111280. REFINING OUTPUTS OF GENERATIVE MODELS_simplified_abstract_(google llc)
Inventor(s): Xiaohang Li of Cupertino CA US for google llc, Feng Yang of Sunnyvale CA US for google llc, Fong Shen of Los Altos CA US for google llc
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: one example method includes receiving, by an artificial intelligence (ai) system, a query; generating, by the ai system and based on the query, a plurality of candidate digital components using a machine learning model; obtaining, by the ai system, user feedback associated with the plurality of candidate digital components, each user feedback indicating a user preference level of a corresponding candidate digital component; obtaining, by the ai system, performance data indicating an acceptance level of each candidate digital component of the plurality of candidate digital components; identifying, by the ai system and based on the user feedback and the performance data, a candidate digital component of the plurality of candidate digital components; generating, by the ai system and based on the candidate digital component, training data; and refining, by the ai system, the machine learning model using the training data.
Inventor(s): Yan Liu of Santa Monica CA US for google llc, Chuizheng Meng of Los Angeles CA US for google llc, Yihe Dong of New York NY US for google llc, Sercan Omer Arik of San Francisco CA US for google llc, Tomas Pfister of Redwood City CA US for google llc
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: a machine-learned model includes an encoder having a feature block configured to embed input data into a plurality of features in an embedding space. the input data includes multiple components such as covariate, treatment, and output components. the encoder includes one or more encoding layers, each including a temporal attention block and a feature-wise attention block. the temporal attention block is configured to obtain the embedded input data and apply temporal causal attention along a time dimension in parallel for each feature of the plurality of features to generate temporal embeddings. the feature-wise attention block is configured to obtain the temporal embeddings and generate component representations such as a covariate representation, a treatment representation, and an output representation.
Inventor(s): Sergio Orts Escolano of Zurich CH for google llc, Zhiwen Fan of Austin TX US for google llc, Di Qiu of Kitchener CA for google llc, Yinda Zhang of Palo Alto CA US for google llc, Daoye Wang of Zurich CH for google llc, Erroll Wood of Cambridge GB for google llc, Abhimitra Meka of San Francisco CA US for google llc, Hossam Isack of Oakland CA US for google llc, Paulo Fabiano Urnau Gotardo of Zurich CH for google llc, Kripasindhu Sarkar of Zurich CH for google llc, Thabo Beeler of Egg CH for google llc, Zhengyang Shen of Sunnyvale CA US for google llc, Alexander Sahba Koumis of San Francisco CA US for google llc
IPC Code(s): G06T5/50, G06T7/194, H04N5/272
CPC Code(s): G06T5/50
Abstract: a method including capturing a first plurality of images that include a foreground object and a background, capturing a second plurality of images that include the background, generating an alpha matte based on the first plurality of images and the second plurality of images using a trained machine learned model trained using a loss function configured to cause the trained machine learned model to learn high-frequency details of the foreground object, generating a foreground object image based on the first plurality of images and the second plurality of images using the trained machine learned model, and synthesizing an image including the foreground object image and a second background scene using the alpha matte.
Inventor(s): Jay TENENBAUM of Mountain View CA US for google llc, Assaf ZOMET of Mountain View CA US for google llc, Barel LEVY of Mountain View CA US for google llc, Yaron BRODSKY of Mountain View CA US for google llc, Shiran ZADA of Mountain View CA US for google llc, Yael Pritch KNAAN of Mountain View CA US for google llc, Ariel EPHRAT of Mountain View CA US for google llc, Inbar MOSSERI of Mountain View CA US for google llc, Avram GOLBERT of Mountain View CA US for google llc
IPC Code(s): G06T11/60, G06T5/77
CPC Code(s): G06T11/60
Abstract: a media application receives a set of images that include a source image and a target image, the source image and the target image including at least a subject. the media application determines whether to use one or more editors selected from a group of a head editor, a face editor, or combinations thereof. responsive to determining to use the head editor, the media application generates a compositive image by replacing at least a portion of head pixels associated with a target head of the subject in the target image with head pixels from a source head of the subject in the source image and replacing neck pixels associated with a target neck and shoulder pixels associated with target shoulders that include an area between the target head and a target torso with an interpolated region that is generated from an interpolation of the source image and the target image.
20250111666. VISUALIZING MEDIA TRENDS AT A CONTENT SHARING PLATFORM_simplified_abstract_(google llc)
Inventor(s): Mingyan Gao of Los Altos CA US for google llc, Hui Miao of Palo Alto CA US for google llc, Ye Jin of Palo Alto CA US for google llc, Sourabh Bansod of Mountain View CA US for google llc, Wenqi He of San Mateo CA US for google llc, Bibang Liu of Sunnyvale CA US for google llc, Jeffrey Daniel Forrester of Alameda CA US for google llc, Chao Tao of Santa Clara CA US for google llc, Eman Aboulatta of Santa Clara CA US for google llc, Jonathan Samuel Leavitt of Berkeley CA US for google llc
IPC Code(s): G06V10/94, G06F3/0484, G06V20/40
CPC Code(s): G06V10/945
Abstract: methods and systems for visualizing media trends at a content sharing platform are provided herein. a request of a client device is received for media items of a platform. a graphical user interface (gui) including audiovisual content of one or more media items and an indication that the media items are associated with a media trend of the platform is provided for presentation on the client device. the media trend is associated with a set of media items, where content of the set of media items share common audiovisual characteristics in accordance with a concept of the media trend a user interaction with a gui element associated with the media items is detected. responsive to the detection, an additional gui is provided for presentation on to the client device, the additional gui including audiovisual content of a set of media items associated with the media trend.
Inventor(s): Tao Zhu of Los Altos CA US for google llc, Jiahui Yu of Bellevue WA US for google llc, Jingchen Feng of Los Altos CA US for google llc, Kai Chen of Brisbane CA US for google llc, Pooya Abolghasemi of Redwood Cty CA US for google llc, Gagan Bansal of Sunnyvale CA US for google llc, Jieren Xu of San Jose CA US for google llc, Hui Miao of Palo Alto CA US for google llc, Yaping Zhang of Mountain View CA US for google llc, Shuchao Bi of Mountain View CA US for google llc, Yonghui Wu of Palo Alto CA US for google llc, Claire Cui of Mountain View CA US for google llc, Rohan Anil of Lafayette CA US for google llc
IPC Code(s): G06V20/40, G06F40/284, G10L25/57
CPC Code(s): G06V20/41
Abstract: methods and systems for media item characterization based on multimodal embeddings are provided herein. a media item including a sequence of video frames is identified. a set of video embeddings representing visual features of the sequence of video frames is obtained. a set of audio embeddings representing audio features of the sequence of video frames is obtained. a set of audiovisual embeddings is generated based on the set of video embeddings and the set of audio embeddings. each of the set of audiovisual embeddings represents a visual feature and an audio feature of a respective video frame of the sequence of video frames. one or more media characteristics associated with the media item are determined based on the set of audiovisual embeddings.
20250111674. Summarizing Events Over a Time Period_simplified_abstract_(google llc)
Inventor(s): Yuan Li of Los Altos CA US for google llc, Indu Ramamurthi of Los Altos CA US for google llc, Ryan Kam Wang Tai of Sunnyvale CA US for google llc
IPC Code(s): G06V20/40, G06F40/40, G11B27/031
CPC Code(s): G06V20/47
Abstract: this document describes systems and techniques for implementing event summarization over a period of time. a request is received to create an event summarization that includes details associated with the event summarization. the systems and techniques identify at least one image relevant to the event summarization based on the details associated with the event summarization. at least one of the identified images is selected that is relevant to the event summarization. the selected images are arranged based on how they will be included in the event summarization. a video summary is created that represents the event summarization and includes the arrangement of the selected images.
Inventor(s): Hui Miao of Palo Alto CA US for google llc, Chun-Te Chu of Bellevue WA US for google llc, Mingyan Gao of Los Altos CA US for google llc, Huanfen Yao of Dublin CA US for google llc, Ting Liu of Los Angeles CA US for google llc, Long Zhao of Los Angeles CA US for google llc, Liangzhe Yuan of Los Angeles CA US for google llc, Yukun Zhu of Shoreline WA US for google llc, Vinay Kumar Bettadapura of San Jose CA US for google llc, Ye Jin of Mountain View CA US for google llc
IPC Code(s): G06V20/40, G06V10/74, G06V10/75, G06V10/762, G06V10/80
CPC Code(s): G06V20/48
Abstract: methods and systems for media trend detection and maintenance are provided herein. a set of media items each having common media characteristics is identified. a set of pose values is determined for each respective media item of the set of media items. each pose value is associated with a particular predefined pose for objects depicted by the set of media items. a set of distance scores is calculated. each distance score represents a distance between the respective set of pose values determined for a media item and a respective set of pose values determined for an additional media item. a coherence score is determined for the set of media items based on the calculated set of distance scores. responsive to a determination that the coherence score satisfies one or more coherence criteria, a determination is made that the set of media items corresponds to a media trend of a platform.
20250112455. Distributed Main-Tie-Tie-Main Transfer Control Scheme_simplified_abstract_(google llc)
Inventor(s): Hammad Ahmad Khan of Leesburg VA US for google llc, Kei Hao of Anaheim CA US for google llc, Krishnanjan Gubba Ravikumar of Ashburn VA US for google llc
IPC Code(s): H02H7/22, H02H1/00
CPC Code(s): H02H7/22
Abstract: the technology is directed to a main-tie-tie-main (mttm) power system. the mttm system may include a first main protection relay controlling a first utility breaker and a second main protection relay controlling a second utility breaker. the mttm system may further include a first tie protection relay controlling a first tie breaker and a second tie protection relay controlling a second tie breaker. the mttm system may also include a common bus communicatively connecting the first main protection relay, second main protection relay, first tie protection relay, and second tie protection relay together. each of the first main protection relay, second main protection relay, first tie protection relay, and second tie protection relay is connected to the common bus via one or more communication lines.
Inventor(s): Kei Hao of Anaheim CA US for google llc, Hammad Ahmad Khan of Leesburg VA US for google llc, Krishnanjan Gubba Ravikumar of Ashburn VA US for google llc
IPC Code(s): H02J3/38, H02H3/08, H02J9/06
CPC Code(s): H02J3/38
Abstract: the disclosed technology is directed to a power distribution architecture or network, and a protection and control scheme for the power distribution network. in one aspect of the disclosed technology, the backup generator bus is used as a tie between two independent power sources or mains used to supply electrical power to loads on the power distribution network. another aspect of the disclosed technology is a protection scheme to detect and locate faults that may occur on the power distribution network. another aspect of the disclosed technology is a control method or process for transferring loads from one source or main to another source or main in the event of planned or emergency transfers.
Inventor(s): Christopher Schneider of Edinburgh GB for google llc, Bessie S Jiang of London GB for google llc, Martin Sablotny of Everett WA US for google llc, J. Nicolas Watson of Reston VA US for google llc
IPC Code(s): H04L47/80, H04L9/40
CPC Code(s): H04L47/801
Abstract: baseline data characterizing actions of one or more computing resources associated with one or more entities is received. one or more functional roles performed by the one or more computing resources are identified using a machine learning model, wherein the baseline data is provided as input to the machine learning model. a security-related control to be applied to the one or more computing resources is identified based on the one or more functional roles. the security-related control is applied to the one or more computing resources.
Inventor(s): Adam Amir Mostafavi of Foster City CA US for google llc, Adnan Begovic of Seattle WA US for google llc, Alexander Crettenand of San Martin CA US for google llc, Mohammad Aleagha of Redwood City CA US for google llc, Heidi Muth of Oakland CA US for google llc, Daniel Fredrick Zucker of Palo Alto CA US for google llc, Nikita Slushkin of Ladera Ranch CA US for google llc, Nicholas Michael Ritchie of Ben Lomond CA US for google llc, Cale Williams Hopkins of Cupertino CA US for google llc, Howard Zeng of Unionville CA for google llc, Jeremy Newton-Smith of Mountain View CA US for google llc, Jonathan Chen of San Mateo CA US for google llc, Teddy Ku of Mountain View CA US for google llc
IPC Code(s): H04N21/2187, H04N21/431
CPC Code(s): H04N21/2187
Abstract: the present document describes devices and methods for rendering multiple live-streams on a user interface (ui) with minimal resources. the ui is activated, having a first set of remote sensors loaded for rendering. the first set of remote sensors receive a first activation signal and begin streaming first data, which the ui renders. respondent to an action changing the set of remote sensors to be rendered on the ui, a second set of remote sensors are loaded for rendering. the second set of remote sensors receive a second activation signal and begin streaming second data, which the ui renders while the first set of remote sensors continue streaming the first data. the first data is no longer streamed after a threshold time is reached.
20250113130. SMART GLASSES MONOCOQUE TEMPLE ELECTRONICS ASSEMBLY_simplified_abstract_(google llc)
Inventor(s): Eric Anthony Bokides of Boise ID US for google llc, Joshua Moore of Elora CA for google llc, Adam Umar Abdul Kareem of Bolingbrook IL US for google llc, Emeka Godswill Ugwu of Oakland CA US for google llc, Daniel Corbalan of Forest Hills NY US for google llc, Jiwon Yang of Toronto CA for google llc, Sheng-Kai Chang of Taipei City TW for google llc, Che-Wei Liu of New Taipei City TW for google llc, Coulter Eastwood of Kitchener CA for google llc
IPC Code(s): H04R1/02
CPC Code(s): H04R1/025
Abstract: a method for making smart glasses includes obtaining a monocoque temple pre-form made as a one-piece seamless shell structure with shell walls enclosing a hollow compartment. the hollow compartment has an open end and a bottom opposite the open end. the method further includes inserting a first pre-assembled smart glasses components module into the hollow compartment through the open end of the hollow compartment and disposing the first pre-assembled smart glasses components module at a bottom of the hollow compartment. the method further includes inserting a second pre-assembled smart glasses components module through the open end of the hollow compartment, electrically connecting the second pre-assembled smart glasses components module to the first pre-assembled smart glasses components module, and attaching the monocoque temple pre-form to a frame of the smart glasses.
20250113176. MULTIPLE PROFILE DOWNLOADS FOR USER EQUIPMENT_simplified_abstract_(google llc)
Inventor(s): Edison Chen of Taipei TW for google llc
IPC Code(s): H04W8/20
CPC Code(s): H04W8/20
Abstract: a user equipment (ue) concurrently downloads available subscriber identity module (sim) profiles. the ue identifies a condition wherein one or more sim profiles is available for download. the ue first identifies the number of available sim profiles and, in response to identifying multiple available sim profiles, concurrently downloads at least two of the sim profiles to the ue. this enables the ue to more quickly use the available sim profiles to connect to corresponding networks, thereby improving the user experience with the ue.
- GOOGLE LLC
- G02B27/01
- CPC G02B27/0172
- Google llc
- G02B27/14
- CPC G02B27/149
- G06F3/041
- G06F3/044
- G06F18/2415
- CPC G06F3/0418
- G06F3/0482
- G06F3/01
- CPC G06F3/0482
- G06F3/06
- CPC G06F3/0631
- G06F8/71
- CPC G06F8/71
- G06F8/72
- G06F8/51
- CPC G06F8/72
- G06F9/50
- CPC G06F9/5027
- G06F11/36
- CPC G06F11/3608
- G06F16/22
- G06N3/08
- CPC G06F16/2282
- G06F16/2457
- G06F16/9538
- CPC G06F16/24578
- G06F16/332
- G06F3/04842
- G06F40/242
- CPC G06F16/3329
- G06F16/33
- G06F16/35
- G06Q30/0241
- CPC G06F16/3334
- G06F40/284
- G06F40/40
- G06N3/0455
- CPC G06F40/284
- G06F40/30
- G06F3/16
- CPC G06F40/30
- G06N3/045
- G06V10/26
- G06V10/32
- G06V10/82
- CPC G06N3/045
- G06N3/0475
- CPC G06N3/0475
- G06N3/088
- CPC G06N3/088
- G06N10/40
- CPC G06N10/40
- G06N20/00
- CPC G06N20/00
- G06T5/50
- G06T7/194
- H04N5/272
- CPC G06T5/50
- G06T11/60
- G06T5/77
- CPC G06T11/60
- G06V10/94
- G06F3/0484
- G06V20/40
- CPC G06V10/945
- G10L25/57
- CPC G06V20/41
- G11B27/031
- CPC G06V20/47
- G06V10/74
- G06V10/75
- G06V10/762
- G06V10/80
- CPC G06V20/48
- H02H7/22
- H02H1/00
- CPC H02H7/22
- H02J3/38
- H02H3/08
- H02J9/06
- CPC H02J3/38
- H04L47/80
- H04L9/40
- CPC H04L47/801
- H04N21/2187
- H04N21/431
- CPC H04N21/2187
- H04R1/02
- CPC H04R1/025
- H04W8/20
- CPC H04W8/20
