GOOGLE LLC patent applications on September 5th, 2024
Patent Applications by GOOGLE LLC on September 5th, 2024
GOOGLE LLC: 28 patent applications
GOOGLE LLC has applied for patents in the areas of G10L15/22 (5), G10L15/06 (3), G02B27/01 (2), G06F40/30 (2), G10L15/30 (2) G10L15/063 (2), G02B27/0172 (1), G06T11/00 (1), H04L67/306 (1), H04L45/507 (1)
With keywords such as: data, device, user, based, audio, systems, methods, robotic, client, and content in patent application abstracts.
Patent Applications by GOOGLE LLC
Inventor(s): Timothy Paul Bodiya of Shanghai (CN) for google llc, Ozan Cakmakci of Sunnyvale CA (US) for google llc
IPC Code(s): G02B27/01, G02B3/14
CPC Code(s): G02B27/0172
Abstract: systems, devices, and methods are described in which one or more tunable lens elements are incorporated within a lens structure communicatively coupled to a wearable display device operable to present augmented reality (ar) content to a user. the lens structure includes a display optics lens layer having a provided ar display, one or more eye-side lens layers disposed adjacent to the display optics lens layer and facing an eye of the user, and one or more world-side lens layers disposed adjacent to the display optics lens layer and facing away from the eye of the user. the world-side lens layers includes a tunable lens component to selectively adjust a focal modulation of at least a portion of a real-world view of the user via the lens structure.
20240295742. MEMS DEVICE WITH ELLIPTICAL MIRROR_simplified_abstract_(google llc)
Inventor(s): Daniel Adema of Waterloo (CA) for google llc, Sangtak Park of Waterloo (CA) for google llc
IPC Code(s): G02B27/09, G02B26/08, G02B26/10, G02B27/01
CPC Code(s): G02B27/0916
Abstract: optical systems may include mems mirrors having elliptical mirror plates. a laser scanning system may include a mems mirror that scans an incident light beam along a single scanning axis. the mems mirror may include an elliptical mirror plate having a semi-major axis that is aligned parallel or perpendicular to the rotational axis of the elliptical mirror plate. the incident light beam may have an elliptical cross-section, such that the incident light beam completely or substantially overlaps the reflecting surface of the elliptical mirror plate. after being reflected by the elliptical mirror plate, the light beam may be circularized via one or more shaping lenses disposed in the optical path of the reflected light beam, prior to projection of the light beam.
20240295880. Robot Collaboration via Cloud Server_simplified_abstract_(google llc)
Inventor(s): Rainer Hessmer of Los Gatos CA (US) for google llc, Nikhil J. Joshi of Fremont CA (US) for google llc, Daniel Lam of Mountain View CA (US) for google llc, Pavel Vodenski of San Carlos CA (US) for google llc
IPC Code(s): G05D1/02
CPC Code(s): G05D1/0295
Abstract: a method includes receiving, by a robotic device, an indication that the robotic device was elected to be a leader robotic device by a plurality of robotic devices and receiving an indication of a new task in a remotely stored list of tasks. the method further includes determining, based on a remotely stored list of robotic devices, an additional robotic device to assign the new task. the remotely stored list of robotic devices comprises an entry for each respective robotic device of the plurality of robotic devices associating the respective robotic device with an identifier and a heartbeat. the method additionally includes assigning the new task to the additional robotic device based on the additional robotic device having an active heartbeat. assigning the new task to the additional robotic device comprises associating the new task with the additional robotic device in the remotely stored list of tasks to cause the additional robotic device to carry out the new task.
20240295979. Multi-Pass Distributed Data Shuffle_simplified_abstract_(google llc)
Inventor(s): Mohsen Vakilian of Kirkland WA (US) for google llc, Hossein Ahmadi of Seattle WA (US) for google llc
IPC Code(s): G06F3/06, G06F12/02, G06F12/0804
CPC Code(s): G06F3/0644
Abstract: a system and method for repartitioning data in a distributed network. the method may include executing, by one or more processors, a first pass of a data set from a plurality of first sources to a plurality of first sinks, each first sink collecting data from one or more of the first sources, and executing, by the one or more processors, a second pass of the data set from a plurality of second sources to a plurality of second sinks, each one of the plurality of first sinks corresponding to one of the plurality of second sources, and each second sink collecting data from one or more of the second sources. executing the first and second passes causes the data set to be repartitioned such that one or more second sinks collect data that originated from two or more of the first sources.
Inventor(s): Ori Isachar of Tel Aviv (IL) for google llc, Shay Gal-On of Mountain View CA (US) for google llc, Martin Guy Dixon of Portland OR (US) for google llc
IPC Code(s): G06F11/26, G06F11/22
CPC Code(s): G06F11/26
Abstract: the technology generally relates to systems and methods for performing in-field testing of processing cores within a system-on-chip (soc), so as to identify faults, including those associated with silent data corruption. for example, an soc may contain operational cores and spare cores. an operational core may be selected for testing while a spare core is used to replace the tested core. in addition, a spare core may be used to replace an operational core that has been determined to be corrupted.
20240296105. Dedicated Telemetry Subsystem For Telemetry Data_simplified_abstract_(google llc)
Inventor(s): Shay Gal-On of Mountain View CA (US) for google llc, Ori Isachar of Tel Aviv (IL) for google llc, Victor W. Lee of Santa Clara CA (US) for google llc, Stephane Eranian of Los Gatos CA (US) for google llc, Sreekumar Vadakke Kodakara of Campbell CA (US) for google llc, Yunlian Jiang of Fremont CA (US) for google llc, Guy Costi of Shoam (IL) for google llc
IPC Code(s): G06F11/34
CPC Code(s): G06F11/349
Abstract: generally disclosed herein is an approach for a telemetry subsystem enabling the telemetry data to be collected and processed without the need to interrupt processing jobs being processed by processing cores. the telemetry subsystem may include one or more telemetry cores dedicated to telemetry data collection. telemetry cores are configured to receive telemetry data from telemetry agents, processing cores, and other components of a system on chip (soc).
Inventor(s): Victor Carbune of Zurich (CH) for google llc, Thomas Deselaers of Zurich (CH) for google llc
IPC Code(s): G06F40/30, G06F16/93, G06F40/169, G06F40/20, G06F40/216, G06F40/284, G06F40/295, G06N3/042
CPC Code(s): G06F40/30
Abstract: implementations described herein determine, for a given document generated by a given source, one or more portions of content (e.g., phrase(s), image(s), paragraph(s), etc.) of the given document that may be influenced by a source perspective of the given source. further, implementations determine one or more additional resources that are related to the given source and that are related to the portion(s) of content of the given document. yet further, implementations utilize the additional resource(s) to determine additional content that provides context for the portion(s) that may be influenced by a source perspective. a relationship, between the additional resource(s) and the portions of the given document, can be defined. based on the relationship being defined, the additional content can be caused to be rendered at a client device in response to the client device accessing the given document.
20240296313. GENERATING NATURAL LANGUAGE DESCRIPTIONS OF IMAGES_simplified_abstract_(google llc)
Inventor(s): Samy Bengio of Los Altos CA (US) for google llc, Oriol Vinyals of London (GB) for google llc, Alexander Toshkov Toshev of San Francisco CA (US) for google llc, Dumitru Erhan of San Francisco CA (US) for google llc
IPC Code(s): G06N3/047, G06F40/40, G06N3/045
CPC Code(s): G06N3/047
Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating descriptions of input images. one of the methods includes obtaining an input image; processing the input image using a first neural network to generate an alternative representation for the input image; and processing the alternative representation for the input image using a second neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.
Inventor(s): David Wilson Romero Guzman of Amstelveen (NL) for google llc, Neil Zeghidour of Paris (FR) for google llc
IPC Code(s): G06N3/084, G06N3/048
CPC Code(s): G06N3/084
Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for jointly learning the architecture of a neural network during the training of the neural network. in particular, the architecture of the neural network is learned using differentiable parametric masks.
Inventor(s): Ilya Gennadyevich Gelfenbeyn of Sunnyvale CA (US) for google llc, Artem Goncharuk of Arlington VA (US) for google llc, Pavel Aleksandrovich Sirotin of Sunnyvale CA (US) for google llc
IPC Code(s): G06N5/022, G06F40/30
CPC Code(s): G06N5/022
Abstract: a method for example-driven machine learning is disclosed herein. the method comprises maintaining a plurality of dialog system rules and a knowledge database including a plurality of intent objects and a plurality of entity objects. the plurality of intent objects and the plurality of entity objects are associated with at least one dialog system rule. an exemplary phrase is received and one or more linguistic elements are retrieved from the exemplary phrase. it is determined that at least one of the linguistic elements is directed to at least one of the plurality of intent objects of the plurality of entity objects and at least one of the linguistic elements in association with the at least one dialog system rule is added to the knowledge database.
20240296359. CLASSIFICATION USING QUANTUM NEURAL NETWORKS_simplified_abstract_(google llc)
Inventor(s): Edward Henry Farhi of Venice CA (US) for google llc, Hartmut Neven of Malibu CA (US) for google llc
IPC Code(s): G06N10/00, G06N3/063, G06N3/082, G06N3/084
CPC Code(s): G06N10/00
Abstract: this disclosure relates to classification methods that can be implemented on quantum computing systems. according to a first aspect, this specification describes a method for training a classifier implemented on a quantum computer, the method comprising: preparing a plurality of qubits in an input state with a known classification, said plurality of qubits comprising one or more readout qubits; applying one or more parameterised quantum gates to the plurality of qubits to transform the input state to an output state; determining, using a readout state of the one or more readout qubits in the output state, a predicted classification of the input state; comparing the predicted classification with the known classification; and updating one or more parameters of the parameterised quantum gates in dependence on the comparison of the predicted classification with the known classification.
Inventor(s): William Huggins of Oakland CA (US) for google llc, Joonho Lee of New York NY (US) for google llc, Ryan Babbush of Mountain View CA (US) for google llc
IPC Code(s): G06N10/20
CPC Code(s): G06N10/20
Abstract: methods, systems, and apparatus for hybrid quantum-classical quantum monte carlo. in one aspect, a method includes receiving, by a classical computer, data generated by a quantum computer, the data representing results of measurements of a trial wavefunction, wherein the trial wavefunction approximates the target wavefunction and is prepared by the quantum computer, computing, by the classical computer, a classical shadow of the trial wavefunction using the data representing the results of the measurements of the trial wavefunction, and performing, by the classical computer, imaginary time propagation for a sequence of imaginary time steps of an initial wavefunction using a hamiltonian that characterizes the fermionic quantum system, wherein: the imaginary time propagation is performed until predetermined convergence criteria are met; and performing each imaginary time step of the imaginary time propagation comprises updating the wavefunction for the previous imaginary time step using the classical shadow of the trial wavefunction to obtain a wavefunction for the current imaginary time step.
Inventor(s): Nicholas Charles Rubin of San Francisco CA (US) for google llc, Andrew Zhao of Albuquerque NM (US) for google llc
IPC Code(s): G06N10/60, G06F17/16
CPC Code(s): G06N10/60
Abstract: methods, systems, and apparatus for solving quadratic optimization problems over orthogonal groups using quantum computing. in one aspect, a method includes receiving data representing a quadratic optimization problem, wherein decision variables of the quadratic optimization problem take values in an orthogonal group or a special orthogonal group; encoding the quadratic optimization problem as a quantum hamiltonian, the encoding comprising using a clifford algebra representation of the group to map orthogonal matrices or special orthogonal matrices in the group to respective quantum states in a hilbert space; determining an approximate eigenstate of the quantum hamiltonian; computing expectation values of pauli operators with respect to the approximate eigenstate, wherein the pauli operators comprise operators obtained by mapping multiplication operations of the clifford algebra into the hilbert space; and rounding the expectation values of the pauli operators to elements of the orthogonal group to obtain a solution to the quadratic optimization problem.
Inventor(s): Boris Dadachev of Mountain View CA (US) for google llc, Kishore Papineni of Mountain View CA (US) for google llc, Siva Kumar Gorantla of Mountain View CA (US) for google llc, Levent Koc of Mountain View CA (US) for google llc
IPC Code(s): G06Q30/0251, G06Q30/0241
CPC Code(s): G06Q30/0256
Abstract: example methods, apparatus, and systems for generating custom content responsive to a received search query are disclosed. an example method for generating custom content responsive to a received search query includes receiving, via a communication interface from a user computing device, a search query including one or more search terms; determining, responsive to the search query, a set of search results relevant to the search query; identifying, responsive to the search query, third-party content and/or a third party relevant to the search query; generating, based on (i) the search query and (ii) the third-party content or the third party, custom content relevant to the search query and related to a landing page associated with the third-party content or the third party, for presentation along with the set of research results; and transmitting, via the communication interface to the user computing device, the custom content.
20240296596. PERSONALIZED TEXT-TO-IMAGE DIFFUSION MODEL_simplified_abstract_(google llc)
Inventor(s): Kfir Aberman of San Mateo CA (US) for google llc, Nataniel Ruiz Gutierrez of Brookline MA (US) for google llc, Michael Rubinstein of Natick MA (US) for google llc, Yuanzhen Li of Newton Centre CA (US) for google llc, Yael Pritch Knaan of Tel Aviv (IL) for google llc, Varun Jampani of Rockland MA (US) for google llc
IPC Code(s): G06T11/00, G06V10/764
CPC Code(s): G06T11/00
Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text-to-image model so that the text-to-image model generates images that each depict a variable instance of an object class when the object class without the unique identifier is provided as a text input, and that generates images that each depict a same subject instance of the object class when the unique identifier is provided as the text input.
Inventor(s): Andrii Maksai of Zurich (CH) for google llc, Henry Rowley of Cupertino CA (US) for google llc, Jesse Berent of Geneva (CH) for google llc, Claudiu Musat of Vaud (CH) for google llc
IPC Code(s): G06T11/20, G06V30/19, G06V30/32
CPC Code(s): G06T11/203
Abstract: systems and methods for editing and generating digital ink. the present technology may provide systems and methods for training a handwriting model to generate digital ink that is stylistically and visually consistent with an original handwriting input, but which incorporates one or more changes to the text of the original handwriting input. in some examples, training may be performed using training examples that include an original handwriting sample and an original label representing the sequence of characters in the original handwriting sample. in such a case, the original handwriting sample may be processed to generate a style vector that is randomly masked, and the handwriting model may then be trained to generate a predicted handwriting sample that closely matches the original handwriting sample using the masked style vector and the original label as inputs.
Inventor(s): James Sraw Singh of New York NY (US) for google llc, Ivan Neulander of Los Angeles CA (US) for google llc, Subramanian Shyamsunder Mathur of New York NY (US) for google llc, Agustin III Olivan Venezuela of Queens NY (US) for google llc
IPC Code(s): G06T19/00, G06F16/953, G06T11/60, G06T17/00
CPC Code(s): G06T19/006
Abstract: systems and methods for generating augmented reality prerenderings can provide the benefit of an augmented reality rendering without requiring the use of user data. template images can be used instead of user data to protect the user's privacy while enabling the user to see an object or product rendered onto a preferred template image or a variety of template images.
20240296832. Self-Training With Oracle And Top-Ranked Hypotheses_simplified_abstract_(google llc)
Inventor(s): Andrew M. Rosenberg of Brooklyn NY (US) for google llc, Murali Karthick Baskar of Mountain View CA (US) for google llc, Bhuvana Ramabhadran of Mt. Kisco NY (US) for google llc
IPC Code(s): G10L15/06, G10L15/01, G10L15/16, G10L15/197
CPC Code(s): G10L15/063
Abstract: a method includes, for each training sample of a plurality of training samples, processing, using an rnn-t model, a corresponding sequence of acoustic frames to obtain an n-best list of speech recognition hypotheses, and, for each speech recognition hypothesis of the n-best list, determining a corresponding number of word errors relative to a corresponding ground-truth transcription. for a top-ranked hypothesis from the n-best list, the method includes determining a first loss based on the corresponding ground-truth transcription. the method includes identifying, as an oracle hypothesis, the speech recognition hypothesis from the n-best list having the smallest corresponding number of word errors relative to the corresponding ground-truth transcription, and determining a second loss for the oracle hypothesis based on the corresponding ground-truth transcription. the method includes determining a corresponding self-training combined loss based on the first and second losses, and training the model based on the corresponding self-training combined loss.
Inventor(s): Françoise Beaufays of Mountain View CA (US) for google llc, Khe Chai Sim of Dublin CA (US) for google llc, Johan Schalkwyk of Scarsdale NY (US) for google llc
IPC Code(s): G10L15/06, G10L15/187, G10L15/22, G10L15/30
CPC Code(s): G10L15/063
Abstract: implementations disclosed herein are directed to unsupervised federated training of global machine learning (“ml”) model layers that, after the federated training, can be combined with additional layer(s), thereby resulting in a combined ml model. processor(s) can: detect audio data that captures a spoken utterance of a user of a client device; process, using a local ml model, the audio data to generate predicted output(s); generate, using unsupervised learning locally at the client device, a gradient based on the predicted output(s); transmit the gradient to a remote system; update weight(s) of the global ml model layers based on the gradient; subsequent to updating the weight(s), train, using supervised learning remotely at the remote system, a combined ml model that includes the updated global ml model layers and additional layer(s); transmit the combined ml model to the client device; and use the combined ml model to make prediction(s) at the client device.
Inventor(s): Jason Sanders of New York NY (US) for google llc, Gabriel Taubman of Brooklyn NY (US) for google llc, John J. Lee of Long Island City NY (US) for google llc
IPC Code(s): G10L15/08, G06F16/683, G10L15/18, G10L15/22, G10L15/26, G10L21/0208, G10L21/0272, G10L25/48, H04M3/493
CPC Code(s): G10L15/08
Abstract: implementations relate to techniques for providing context-dependent search results. a computer-implemented method includes receiving an audio stream at a computing device during a time interval, the audio stream comprising user speech data and background audio, separating the audio stream into a first substream that includes the user speech data and a second substream that includes the background audio, identifying concepts related to the background audio, generating a set of terms related to the identified concepts, influencing a speech recognizer based on at least one of the terms related to the background audio, and obtaining a recognized version of the user speech data using the speech recognizer.
Inventor(s): Andrew M. Rosenberg of Brooklyn NY (US) for google llc, Yosuke Higuchi of Mountain View CA (US) for google llc, Bhuvana Ramabhadran of Mt. Kisco NY (US) for google llc
IPC Code(s): G10L15/16, G10L15/22
CPC Code(s): G10L15/16
Abstract: a method includes receiving a sequence of acoustic frames characterizing an utterance. during a first pass, the method includes generating first-pass audio encodings based on the sequence of acoustic frames using a stack of mask-conformer blocks of an acoustic encoder, generating a first-pass transcription of the utterance based on the first-pass audio encodings using a speech recognition decoder, and generating a first-pass masked output sequence using a mask-predict decoder of the acoustic encoder. during a second pass, the method includes generating second-pass audio encodings by performing cross-attention on the sequence of acoustic frames and the masked first-pass transcription using the stack of mask-conformer blocks of the acoustic encoder and generating a second-pass transcription of the utterance based on the second-pass audio encodings using the speech recognition decoder.
Inventor(s): Shaan Jagdeep Patrick Bijwadia of San Francisco CA (US) for google llc, Shuo-yiin Chang of Sunnyvale CA (US) for google llc, Tara N. Sainath of Jersey City NJ (US) for google llc, Weiran Wang of San Jose CA (US) for google llc, Zhong Meng of Mountain View CA (US) for google llc
IPC Code(s): G10L15/197, G10L15/02, G10L15/06
CPC Code(s): G10L15/197
Abstract: a joint auxiliary task and asr model includes an encoder to receive a sequence of acoustic frames and generate, at each of a plurality of output steps, a higher-order feature representation for a corresponding acoustic frame. the model also includes a multi-output hat decoder to generate at each of the plurality of output steps a probability distribution over possible speech recognition hypotheses, and an indication of whether the output step corresponds to an auxiliary token associated with a particular auxiliary task. the model is trained by a jeit training process based on: a paired training data set including paired audio data and transcriptions, the transcriptions annotated with ground-truth auxiliary tokens associated with the particular auxiliary task; and an unpaired training data set including textual utterances not paired with any corresponding audio data, the textual utterances annotated with the ground-truth auxiliary tokens associated with the particular auxiliary task.
Inventor(s): Françoise Beaufays of Mountain View CA (US) for google llc, Rajiv Mathews of Sunnyvale CA (US) for google llc, Dragan Zivkovic of Sunnyvale CA (US) for google llc, Kurt Partridge of San Francisco CA (US) for google llc, Andrew Hard of Menlo Park CA (US) for google llc
IPC Code(s): G10L15/22, G10L15/065, G10L15/10, G10L15/30
CPC Code(s): G10L15/22
Abstract: processor(s) of a client device can: receive sensor data that captures environmental attributes of an environment of the client device; process the sensor data using a machine learning model to generate a predicted output that dictates whether one or more currently dormant automated assistant functions are activated; making a decision as to whether to trigger the one or more currently dormant automated assistant functions; subsequent to making the decision, determining that the decision was incorrect; and in response to determining that the determination was incorrect, generating a gradient based on comparing the predicted output to ground truth output. in some implementations, the generated gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model. in some implementations, the generated gradient is additionally or alternatively transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.
Inventor(s): Matthew Sharifi of Kilchberg (CH) for google llc, Victor Carbune of Zurich (CH) for google llc
IPC Code(s): G10L17/08, G06F3/16, G06F21/32, G10L15/08, G10L15/22, G10L17/02, G10L17/04, G10L17/10, G10L17/14, G10L17/18, G10L17/22, G10L17/24
CPC Code(s): G10L17/08
Abstract: implementations relate to automatic generation of speaker features for each of one or more particular text-dependent speaker verifications (td-svs) for a user. implementations can generate speaker features for a particular td-sv using instances of audio data that each capture a corresponding spoken utterance of the user during normal non-enrollment interactions with an automated assistant via one or more respective assistant devices. for example, a portion of an instance of audio data can be used in response to: (a) determining that recognized term(s) for the spoken utterance captured by that the portion correspond to the particular td-sv; and (b) determining that an authentication measure, for the user and for the spoken utterance, satisfies a threshold. implementations additionally or alternatively relate to utilization of speaker features, for each of one or more particular td-svs for a user, in determining whether to authenticate a spoken utterance for the user.
20240297783. AGGREGATING ENCRYPTED NETWORK VALUES_simplified_abstract_(google llc.)
Inventor(s): Gang Wang of Frederick MD (US) for google llc., Marcel M.Moti Yung of New York NY (US) for google llc.
IPC Code(s): H04L9/08, H04L9/00, H04L9/14
CPC Code(s): H04L9/0825
Abstract: methods, systems, and apparatus, including a method for determining network measurements. in some respects, a method includes receiving, by a first aggregation server and from each of multiple client devices, encrypted impression data. a second aggregation server received from each of at least a portion of the multiple client devices, conversion data that includes, for each conversion recorded by the client device, encrypted conversion value data. the first aggregation server and the second aggregation server perform a multi-party computation process to decrypt the encrypted impression data and the encrypted conversion data.
20240297796. ANONYMOUS EVENT ATTESTATION WITH GROUP SIGNATURES_simplified_abstract_(google llc)
Inventor(s): Gang Wang of Frederick MD (US) for google llc, Marcel M. Moti Yung of New York NY (US) for google llc
IPC Code(s): H04L9/32
CPC Code(s): H04L9/3255
Abstract: methods, systems, and computer media provide attestation tokens that protect the integrity of communications transmitted from client devices, while at the same time avoiding the use of stable device identifiers that could be used to track client devices or their users. in one approach, client devices can receive anonymous certificates from a device integrity computing system signifying membership in a selected device trustworthiness group, and attestation tokens can be signed anonymously with the anonymous certificates using a group signature scheme. client devices can include throttlers imposing limits on the quantity of attestation tokens created by the client device.
20240297845. Encoding Source Routes Using MPLS Sub-Labels_simplified_abstract_(google llc)
Inventor(s): Alexander Krentsel of Berkeley CA (US) for google llc, Ashok Narayanan of Lexington MA (US) for google llc, Sylvia Ratnasamy of Berkeley CA (US) for google llc, Robert Shakir of San Francisco CA (US) for google llc
IPC Code(s): H04L45/50, H04L45/00
CPC Code(s): H04L45/507
Abstract: generally disclosed herein is an approach for modifying use of segment routing multiprotocol label switching (sr-mpls) allowing an arbitrary mpls control plane and traditional mpls data plane to utilize a single mpls label to represent two or more edges in a path. mpls labels may be divided into smaller sub-labels, which together uniquely represent a pair of edges along a route. in one example, a single mpls label may be divided into two sub-labels, the first sub-label representing a first edge, and the second sub-label representing a second edge. in this regard, longer source routes may be supported in a packet header in network designs that implement strict source routing.
Inventor(s): Justin Lewis of Marina del Rey CA (US) for google llc, Kevin Greene of San Francisco CA (US) for google llc
IPC Code(s): H04L67/306, G06F3/0482, H04L51/52, H04L65/612, H04L67/01, H04L67/50, H04L67/52, H04L67/55, H04L67/60
CPC Code(s): H04L67/306
Abstract: systems and methods for recommending media content to a user based on information associated with a referral source that referred the user to a media item provided by a source of the media content are presented. in one or more aspects, a system is provided that includes a presentation component that presents, via user a interface, a first media item associated with a media presentation source referred to a user through a referral source. the system further includes an analytics component that identifies a second media item based on media items associated with the media presentation source that are referred to other users through the referral source, and a recommendation component that recommends the second media item to the user through the user interface.
Inventor(s): Kevin Greene of San Francisco CA (US) for google llc, Justin Lewis of Marina del Rey CA (US) for google llc
IPC Code(s): H04N21/466, G06F16/438, G06F16/783, G06F16/9032, H04N21/45, H04N21/472
CPC Code(s): H04N21/4668
Abstract: systems and methods for identifying related videos based on elements tagged in the videos are presented. in an aspect, a system includes an identification component configured to identify tagged elements in a video, a matching component configured to identify other videos that include one or more of the tagged elements, and a recommendation component configured to recommend the other videos for viewing based on a current or past request to play the video.
- GOOGLE LLC
- G02B27/01
- G02B3/14
- CPC G02B27/0172
- Google llc
- G02B27/09
- G02B26/08
- G02B26/10
- CPC G02B27/0916
- G05D1/02
- CPC G05D1/0295
- G06F3/06
- G06F12/02
- G06F12/0804
- CPC G06F3/0644
- G06F11/26
- G06F11/22
- CPC G06F11/26
- G06F11/34
- CPC G06F11/349
- G06F40/30
- G06F16/93
- G06F40/169
- G06F40/20
- G06F40/216
- G06F40/284
- G06F40/295
- G06N3/042
- CPC G06F40/30
- G06N3/047
- G06F40/40
- G06N3/045
- CPC G06N3/047
- G06N3/084
- G06N3/048
- CPC G06N3/084
- G06N5/022
- CPC G06N5/022
- G06N10/00
- G06N3/063
- G06N3/082
- CPC G06N10/00
- G06N10/20
- CPC G06N10/20
- G06N10/60
- G06F17/16
- CPC G06N10/60
- G06Q30/0251
- G06Q30/0241
- CPC G06Q30/0256
- G06T11/00
- G06V10/764
- CPC G06T11/00
- G06T11/20
- G06V30/19
- G06V30/32
- CPC G06T11/203
- G06T19/00
- G06F16/953
- G06T11/60
- G06T17/00
- CPC G06T19/006
- G10L15/06
- G10L15/01
- G10L15/16
- G10L15/197
- CPC G10L15/063
- G10L15/187
- G10L15/22
- G10L15/30
- G10L15/08
- G06F16/683
- G10L15/18
- G10L15/26
- G10L21/0208
- G10L21/0272
- G10L25/48
- H04M3/493
- CPC G10L15/08
- CPC G10L15/16
- G10L15/02
- CPC G10L15/197
- G10L15/065
- G10L15/10
- CPC G10L15/22
- G10L17/08
- G06F3/16
- G06F21/32
- G10L17/02
- G10L17/04
- G10L17/10
- G10L17/14
- G10L17/18
- G10L17/22
- G10L17/24
- CPC G10L17/08
- H04L9/08
- H04L9/00
- H04L9/14
- CPC H04L9/0825
- Google llc.
- H04L9/32
- CPC H04L9/3255
- H04L45/50
- H04L45/00
- CPC H04L45/507
- H04L67/306
- G06F3/0482
- H04L51/52
- H04L65/612
- H04L67/01
- H04L67/50
- H04L67/52
- H04L67/55
- H04L67/60
- CPC H04L67/306
- H04N21/466
- G06F16/438
- G06F16/783
- G06F16/9032
- H04N21/45
- H04N21/472
- CPC H04N21/4668
