GOOGLE LLC patent applications on February 6th, 2025
Patent Applications by GOOGLE LLC on February 6th, 2025
GOOGLE LLC: 49 patent applications
GOOGLE LLC has applied for patents in the areas of G06F9/451 (4), G06N20/00 (4), G06N3/045 (3), G06N10/00 (3), G10L15/22 (3) G06N20/00 (2), G06F9/453 (2), H04L9/3271 (1), G06T19/006 (1), G09G3/3208 (1)
With keywords such as: data, user, based, input, image, device, network, request, sample, and application in patent application abstracts.
Patent Applications by GOOGLE LLC
20250044595. OPTICAL WAVEGUIDE WITH MULTIPLE OPTICAL PATHS_simplified_abstract_(google llc)
Inventor(s): Joseph Daniel Lowney of Tucson AZ (US) for google llc
IPC Code(s): G02B27/01, G02B27/00, G02B27/10
CPC Code(s): G02B27/0172
Abstract: systems and methods are provided involving a waveguide comprising an incoupler and an outcoupler. the incoupler is configured to receive display light representative of an image for display, and to direct a first portion of the display light to propagate within the waveguide along a first optical path and a second portion of the display light to propagate within the waveguide along a second optical path, such that the first optical path and the second optical path have substantially non-overlapping propagation angles. the outcoupler is configured to combine the first portion of the display light and the second portion of the display light to display a representation of the image.
Inventor(s): Sneha Ashok of San Jose CA (US) for google llc, Cliff Kuang of San Francisco CA (US) for google llc, Mugurel-Ionut Andreica of Adliswil (CH) for google llc
IPC Code(s): G06F3/0482, G06F3/0488, G06F9/451
CPC Code(s): G06F3/0482
Abstract: implementations relate to providing a macro to a user to instruct the user regarding how to perform an action. when a user submits a request to perform an action, an option is provided that indicates a macro is available. upon selecting the macro, one or more elements are displayed, with a corresponding application displayed in the background. the next element is displayed after a period of time, depending on the user familiarity with the action.
20250045032. SHARDING FOR SYNCHRONOUS PROCESSORS_simplified_abstract_(google llc)
Inventor(s): Reiner Pope of Sunnyvale CA (US) for google llc, Herman Schmit of Marina CA (US) for google llc, Michial Allen Gunter of San Francisco CA (US) for google llc
IPC Code(s): G06F8/41, G06F9/48, G06F9/50, G06F15/82, G06F17/16
CPC Code(s): G06F8/451
Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharding dataflow graphs for a device having multiple synchronous tiles. one of the methods includes receiving a representation of a dataflow graph comprising a plurality of nodes that each represent respective matrix operations to be performed by a device having a plurality synchronous tiles. candidate allocations of respective portions of the dataflow graph to each tile of the plurality of synchronous tiles are evaluated according to one or more resource constraints of the device. one of the candidate allocations is selected based on evaluating each candidate allocation.
20250045059. JUST-IN-TIME CONTAINERS_simplified_abstract_(google llc)
Inventor(s): Dominic Kramer of Mountain View CA (US) for google llc, Ryan Day of Mountain View CA (US) for google llc
IPC Code(s): G06F9/4401, G06F8/61, G06F9/445
CPC Code(s): G06F9/4401
Abstract: a method including receiving, from a developer device, a request to build an execution environment for a software application, the software application comprising a manifest of dependencies. the method also includes generating, using a bootstrap execution environment based on the manifest of dependencies, the execution environment for the software application comprising a set of application dependencies, and storing the execution environment at a data store. the method further includes executing the software application in the execution environment.
Inventor(s): Cliff Kuang of San Francisco CA (US) for google llc, Diana Avram of Zurich (CH) for google llc, Mugurel-Ionut Andreica of Adliswil (CH) for google llc, Radu Voroneanu of Zurich (CH) for google llc, Sneha Ashok of San Jose CA (US) for google llc, Deepak Goyal of Sunnyvale CA (US) for google llc, Kyunghoon Lee of Santa Clara CA (US) for google llc, Alice Liang of Sunnyvale CA (US) for google llc, Dana Ritter of Horgen (CH) for google llc, Adam Coimbra of Los Altos CA (US) for google llc, Anton Berezin of Zurich (CH) for google llc, Andre Elisseeff of Basel (CH) for google llc
IPC Code(s): G06F9/451, G06F3/0482, G06F3/0484
CPC Code(s): G06F9/451
Abstract: implementations relate to determining a rendering type for an application that is executing automatically. based on user interactions with an application that is associated with specified input from the user while the user is interacting with the application, a confidence metric is generated for each specified input and a rendering type is determined based on the confidence metrics. subsequently, when the user requests that a sequence of actions be performed, the application will be displayed according to the rendering type.
Inventor(s): Diana Avram of Zurich (CH) for google llc, Mugurel-Ionut Andreica of Adliswil (CH) for google llc, Andrea D'olimpio of Zurich (CH) for google llc, Bogdan Prisacari of Adliswil (CH) for google llc, Felix Weissenberger of Zurich (CH) for google llc, Andre Elisseeff of Basel (CH) for google llc, Cliff Kuang of San Francisco CA (US) for google llc, Dana Ritter of Horgen (CH) for google llc, Adam Coimbra of Los Altos CA (US) for google llc
IPC Code(s): G06F9/451
CPC Code(s): G06F9/453
Abstract: implementations relate to identifying actions performed by a user while the user is interacting with an application and providing a routine suggestion to the user based on the identified actions. while a user is interacting with an application, screenshots of the user actions are captured and processed to determine what actions were performed by the user. the identified actions are compared to one or more template routines and a template routine is selected that matches the actions and intent of the user and provided to the user as a suggested routine. the suggested routine can be implemented by an automated assistant to perform the actions of the template by providing a corresponding command.
Inventor(s): Cliff Kuang of San Francisco CA (US) for google llc, Adam Coimbra of Los Altos CA (US) for google llc, Bogdan Prisacari of Adliswil (CH) for google llc, Felix Weissenberger of Zurich (CH) for google llc, Eric Stavarache of Zurich (CH) for google llc, Mugurel-Ionut Andreica of Adliswil (CH) for google llc, Jonathan Splitlog of Mountain View CA (US) for google llc, Caleb Misclevitz of Portland OR (US) for google llc
IPC Code(s): G06F9/451, G06F3/0484, G06V40/20
CPC Code(s): G06F9/453
Abstract: implementations relate to determining a general routine when an automated assistant is not configured to fulfill a user request. when the user submits a request to an automated assistant to perform a routine and the automated assistant is not configured to fulfill the request, the user demonstrates the actions that are included in the routine. the automated assistant generates a routine based on the actions of the user and stores the routine with the request that was initially submitted by the user. in some implementations, a general routine can include one or more parameters and the user provides a value for the parameters with the request. general routines can additionally be generated based on previous routines performed by the user and/or other users.
Inventor(s): Hari Bhaskar Sankaranarayanan of Bengaluru (IN) for google llc
IPC Code(s): G06F11/34, H04L67/55
CPC Code(s): G06F11/3495
Abstract: a method includes obtaining a pre-trained machine learning model and a training embedding snapshot from a remote system, and obtaining one or more input data samples captured by a user device. the method includes, for each particular input data sample of the one or more input data samples, processing, using an on-device machine learning model corresponding to the pre-trained machine learning model, the particular input data sample to generate a corresponding on-device embedding and one or more corresponding predicted outputs, and generating, using the training embedding snapshot and the corresponding on-device embedding, corresponding performance data. the method includes aggregating the corresponding performance data for the one or more input data samples to determine one or more performance metrics for the on-device machine learning model, and transmitting the one or more performance metrics to the remote system.
Inventor(s): Horia Alexandru Toma of Sunnyvale CA (US) for google llc, Gurushankar Rajamani of Sunnyvale CA (US) for google llc, Sukalpa Biswas of Fremont CA (US) for google llc, Robert S. Sprinkle of Waikoloa HI (US) for google llc
IPC Code(s): G06F13/42
CPC Code(s): G06F13/4234
Abstract: the present application relates to systems and methods for providing high bandwidth connections between memory and computing units. for example, memory units can be configured to perform concurrent read and write operations in parallel to one another. the memory units can also be configured to alter the relative bandwidths that are available for the read and write operations. for example, bi-directional transmission interfaces of a memory unit can be assigned to operate as uni-directional interfaces that are a part of either a data input path or a data output path.
20250045271. VERIFIED ENTITY ATTRIBUTES_simplified_abstract_(google llc)
Inventor(s): Wei-Haw Chuang of Menlo Park CA (US) for google llc
IPC Code(s): G06F16/23, G06F16/951, G06F16/953, G06F16/958, H04L9/40
CPC Code(s): G06F16/2365
Abstract: systems and methods enable an entity to certify a web page address as being linked to the entity. the web page address includes semantic web mark-up identified attributes for the entity. a system may extract the attributes from the web page for the entity and use the attributes to generate an information card for the entity. the certification process ensures that the attributes are accurate, so that information cards generated for the entity are of high quality and reliable. implementations may also simplify maintenance and quality assurances processes for an entity repository.
20250045305. ADAPTIVE RANKING OF NAVIGATION SUGGESTIONS_simplified_abstract_(google llc)
Inventor(s): Mohamad Hasan Ahmadi of Santa Monica CA (US) for google llc, Jun Zou of Bothell WA (US) for google llc, Angela Alison Yoeurng of Pasadena CA (US) for google llc, Justin Gabriel Donnelly of Westlake Village CA (US) for google llc, Tommy Chendong Li of Santa Monica CA (US) for google llc, Tarun Bansal of Seattle WA (US) for google llc, Manuk Armen Hovanesian of Glendale CA (US) for google llc, Michael Blair Crouse of Seattle WA (US) for google llc, Sophie Chang of Seattle WA (US) for google llc, Yana Yushkina of Seattle WA (US) for google llc, Jesse Hong Lee of San Francisco CA (US) for google llc
IPC Code(s): G06F16/332, G06F16/33, G06N20/00
CPC Code(s): G06F16/3322
Abstract: a method is disclosed for providing autocomplete suggestions for a navigation text box. the method includes receiving an input in the navigation text box and obtaining candidate navigation suggestions from a retrieval source. respective signals are obtained for the candidate navigation suggestions, and respective probability scores are obtained by providing the input, candidate navigation suggestions, and signals to a navigation suggestion ranking model. the probability scores reflect a prediction of the likelihood of selection. based on their respective probability scores, at least some candidate navigation suggestions are provided as autocomplete suggestions for the input. this method enhances user experience by offering relevant and personalized navigation suggestions in real-time, improving efficiency and accuracy in navigation tasks.
Inventor(s): Jinhyuk Lee of Sunnyvale CA (US) for google llc, Zhuyun Dai of Sunnyvale CA (US) for google llc, Xiaoqi Ren of Kirkland WA (US) for google llc, Iftekhar Naim of Los Gatos CA (US) for google llc, Yi Luan of Kirkland WA (US) for google llc, Blair Yuxin Chen of San Jose CA (US) for google llc, Siddhartha Reddy Jonnalagadda of Sunnyvale CA (US) for google llc, Ming-Wei Chang of Redmond WA (US) for google llc, Daniel Matthew Cer of Santa Clara CA (US) for google llc, Gustavo Adolfo Hernandez Abrego of Mountain View CA (US) for google llc, Jeremy Robert Cole of San Francisco CA (US) for google llc, Colin Hearne Evans of San Mateo CA (US) for google llc, Yuzhe Zhao of San Francisco CA (US) for google llc, Pranay Bhatia of Palo Alto CA (US) for google llc, Rajvi Kapadia of Sunnyvale CA (US) for google llc, Riham Hassan Abdel-Moneim Mansour of Kirkland WA (US) for google llc, Raphael Dominik Hoffman of Los Altos CA (US) for google llc, Simon Kunio Tokumine of San Francisco CA (US) for google llc, Scott Bradley Huffman of Portola Valley CA (US) for google llc, Stephen Zachary Karukas of Seattle WA (US) for google llc, Michael Yiupun Kwong of San Jose CA (US) for google llc, Shu Zheng of Bellevue CA (US) for google llc, Yan Qiao of Millbrae CA (US) for google llc, Lukas Rutishauser of Kirkland CA (US) for google llc, Anand Rajan Iyer of Sunnyvale CA (US) for google llc
IPC Code(s): G06F16/33, G06N3/08
CPC Code(s): G06F16/3344
Abstract: an example method includes providing, to a sequence model (i) a plurality of few-shot prompts, wherein each prompt comprises a demonstration passage, a demonstration task, and a demonstration query, wherein the demonstration task describes a type of retrieval, and wherein the demonstration query is relevant to the demonstration task, and (ii) a plurality of passages sampled from a corpus of passages. the method also includes receiving, from the sequence model and for the plurality of passages and based on the plurality of few-shot prompts, a respective plurality of predicted task-query pairs, the sequence model having been prompted to predict a task based on an input passage, and predict an output query relevant to the predicted task. the method further includes generating a synthetic training dataset comprising the plurality of passages and the respective plurality of predicted task-query pairs. the method also includes providing the synthetic training dataset.
20250045326. HANDLING CONTRADICTORY QUERIERS ON A SHARED DEVICE_simplified_abstract_(google llc)
Inventor(s): Matthew Sharifi of Kilchberg (CH) for google llc, Victor Carbune of Zürich (CH) for google llc
IPC Code(s): G06F16/632, G06F16/638, G10L17/02, G10L17/06
CPC Code(s): G06F16/632
Abstract: a method for handling contradictory queries on a shared device includes receiving a first query issued by a first user, the first query specifying a first long-standing operation for a digital assistant to perform, and while the digital assistant is performing the first long-standing operation, receiving a second query, the second query specifying a second long-standing operation for the digital assistant to perform. the method also includes determining that the second query was issued by another user different than the first user and determining, using a query resolver, that performing the second long-standing operation would conflict with the first long-standing operation. the method further includes identifying one or more compromise operations for the digital assistant to perform, and instructing the digital assistant to perform a selected compromise operation among the identified one or more compromise operations.
Inventor(s): Diana Avram of Zurich (CH) for google llc, Mugurel-Ionut Andreica of Adliswil (CH) for google llc, Alice Liang of Sunnyvale CA (US) for google llc, Andrea D'olimpio of Zurich (CH) for google llc, Cliff Kuang of San Francisco CA (US) for google llc, Andre Elisseeff of Basel (CH) for google llc, Dana Ritter of Horgen (CH) for google llc, Florian Hasler of Winterthur (CH) for google llc, Radu Voroneanu of Zurich (CH) for google llc
IPC Code(s): G06F16/638, G06F16/635, H04L67/50
CPC Code(s): G06F16/639
Abstract: implementations relate to identifying actions performed by a user while the user is interacting with multiple applications and providing a routine suggestion to the user based on the identified actions. while a user is interacting with one or more applications, the user actions are determined. the user actions are compared to one or more template routines and a template routine is selected that matches the actions of the user and provided to the user as a suggested routine. the suggested routine can be implemented by an automated assistant to perform the actions of the template by providing a corresponding command.
20250045349. LINEAR MEMORY ATTENTION SYSTEM AND METHODS_simplified_abstract_(google llc)
Inventor(s): Markus Norman Rabe of San Francisco CA (US) for google llc, Charles Edgar Staats, III of Mountain View CA (US) for google llc
IPC Code(s): G06F17/16
CPC Code(s): G06F17/16
Abstract: a linear memory attention system and method implements an iterative process to compute attention by first computing partial first and partial second attention components for a token for each iteration. from these partial first and partial second attention components for each iteration, respective first and second attention components are then determined. on the final iteration for the token, attention for the token is computed by dividing the second attention component by the first attention component. in an implementation, a normalization scaler is used to ensure numerical stability. in an implementation, parallelism is achieved by splitting queries into chunks of constant size and processing the keys and values of the chunks. the use of checkpointing facilitates a more efficient use of memory and allows for recomputation during backpropagation.
20250045357. OPERATOR AVERAGING WITHIN QUANTUM COMPUTING SYSTEMS_simplified_abstract_(google llc)
Inventor(s): Ryan Babbush of Venice CA (US) for google llc
IPC Code(s): G06F17/18, G06N10/00
CPC Code(s): G06F17/18
Abstract: methods, systems and apparatus for estimating an expectation value of a quantum mechanical observable. in one aspect, a method includes identifying a first operator associated with the observable, wherein the first operator comprises a linear combination of terms. one or more constraints on expectation values of one or more of the terms in the linear combination are determined. a second operator is defined, wherein the second operator comprises a combination of the first operator and one or more of the determined constraints. the expectation value of the quantum mechanical observable is estimated using the second operator.
Inventor(s): Daniel V. Klein of Pittsburgh PA (US) for google llc, Ramprasad Sedouram of Bengaluru (IN) for google llc
IPC Code(s): G06F21/32, G06V40/16, G10L17/00
CPC Code(s): G06F21/32
Abstract: a method includes processing, using a speech recognizer, a first portion of audio data to generate a first lattice, and generating a first partial transcription for an utterance based on the first lattice. the method includes processing, using the recognizer, a second portion of the data to generate, based on the first lattice, a second lattice representing a plurality of partial speech recognition hypotheses for the utterance and a plurality of corresponding speech recognition scores. for each particular partial speech recognition hypothesis, the method includes generating a corresponding re-ranked score based on the corresponding speech recognition score and whether the particular partial speech recognition hypothesis shares a prefix with the first partial transcription. the method includes generating a second partial transcription for the utterance by selecting the partial speech recognition hypothesis of the second plurality of partial speech recognition hypotheses having the highest corresponding re-ranked score.
Inventor(s): Kevin Yeo of Mountain View CA (US) for google llc, Sarvar Patel of Montville NJ (US) for google llc, Giuseppe Persiano of New York NY (US) for google llc
IPC Code(s): G06F21/62, H04L9/06, H04L9/08, H04L9/32, H04L9/40
CPC Code(s): G06F21/6227
Abstract: a method for sharing read access to a document stored on memory hardware. the method includes receiving a shared read access command from a sharor sharing read access to a sharee for a document stored on memory hardware in communication with the data processing hardware, and receiving a shared read access request from the sharee. the shared read access command includes an encrypted value and a first cryptographic share value based on a write key, a read key, a document identifier, and a sharee identifier. the method also includes multiplying the first and second cryptographic share values to determine a cryptographic read access value. the cryptographic read access value authorizes read access to the sharee for the document. the method also includes storing a read access token for the sharee including the cryptographic read access value and the encrypted value in a user read set of the memory hardware.
20250045526. Methods for Emotion Classification in Text_simplified_abstract_(google llc)
Inventor(s): Dana Movshovitz-Attias of Mountain View CA (US) for google llc, John Patrick McGregor, Jr. of Mountain View CA (US) for google llc, Gaurav Nemade of Mountain View CA (US) for google llc, Sujith Ravi of Santa Clara CA (US) for google llc, Jeongwoo Ko of Mountain View CA (US) for google llc, Dora Demszky of Stanford CA (US) for google llc
IPC Code(s): G06F40/289, G06F40/30, G06N20/00
CPC Code(s): G06F40/289
Abstract: the technology relates to methods for detecting and classifying emotions in textual communication, and using this information to suggest graphical indicia such as emoji, stickers or gifs to a user. two main types of models are fully supervised models and few-shot models. in addition to fully supervised and few-shot models, other types of models focusing on the back-end (server) side or client (on-device) side may also be employed. server-side models are larger-scale models that can enable higher degrees of accuracy, such as for use cases where models can be hosted on cloud servers where computational and storage resources are relatively abundant. on-device models are smaller-scale models, which enable use on resource-constrained devices such as mobile phones, smart watches or other wearables (e.g., head mounted displays), in-home devices, embedded devices, etc.
Inventor(s): Swaroop Mishra of Mountain View CA (US) for google llc, Ragha Kotikalapudi of San Jose CA (US) for google llc, Sahitya Potluri of Sunnyvale CA (US) for google llc, Taylor Bos of Santa Clara CA (US) for google llc, YaGuang Li of Sunnyvale CA (US) for google llc, Hanzhao Lin of Cupertino CA (US) for google llc, Steven Zheng of San Bruno CA (US) for google llc, Yu Du of Sunnyvale CA (US) for google llc, Chen Zhu of Palo Alto CA (US) for google llc, Chenkai Kuang of Sunnyvale CA (US) for google llc, Xinying Song of Kirkland WA (US) for google llc, Heng-Tze Cheng of Mountain View CA (US) for google llc, Ed H. Chi of Los Altos CA (US) for google llc, Quoc Le of Sunnyvale CA (US) for google llc
IPC Code(s): G06F40/40
CPC Code(s): G06F40/40
Abstract: implementations relate to a method implemented by one or more processors, the method including: receiving natural language (nl) based input associated with a client device; generating, using a large language model (llm) and based on processing the nl based input, llm output; determining, based on the llm output, a sequence of llm responses, the sequence of llm responses including at least one intermediate llm response and a final llm response. in some implementations, the method may further include causing the final llm response to be rendered at the client device. in additional or alternative implementations, the method may further include storing, as an instance of training data for fine-tuning the llm or an additional llm, the nl based input along with the final llm response.
20250045559. NEURAL NETWORK INSTRUCTION SET ARCHITECTURE_simplified_abstract_(google llc)
Inventor(s): Ravi Narayanaswami of San Jose CA (US) for google llc, Dong Hyuk Woo of San Jose CA (US) for google llc, Olivier Temam of Antony (FR) for google llc, Harshit Khaitan of San Jose CA (US) for google llc
IPC Code(s): G06N3/04, G06F13/28, G06N3/045, G06N3/063
CPC Code(s): G06N3/04
Abstract: a computer-implemented method that includes receiving, by a processing unit, an instruction that specifies data values for performing a tensor computation. in response to receiving the instruction, the method may include, performing, by the processing unit, the tensor computation by executing a loop nest comprising a plurality of loops, wherein a structure of the loop nest is defined based on one or more of the data values of the instruction. the tensor computation can be at least a portion of a computation of a neural network layer. the data values specified by the instruction may comprise a value that specifies a type of the neural network layer, and the structure of the loop nest can be defined at least in part by the type of the neural network layer.
20250045563. NEURAL NETWORK FOR PROCESSING GRAPH DATA_simplified_abstract_(google llc)
Inventor(s): Patrick F. Riley of Los Altos CA (US) for google llc, Marc Berndl of Mountain View CA (US) for google llc
IPC Code(s): G06N3/045, G16C20/70
CPC Code(s): G06N3/045
Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving graph data representing an input graph comprising a plurality of vertices connected by edges; generating, from the graph data, vertex input data representing characteristics of each vertex in the input graph and pair input data representing characteristics of pairs of vertices in the input graph; and generating order-invariant features of the input graph using a neural network, wherein the neural network comprises: a first subnetwork configured to generate a first alternative representation of the vertex input data and a first alternative representation of the pair input data from the vertex input data and the pair input data; and a combining layer configured to receive an input alternative representation and to process the input alternative representation to generate the order-invariant features.
20250045577. STOCHASTIC OPTIMIZATION USING MACHINE LEARNING_simplified_abstract_(google llc)
Inventor(s): Bo Dai of San Jose CA (US) for google llc, Hanjun Dai of Atlanta GA (US) for google llc, Yuan Xue of Palo Alto CA (US) for google llc, Zia Syed of Los Altos CA (US) for google llc, Dale Eric Schuurmans of Edmonton (CA) for google llc
IPC Code(s): G06N3/08
CPC Code(s): G06N3/08
Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing stochastic optimization using machine learning. one of the methods includes obtaining data defining a multi-stage stochastic optimization (msso) problem instance, the data characterizing an observation distribution, an action space, and a cost function; generating a neural network input characterizing the msso problem instance from the data; providing the neural network input as input to a neural network that generates, from the network input, a neural network output characterizing parameters of a value function corresponding to the msso problem instance; processing the neural network input using the neural network to generate the neural network output; obtaining a new observation determined according to the observation distribution for the msso problem instance; determining, using the value function characterized by the network output, an optimal action to take in response to the new observation; and executing the optimal action.
20250045613. UNIVERSAL CONTROL FOR IMPLEMENTING QUANTUM GATES_simplified_abstract_(google llc)
Inventor(s): Yuezhen Niu of El Segundo CA (US) for google llc, Hartmut Neven of Malibu CA (US) for google llc, Vadim Smelyanskiy of Mountain View CA (US) for google llc, Sergio Boixo Castrillo of Rancho Palos Verdes CA (US) for google llc
IPC Code(s): G06N10/00, H03K3/38
CPC Code(s): G06N10/00
Abstract: methods, systems, and apparatus for implementing a unitary quantum gate on one or more qubits. in one aspect, a method includes the actions designing a control pulse for the unitary quantum gate, comprising: defining a universal quantum control cost function, wherein the control cost function comprises a qubit leakage penalty term representing i) coherent qubit leakage, and ii) incoherent qubit leakage across all frequency components during a time dependent hamiltonian evolution that realizes the unitary quantum gate; adjusting parameters of the time dependent hamiltonian evolution to vary a control cost according to the control cost function such that leakage errors are reduced; generating the control pulse using the adjusted parameters; and applying the control pulse to the one or more qubits to implement the unitary quantum gate.
Inventor(s): Andrew Hard of Menlo Park CA (US) for google llc, Kurt Partridge of San Francisco CA (US) for google llc, Sean Augenstein of San Mateo CA (US) for google llc, Rajiv Mathews of Sunnyvale CA (US) for google llc
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: processor(s) of a client device can receive global weights of a global ml model from a remote system, obtain a client device data set, determine a fisher information matrix for the client data set, and transmit the fisher information matrix for the client data set to the remote system. further, processor(s) of the remote system can determine a corresponding elastic weight consolidation (ewc) loss term for each of the global weights based on at least the fisher information matrix, generate a server update for the global ml model based on (i) processing server data remotely at the remote system and using the global ml model and (ii) based on the corresponding ewc loss term for each of the global weights, and update the global weights of the global ml model based on the server update.
Inventor(s): Sarath Shekkizhar of Seattle WA (US) for google llc, Mohamed Soliman Ahmed Soliman Farghl of Munich (DE) for google llc, Animesh Nandi of Cupertino CA (US) for google llc, Mohammadhossein Bateni of Gillette NJ (US) for google llc, Sasan Tavakkol of Irvine CA (US) for google llc, Neslihan Bulut of Mountain View CA (US) for google llc
IPC Code(s): G06N20/00, G06F16/901
CPC Code(s): G06N20/00
Abstract: a method and systems are disclosed for data sampling using locality sensitive hashing. training data set comprising a plurality of data points is received. each data point of the plurality of data points is assigned to a hash bucket of a set of hash buckets associated with a set of hash functions. a sample set of data points is generated by sampling data points from each bucket of the set of hash buckets. each sample data point pair comprises a pair of data points from the sample set of data points. an artificial intelligence (ai) model to output a numerical value that produces a degree of similarity between an input pair of data points is trained using the plurality of sample data point pairs. a data structure representing relationships between data points of the plurality of data points is generated using the trained ai model and the training data set.
Inventor(s): Mar Gonzalez Franco of Seattle WA (US) for google llc, Junjiao Tian of Atlanta GA (US) for google llc, Lavisha Aggarwal of Bellevue WA (US) for google llc, Andrea Colaco of Los Altos CA (US) for google llc
IPC Code(s): G06T7/12, G06T3/40, G06V10/25
CPC Code(s): G06T7/12
Abstract: implementations relate to generation of segmentation masks for images in a zero-shot, unsupervised manner. implementations also relate to generation of labels for the segmentation layers of the segmentation mask. implementations use self-attention maps from a pass of the image through a generative image model to determine the segmentation mask and may use cross-attention maps generated when a prompt describing the image is provided with the image to the generative image model. implementations aggregate maps from different resolutions to determine the mask and labels. the disclosed techniques enable accurate segmentation for any image without apriori training, facilitating applications in image processing, computer vision, extended reality applications, and robotics.
Inventor(s): Onur G. Guleryuz of San Francisco CA (US) for google llc, Ruofei Du of San Francisco CA (US) for google llc, Hugues H. Hoppe of Mercer Island WA (US) for google llc, Sean Ryan Francesco Fanello of San Francisco CA (US) for google llc, Philip Andrew Chou of Bellevue WA (US) for google llc, Danhang Tang of Los Angeles CA (US) for google llc, Philip Davidson of Arlington MA (US) for google llc
IPC Code(s): G06T9/00, G06V10/82
CPC Code(s): G06T9/00
Abstract: nonlinear peri-codec optimization for image and video coding includes obtaining a source image including pixel values expressed in a first defined image sample space, generating a neuralized image representing the source image, the neuralized image including pixel values that are expressed as neural latent space values, encoding the input image wherein the neural latent space values are used as pixel values in a second defined image sample space and the input image is in an operative image format of the encoder, such that a decoder decodes the encoded image to obtain a reconstructed image in the second defined image sample space, wherein the reconstructed image is a reconstructed neuralized image including reconstructed neural latent space values, such that a deneuralized reconstructed image corresponding to the source image is obtained by a nonlinear post-codec image processor in the first defined image sample space.
20250045974. DATA COMPRESSION USING INTEGER NEURAL NETWORKS_simplified_abstract_(google llc)
Inventor(s): Nicholas Johnston of San Jose CA (US) for google llc, Johannes Balle of San Francisco CA (US) for google llc
IPC Code(s): G06T9/00, G06F17/18, G06N3/045, G06N3/084
CPC Code(s): G06T9/002
Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reliably performing data compression and data decompression across a wide variety of hardware and software platforms by using integer neural networks. in one aspect, there is provided a method for entropy encoding data which defines a sequence comprising a plurality of components, the method comprising: for each component of the plurality of components: processing an input comprising: (i) a respective integer representation of each of one or more components of the data which precede the component in the sequence, (ii) an integer representation of one or more respective latent variables characterizing the data, or (iii) both, using an integer neural network to generate data defining a probability distribution over the predetermined set of possible code symbols for the component of the data.
Inventor(s): Shengzhi Wu of Shenzhen (CN) for google llc, Alexander James Faaborg of Mountain View CA (US) for google llc
IPC Code(s): G06T19/00, G02B27/01, H04L67/131
CPC Code(s): G06T19/006
Abstract: according to an aspect, a method for sharing a collaborative augmented reality (ar) environment including obtaining, by a sensor system of a first computing system, visual data representing a physical space of an ar environment, where the visual data is used to create a three-dimensional (3d) map of the physical space. the 3d map includes a coordinate space having at least one virtual object added by a user of the first computing system. the method includes broadcasting, by a transducer on the first computing system, an ultrasound signal, where the ultrasound signal includes an identifier associated with the 3d map. the identifier is configured to be detected by a second computing system to join the ar environment.
20250046241. Zonal Attenuation Compensation_simplified_abstract_(google llc)
Inventor(s): Hyunchul Kim of San Jose CA (US) for google llc, Chien-Hui Wen of Cupertino CA (US) for google llc, Ken Kok Foo of Sunnyvale CA (US) for google llc
IPC Code(s): G09G3/3208, G06T7/11, G06T7/136
CPC Code(s): G09G3/3208
Abstract: this document describes systems and techniques directed at zonal attenuation compensation. in aspects, a system includes a graphics processing unit configured to provide image data to a display panel. a zonal attenuation module is configured to combine a zonal attenuation mask with the image data to generate masked image data, the masked image data having a reduced brightness for portions of data corresponding to one or more regions on the display panel based on the zonal attenuation mask. an inverse zonal attenuation module is configured to apply an inverse zonal attenuation mask to the masked image data to reduce a brightness for additional portions of data corresponding to one or more additional regions on the display panel effective to offset increased brightness in the one or more additional regions on the display panel.
Inventor(s): Leonid VELIKOVICH of New York NY (US) for google llc, Ágoston WEISZ of Pfaeffikon (CH) for google llc
IPC Code(s): G10L13/08, G06F40/126
CPC Code(s): G10L13/08
Abstract: a method, device, and computer-readable storage medium for predicting pronunciation of a text sample. the method includes selecting a predicted text sample corresponding to an audio sample, receiving a correction text sample corresponding to the audio sample, updating an encoding of allowable pronunciations of the correction text sample based on the predicted text sample and the audio sample, the updated encoding of allowable pronunciations of the correction text sample including a pronunciation of the predicted text sample, and predicting a pronunciation of the correction text sample based on the updated encoding of allowable pronunciations of the correction text sample.
20250046302. ADAPTIVE INTERFACE IN A VOICE-ACTIVATED NETWORK_simplified_abstract_(google llc)
Inventor(s): Gleb Skobeltsyn of Kilchberg (CH) for google llc, Mihaly Kozsevnyikov of Zug (CH) for google llc, Vladimir Vuskovic of Zollikerberg (CH) for google llc
IPC Code(s): G10L15/18, G06F3/16, G06F40/30, G10L15/22
CPC Code(s): G10L15/1815
Abstract: the systems and methods of the present disclosure generally relate to a data processing system that can identify and surface alternative requests when presented with ambiguous, unclear, or other requests to which a data processing system may not be able to respond. the data processing system can improve the efficiency of network transmissions to reduce network bandwidth usage and processor utilization by selecting alternative requests that are responsive to the intent of the original request.
Inventor(s): Sasha Goldshtein of Tel Aviv (IL) for google llc
IPC Code(s): G10L15/22, H04L51/02
CPC Code(s): G10L15/22
Abstract: implementations are directed to generating voice-based chatbot policy override(s) and/or utilizing voice-based chatbot policy override(s) in conjunction with existing voice-based chatbot(s). the voice-based chatbot policy override(s) can correspond to, for example, machine learning (ml) model(s) that supplement functionality of the existing voice-based chatbot(s). notably, the voice-based chatbot policy override(s) are associated with rule(s) (e.g., by virtue of training the ml model(s) that correspond to the voice-based chatbot policy override(s)) for when the voice-based chatbot policy override(s) should be utilized in lieu of the existing voice-based chatbot(s) in responding to spoken utterance(s) of human user(s) engaged in corresponding conversation(s) with the voice-based chatbot policy override(s). nonetheless, from a perspective of the human user(s), it appears as if they are still engaging in the corresponding conversations with the existing voice-based chatbot(s). thus, the functionality of the existing voice-based chatbot(s) can be supplemented without having to re-train the existing voice-based chatbot(s).
Inventor(s): Scott Lee Kirkman of Menlo Park CA (US) for google llc, Nam Hoon Kim of San Jose CA (US) for google llc, Ilyas Mohammed of San Jose CA (US) for google llc
IPC Code(s): H01G2/06, H01L25/16
CPC Code(s): H01G2/065
Abstract: a microelectronic package assembly is disclosed that implements a capacitor carrier capable of providing package warpage control and that eliminates the need to only bond chip capacitors directly to a surface of the package substrate. the capacitor carrier may be composed of a material that has a high coefficient of thermal expansion and a high young's modulus. a capacitor carrier with these characteristics can eliminate the need for a traditional stiffener in the chip package.
20250046977. ATTENUATOR FOR QUBIT DRIVE SIGNALS_simplified_abstract_(google llc)
Inventor(s): Ofer Naaman of Santa Barbara CA (US) for google llc
IPC Code(s): H01P5/18, G06N10/00, H01P1/20
CPC Code(s): H01P5/18
Abstract: an apparatus includes a directional coupler and an absorptive low pass filter, in which the directional coupler has a first transmission line extending from a first port to a second port and a second transmission line extending from a third port to a fourth port, the first transmission line and the second transmission line configured such that a portion of a signal travelling from the first port onto the first transmission line is coupled to the second transmission line and towards the third port. the second port is connected to the fourth port of the directional coupler via the absorptive low pass filter. when the signal is input into the first port of the directional coupler and output through the third port of the directional coupler, the signal is substantially unattenuated if the frequency of the signal is in a passband of the absorptive low pass filter.
20250047372. ASSISTED BEAM TRACKING FOR USER EQUIPMENT_simplified_abstract_(google llc)
Inventor(s): Jibing WANG of San Jose CA (US) for google llc, Erik STAUFFER of Sunnyvale CA (US) for google llc
IPC Code(s): H04B7/185
CPC Code(s): H04B7/18541
Abstract: a non-terrestrial, wireless communications network (ntn) can assist user equipments (ues) in tracking beams generated by ntn base stations for reselection purposes. the ntn determines one or more candidate beams to which the ue can reselect (e.g., while the ue is in an inactive or idle state with respect to controlling radio resources) based on a geographical location of the ue and respective existing and/or predicted non-terrestrial locations of one or more non-terrestrial base stations of the ntn. the ntn transmits an indication of the one or more candidate beams to the ue (and optionally other beam-related information, such as radio access resources and relative priorities), and the ue can reselect a subsequent beam based on the indication received from the ntn. the ue can locally store a mapping of candidate reselection beams to geographical locations for ease and efficiency of future reselections.
20250047508. Bot Permissions_simplified_abstract_(google llc)
Inventor(s): Shelbian Fung of Mountain View CA (US) for google llc, Richard Dunn of Mountain View CA (US) for google llc, Anton Volkov of Mountain View CA (US) for google llc, Adam Rodriguez of Mountain View CA (US) for google llc
IPC Code(s): H04L9/32, H04L9/40
CPC Code(s): H04L9/3271
Abstract: permission control and management for messaging application bots is described. a method can include providing a messaging application, on a first computing device associated with a first user, to enable communication between the first user and another user, and detecting, at the messaging application, a user request. the method can also include programmatically determining that an action in response to the user request requires access to data associated with the first user, and causing a permission interface to be rendered in the messaging application, the permission interface enabling the first user to approve or prohibit access to the data associated with the first user. the method can include accessing the data associated with the first user and performing the action in response to the user request, upon receiving user input from the first user indicating approval of the access to the data associated with the first user.
20250047601. Rate Limited Scheduler For Solicited Data Transfers_simplified_abstract_(google llc)
Inventor(s): Abhishek Agarwal of Santa Clara CA (US) for google llc, Ye Tang of Palo Alto CA (US) for google llc, Sean Clark of Sunnyvale CA (US) for google llc, Sarin Thomas of Sunnyvale CA (US) for google llc, Hugh McEvoy Walsh of Los Gatos CA (US) for google llc, Xiyu Wang of Santa Clara CA (US) for google llc
IPC Code(s): H04L47/12
CPC Code(s): H04L47/12
Abstract: a flow rate control method for solicited data communications includes receiving, at a first node of a communications network, a request-to-send (rts) signal from a second node of the communications network, the rts signal indicating a size of a solicited data transmission of the second node, determining, by the first node, whether a rate-limiting counter is above zero, wherein the rate-limiting counter is programmed to increase at a programmed rate and in response to the rate-limiting counter being above zero, scheduling, by the first node, a clear-to-send (cts) signal to be sent from the first node to the second node over the communications network, and subtracting, by the first node, a value corresponding to the size of the solicited data transmission of the second node from the rate-limiting counter.
20250047731. INDEPENDENT CONTROL OF INTERACTIVE STREAMING MEDIA_simplified_abstract_(google llc)
Inventor(s): Dov Shimon Zimring of Belmont CA (US) for google llc, Ali Naddaf of Cupertino CA (US) for google llc, Michael Jon Sundermeyer of Palo Alto CA (US) for google llc, Rishi Chandra of Los Altos CA (US) for google llc, John Affaki of Los Altos CA (US) for google llc, Sarah Walter of Novato CA (US) for google llc, Rob McCool of Menlo Park CA (US) for google llc, Majd Bakar of San Jose CA (US) for google llc
IPC Code(s): H04L65/612, A63F13/23, A63F13/26, A63F13/335, A63F13/352, A63F13/355, A63F13/428, A63F13/79, H04L65/613, H04L65/75, H04L67/131
CPC Code(s): H04L65/612
Abstract: in one general aspect, a method can include receiving, at a server computing device, a launch request to launch and stream media content pertaining to a game session, the launch request being provided by a mobile device, selecting, using the launch request, a media host configured to stream the media content, verifying a user associated with the mobile device and launching the game session for streaming to the user on a device other than the mobile device. in addition, the method can include during the game session and while streaming the media content, receiving a plurality of real time control requests from the mobile device, and executing the control request by compositing, in real time, the at least one change and the media content and to produce a composite display and transmitting, to the device other than the mobile device, the encoded composite display for streaming in real time.
20250047756. USAGE-BASED NETWORK CONNECTION MANAGEMENT_simplified_abstract_(google llc)
Inventor(s): Sumukh Ashok Shevde of Encinitas CA (US) for google llc, Sudeep Chauhan of Mountain View CA (US) for google llc
IPC Code(s): H04L67/50, H04B17/318, H04W36/30
CPC Code(s): H04L67/535
Abstract: a computing device is described that includes at least one processor, a network interface, and a storage device that stores instructions executable by the at least one processor to obtain a usage profile generated by at least applying a machine learning model to historical feature usage information of the computing device collected while the computing device was wirelessly connected to a companion computing device. the instructions may further cause the one or more processors to determine a timeout value based on the usage profile. the instructions may further cause the one or more processors to initiate a connection to a network using the network interface responsive to determining that the computing device is no longer wirelessly connected to the companion computing device and after an amount of time specified by the timeout value has elapsed.
20250047806. REAL-TIME VIDEO ENHANCEMENT_simplified_abstract_(google llc)
Inventor(s): Anne Menini of Mountain View CA (US) for google llc, Jeya Maria Jose Valanarasu of Mountain View CA (US) for google llc, Rahul Garg of Sunnyvale CA (US) for google llc, Andeep Singh Toor of Fremont CA (US) for google llc, Xin Tong of Santa Clara CA (US) for google llc, Weijuan Xi of Sunnyvale CA (US) for google llc
IPC Code(s): H04N7/15, G06T5/00
CPC Code(s): H04N7/15
Abstract: methods and systems for real-time video enhancement are provided herein. a current frame of a video stream generated by a client device of a plurality of client devices participating in the video conference is identified during a video conference. an enhanced previous frame corresponding to an enhanced version of a previous frame in the video stream is identified. at least the current frame and the enhanced previous frame are provided as input to a machine-learning model. an output of the machine learning model is obtained. the output of the machine learning model indicates an enhanced current frame corresponding to an enhanced version of the current frame. the current frame is replaced with the enhanced current frame in the video stream.
20250047833. Ranked Reference Framework For Video Coding_simplified_abstract_(google llc)
Inventor(s): Sarah Parker of San Francisco CA (US) for google llc, Debargha Mukherjee of Cupertino CA (US) for google llc, Lester Lu of Los Angeles CA (US) for google llc
IPC Code(s): H04N19/105, H04N19/157, H04N19/176, H04N19/503, H04N19/70
CPC Code(s): H04N19/105
Abstract: a new reference framework is described that ranks reference frames based on a normative procedure (e.g., a calculated score) and signals the reference frames based on their ranks. the bitstream syntax is simplified by using a context tree that relies on the ranking. moreover, mapping reference frames to buffers does not have to be signaled and can be determined at the decoder. in an example, the identifier of a reference frame used to code a current block can include identifying a syntax element corresponding to the identifier, determining context information for the syntax element, determining a node of a context tree that includes the syntax element, and coding the syntax element according to a probability model using the context information associated with the node. the context tree is a binary tree that includes, as nodes, the available reference frames arranged in the ranking.
Inventor(s): Diego Jose Valenzuela Phillips of Kirkland WA (US) for google llc, Gregory Malcolm John Fitch of Sunnyvale CA (US) for google llc, Rachit Gupta of Kirkland WA (US) for google llc, Cristian Alcoholado Moenne of Kirkland WA (US) for google llc
IPC Code(s): H04N21/414
CPC Code(s): H04N21/41422
Abstract: in some examples, processors of an infotainment system receives a request to refresh a graphical user interface of the application being displayed by a display of the infotainment system. the processors determine whether the one or more processors received the request during a refresh limit period. responsive to determining that the one or more processors received the request during a refresh limit period, the processors delay processing of the request. responsive to expiration of the refresh limit period, the processors process the request, and, responsive to processing the request, initiate the refresh limit period.
Inventor(s): Matthew Sharifi of Kilchberg (CH) for google llc, Victor Carbune of Zurich (CH) for google llc
IPC Code(s): H04N21/422, G06V20/40, G10L15/22, G10L25/57, H04N21/472
CPC Code(s): H04N21/42204
Abstract: voice-based interaction with video content being presented by a media player application is enhanced through the use of an automated assistant capable of identifying when a spoken utterance by a user is a request to playback a specific scene in the video content. a query identified in a spoken utterance may be used to access stored scene metadata associated with video content being presented in the vicinity of the user to identify one or more locations in the video content that correspond to the query, such that a media control command may be issued to the media player application to cause the media player application to seek to a particular location in the video content that satisfies the query.
Inventor(s): David Deephanphongs of San Francisco CA (US) for google llc, Ken Krieger of Jackson WY (US) for google llc
IPC Code(s): H04N21/442, G06V40/10, G06V40/16, H04H60/33, H04H60/37, H04H60/45, H04H60/46, H04H60/66, H04N21/25, H04N21/258, H04N21/4223, H04N21/482, H04N21/658
CPC Code(s): H04N21/44218
Abstract: in accordance with some implementations, a method for measuring viewership of media content is disclosed. the method is performed on a client system. the client system obtains identification information for individuals who have agreed to participate in a media viewership totals measurement study, detects a first user of the client system, determines media content being presented in proximity to the first user, automatically determines if the first user is an individual who has agreed to participate in the media viewership totals measurement study; and generates viewership data according to the determination.
Inventor(s): Jin Yu Lee of Mountain View CA (US) for google llc
IPC Code(s): H04N23/68, G02B27/64, G03B5/00, H04N17/00
CPC Code(s): H04N23/687
Abstract: this disclosure describes a method to calibrate a position of an optical image stabilization (ois) lensing element based on an electric current reading of one or more areas of a mobile imaging device . the position is a deviation from a center position, where the ois lensing element is not influenced by a force. a coupling compensation coefficient is generated based on the electric current reading. a derived value for the position is adjusted based on the coupling compensation coefficient. a scaling sensitivity coefficient is generated based on the electric current reading. the derived value for the position is further adjusted based on the scaling sensitivity coefficient. the coupling compensation coefficient and the scaling sensitivity coefficient are further based on maximum and minimum values for the hall effect sensor at the temperature reading and a calibration temperature.
20250048366. MANAGING SMALL DATA COMMUNICATION_simplified_abstract_(google llc)
Inventor(s): Chih-Hsiang Wu of Taoyuan City (TW) for google llc
IPC Code(s): H04W72/21, H04W28/02, H04W88/08
CPC Code(s): H04W72/21
Abstract: a method, for transferring uplink data that includes control-plane or non-control plane information to a central unit (cu), is implemented by a distributed unit (du) of a base station. the method includes receiving, from a user equipment (ue) in an inactive state, the uplink data via a logical channel, determining, based on the logical channel, whether to transmit the uplink data via a transport network layer protocol stack or in an uplink radio resource control (rrc) message transfer message, and transmitting the uplink data to the cu in accordance with the determining.
Inventor(s): Chih-Hsiang Wu of Taoyuan City (TW) for google llc
IPC Code(s): H04W76/18, H04L5/00, H04W24/08, H04W28/06, H04W36/08, H04W76/19, H04W76/20, H04W76/27
CPC Code(s): H04W76/18
Abstract: a method, in a user device configured to communicate with a base station, for managing communication of a segmented radio resource control (rrc) message that includes n segments includes transmitting () a first m segments of the segmented rrc message to the base station, m being an integer greater than zero and less than n, detecting ( or ), before transmitting an (m+1)-th segment of the segmented rrc message, an intervening event that triggers an rrc reconfiguration procedure, and, after detecting the intervening event, transmitting () the (m+1)-th segment through an n-th segment of the segmented rrc message to the base station before the rrc reconfiguration procedure has completed.
- GOOGLE LLC
- G02B27/01
- G02B27/00
- G02B27/10
- CPC G02B27/0172
- Google llc
- G06F3/0482
- G06F3/0488
- G06F9/451
- CPC G06F3/0482
- G06F8/41
- G06F9/48
- G06F9/50
- G06F15/82
- G06F17/16
- CPC G06F8/451
- G06F9/4401
- G06F8/61
- G06F9/445
- CPC G06F9/4401
- G06F3/0484
- CPC G06F9/451
- CPC G06F9/453
- G06V40/20
- G06F11/34
- H04L67/55
- CPC G06F11/3495
- G06F13/42
- CPC G06F13/4234
- G06F16/23
- G06F16/951
- G06F16/953
- G06F16/958
- H04L9/40
- CPC G06F16/2365
- G06F16/332
- G06F16/33
- G06N20/00
- CPC G06F16/3322
- G06N3/08
- CPC G06F16/3344
- G06F16/632
- G06F16/638
- G10L17/02
- G10L17/06
- CPC G06F16/632
- G06F16/635
- H04L67/50
- CPC G06F16/639
- CPC G06F17/16
- G06F17/18
- G06N10/00
- CPC G06F17/18
- G06F21/32
- G06V40/16
- G10L17/00
- CPC G06F21/32
- G06F21/62
- H04L9/06
- H04L9/08
- H04L9/32
- CPC G06F21/6227
- G06F40/289
- G06F40/30
- CPC G06F40/289
- G06F40/40
- CPC G06F40/40
- G06N3/04
- G06F13/28
- G06N3/045
- G06N3/063
- CPC G06N3/04
- G16C20/70
- CPC G06N3/045
- CPC G06N3/08
- H03K3/38
- CPC G06N10/00
- CPC G06N20/00
- G06F16/901
- G06T7/12
- G06T3/40
- G06V10/25
- CPC G06T7/12
- G06T9/00
- G06V10/82
- CPC G06T9/00
- G06N3/084
- CPC G06T9/002
- G06T19/00
- H04L67/131
- CPC G06T19/006
- G09G3/3208
- G06T7/11
- G06T7/136
- CPC G09G3/3208
- G10L13/08
- G06F40/126
- CPC G10L13/08
- G10L15/18
- G06F3/16
- G10L15/22
- CPC G10L15/1815
- H04L51/02
- CPC G10L15/22
- H01G2/06
- H01L25/16
- CPC H01G2/065
- H01P5/18
- H01P1/20
- CPC H01P5/18
- H04B7/185
- CPC H04B7/18541
- CPC H04L9/3271
- H04L47/12
- CPC H04L47/12
- H04L65/612
- A63F13/23
- A63F13/26
- A63F13/335
- A63F13/352
- A63F13/355
- A63F13/428
- A63F13/79
- H04L65/613
- H04L65/75
- CPC H04L65/612
- H04B17/318
- H04W36/30
- CPC H04L67/535
- H04N7/15
- G06T5/00
- CPC H04N7/15
- H04N19/105
- H04N19/157
- H04N19/176
- H04N19/503
- H04N19/70
- CPC H04N19/105
- H04N21/414
- CPC H04N21/41422
- H04N21/422
- G06V20/40
- G10L25/57
- H04N21/472
- CPC H04N21/42204
- H04N21/442
- G06V40/10
- H04H60/33
- H04H60/37
- H04H60/45
- H04H60/46
- H04H60/66
- H04N21/25
- H04N21/258
- H04N21/4223
- H04N21/482
- H04N21/658
- CPC H04N21/44218
- H04N23/68
- G02B27/64
- G03B5/00
- H04N17/00
- CPC H04N23/687
- H04W72/21
- H04W28/02
- H04W88/08
- CPC H04W72/21
- H04W76/18
- H04L5/00
- H04W24/08
- H04W28/06
- H04W36/08
- H04W76/19
- H04W76/20
- H04W76/27
- CPC H04W76/18
