Amazon Technologies, Inc. patent applications on October 3rd, 2024
Patent Applications by Amazon Technologies, Inc. on October 3rd, 2024
Amazon Technologies, Inc.: 20 patent applications
Amazon Technologies, Inc. has applied for patents in the areas of G06F8/65 (2), G06F16/23 (2), G06T7/70 (2), G06T7/12 (2), G06N10/20 (2) G06F8/65 (2), G06T7/12 (1), H04L67/1008 (1), H04L63/205 (1), H04L47/762 (1)
With keywords such as: vehicle, quantum, service, deployment, software, network, data, streaming, systems, and circuit in patent application abstracts.
Patent Applications by Amazon Technologies, Inc.
Inventor(s): Roland Mesde of Cupertino CA (US) for amazon technologies, inc., Alex Bessonov of San Jose CA (US) for amazon technologies, inc., Brian Ewanchuk of Redmond WA (US) for amazon technologies, inc., George Sherif Kamal Hanna of Toronto (CA) for amazon technologies, inc., Paolo Gruenberg Hilario of West Linn OR (US) for amazon technologies, inc.
IPC Code(s): G06F8/65
CPC Code(s): G06F8/65
Abstract: systems and methods for providing vehicle software deployment plans that include one or more fallback deployment plans are disclosed. in some embodiments, a vehicle software deployment system determines a deployment plan for deploying one or more software applications one or more electronic control units (ecus) of a vehicle. additionally, for one or more failure scenarios, the vehicle software deployment system determines one or more respective fallback deployment plans, such as a deployment plan that assumes one of the ecus of the vehicle has failed. a deployment plan bundle is provided to the vehicle, comprising a primary deployment plan as well as the one or more fallback deployment plans. in the event that one of the failure scenarios takes place with respect to the vehicle, a deployment agent of the vehicle automatically deploys one of the fallback deployment plans that was provided with the deployment bundle.
Inventor(s): Roland Mesde of Cupertino CA (US) for amazon technologies, inc., Alex Bessonov of San Jose CA (US) for amazon technologies, inc., Brian Ewanchuk of Redmond WA (US) for amazon technologies, inc., George Sherif Kamal Hanna of Toronto (CA) for amazon technologies, inc., Paolo Gruenberg Hilario of West Linn OR (US) for amazon technologies, inc.
IPC Code(s): G06F8/65
CPC Code(s): G06F8/65
Abstract: systems and methods of determining and providing optimized deployment plans for deploying software to vehicles are disclosed. in some embodiments, a vehicle software deployment system evaluates one or more cost functions to determine relative costs of different deployment configuration options for deploying software to a vehicle, such as resource costs (e.g., bandwidth, compute, memory, etc.), isolation costs (e.g., limited access to input information, limited connectivity to other ecus, etc.), performance costs, etc. based on the evaluation of the one or more cost functions, the vehicle software deployment system determines an optimized deployment plan. also, the vehicle software deployment system receives telemetry data from the vehicle and automatically determines updated optimized deployment plans in response to changes in configuration of the vehicle indicated in the telemetry data.
Inventor(s): Roland Mesde of Cupertino CA (US) for amazon technologies, inc., Alex Bessonov of San Jose CA (US) for amazon technologies, inc., George Sherif Kamal Hanna of Toronto (CA) for amazon technologies, inc., Nitin Giri of Bothell WA (US) for amazon technologies, inc.
IPC Code(s): G06F11/36, B60R16/023
CPC Code(s): G06F11/3692
Abstract: a vehicle software test environment management system provides a virtual vehicle environment that includes virtual electronic control units (vecus) having a virtual bus connectivity configuration used to simulate respective ones of electronic control units (ecus) of a real-world vehicle. the vehicle software test environment management system determines respective instance types of one or more virtual compute instances to be used to implement the vecus based on respective configuration of respective ones of the ecus and further determines respective machine images to emulate respective software environments of the respective ones of the ecus. the vehicle software test environment management system may also deploy a vehicle software application to be certified on one or more of the vecus and test the deployed vehicle software application using recorded signals of one or more ecus of the real-world vehicle.
Inventor(s): Anurag Windlass Gupta of Atherton CA (US) for amazon technologies, inc., Neal Fachan of Seattle WA (US) for amazon technologies, inc., Samuel James McKelvie of Seattle WA (US) for amazon technologies, inc., Laurion Darrell Burchall of Seattle WA (US) for amazon technologies, inc., Christopher Richard Newcombe of Kirkland WA (US) for amazon technologies, inc., Pradeep Jnana Madhavarapu of Mountain View CA (US) for amazon technologies, inc., Benjamin Tobler of Seattle WA (US) for amazon technologies, inc., James McClellan Corey of Bothell WA (US) for amazon technologies, inc.
IPC Code(s): G06F16/23, G06F11/14, G06F11/20
CPC Code(s): G06F16/2365
Abstract: a database system may include a database service and a separate distributed storage service. the database service (or a database engine head node thereof) may be responsible for query parsing, optimization, and execution, transactionality, and consistency, while the storage service may be responsible for generating data pages from redo log records and for durability of those data pages. for example, in response to a write request directed to a particular data page, the database engine head node may generate a redo log record and send it, but not the data page, to a storage service node. the storage service node may store the redo log record and return a write acknowledgement to the database service prior to applying the redo log record. the server node may apply the redo log record and other redo log records to a previously stored version of the data page to create a current version.
Inventor(s): Marc Brooker of Seattle WA (US) for amazon technologies, inc., Falesh Singh of Seattle WA (US) for amazon technologies, inc., Gourav Roy of Redmond WA (US) for amazon technologies, inc., Steven Michael Hershey of Seattle WA (US) for amazon technologies, inc., Marc Bowes of Seattle WA (US) for amazon technologies, inc.
IPC Code(s): G06F16/27, G06F16/23
CPC Code(s): G06F16/275
Abstract: synchronous replication for a distributed database system may be performed using an erasure coding scheme. a request that causes a write to a database hosted in a distributed database system is received. a replication message for a synchronous replication technique is generated, then divided and encoded into a number of chunks according to an erasure encoding scheme that allows the replication message to be reassembled with less than the number of chunks. the chunks are sent to another instance of the database which receives and reassembles the replication message from the chunks and responds to acknowledge that the write is committed.
Inventor(s): Ivan Velickovic of Vancouver (CA) for amazon technologies, inc., Steve Robert DeVos of Mercer Island WA (US) for amazon technologies, inc., James Michael Rolette of Round Rock TX (US) for amazon technologies, inc., Aram Golbaghikoukia of Coquitlam (CA) for amazon technologies, inc., Thiago Macedo De Sousa of Langley (CA) for amazon technologies, inc., Vivek Mishra of Bothell WA (US) for amazon technologies, inc., Soumyajit Das of Everett WA (US) for amazon technologies, inc., Shubham Anand of Seattle WA (US) for amazon technologies, inc., Boo Boon Khoo of Bellevue WA (US) for amazon technologies, inc.
IPC Code(s): G06F21/40, G06F21/55
CPC Code(s): G06F21/40
Abstract: described techniques and systems can identify a request to transfer one or more computer-implemented resources associated with a first computer-implemented account to a second computer-implemented account, the one or more computer-implemented resources at least in part managed through a service accessible by at least one entity selected to consider the request, the service implemented separately from another service to manage access to the one or more computer-implemented resources. also, the techniques and systems can confirm the at least one entity approved the request to transfer the one or more computer-implemented resources associated with the first computer-implemented account, and transfer the one or more computer-implemented resources to the second computer-implemented account.
Inventor(s): Alexandre DAVID of Seattle WA (US) for amazon technologies, inc., Jeremiah M. DUNHAM of Arlington VA (US) for amazon technologies, inc., Amit GOEL of Portland OR (US) for amazon technologies, inc., Dejan JOVANOVIC of Brooklyn NY (US) for amazon technologies, inc., Rami Gokhan KICI of Cupertino CA (US) for amazon technologies, inc.
IPC Code(s): G06N5/01, G06N5/045
CPC Code(s): G06N5/013
Abstract: techniques are described for executing satisfiability modulo theories (smt) solvers in a “shadow” system configuration where input queries are provided to a primary smt solver system and additionally to one or more secondary smt solver systems. smt solver systems can be used by cloud providers and in other computing environments to analyze the implications of configured user account policies defining permissions with respect to users' computing resources and associated actions within a computing environment, to help ensure the security of computing resources and user data, etc. the results generated by a primary smt solver system can be provided to one or more secondary smt solver systems, where each of the secondary smt systems can comprise different system components or different versions of system components, to assess the correctness of the primary smt solver system, to compare performance metrics, among other possible types of analyses.
Inventor(s): Yiheng Duan of Seattle WA (US) for amazon technologies, inc., Yunong Shi of Old Greenwich CT (US) for amazon technologies, inc.
IPC Code(s): G06N10/20, G06N3/092
CPC Code(s): G06N10/20
Abstract: techniques for solving quantum circuit mapping problems using reinforcement learning techniques are disclosed. quantum circuit mapping often requires the use of swap gates in order to configure logical quantum computations to be executed using fixed quantum hardware device layouts. a reinforcement learning model takes inputs such as a logical quantum circuit, a physical qubit connectivity graph corresponding to a quantum hardware device, and an initial qubit allocation scheme, and uses such information to schedule quantum gates of the logical quantum circuit for execution using respective physical qubits of the quantum hardware device. a reinforcement learning model that is configured to solve such quantum circuit mapping problems may comprise a neural network that is assisted by a monte carlo tree search (mcts) algorithm, wherein the mcts algorithm guides the neural network towards quantum circuit routing pathways which are more efficient (e.g., require fewer swap gates to be scheduled).
Inventor(s): Yunong Shi of Old Greenwich CT (US) for amazon technologies, inc., Marijn J. Heule of Pittsburgh PA (US) for amazon technologies, inc., Michael William Whalen of Edina MN (US) for amazon technologies, inc., Bruno Dutertre of Mountain View CA (US) for amazon technologies, inc., Eric M Kessler of New Rochelle NY (US) for amazon technologies, inc., Benjamin Kiesl-Reiter of Munich (DE) for amazon technologies, inc., Robert Jones of Beaverton OR (US) for amazon technologies, inc., David Nunnerley of Bainbridge Island WA (US) for amazon technologies, inc.
IPC Code(s): G06N10/60, G06N10/20, G06N10/40
CPC Code(s): G06N10/60
Abstract: techniques for encoding quantum circuit mapping problems as sat solver optimization problems are disclosed. quantum circuit mapping often requires the use of swap gates in order to configure logical quantum computations to be executed using fixed quantum hardware device layouts. a quantum compilation service takes a logical quantum circuit, a physical qubit connectivity graph, and a requested number of swap gates to solve the mapping using and encodes the information into a conjunctive normal form (cnf) equation using a layout-transition-based encoding scheme. the cnf equation is then provided to a sat solver which attempts to determine an assignment for the mapping using the set number of swap gates requested. multiple cnf equations corresponding to different requested numbers of swap gates may be solved for in parallel using multiple sat solving instances.
20240330738. QUANTUM COMPILATION SERVICE_simplified_abstract_(amazon technologies, inc.)
Inventor(s): Yunong Shi of Old Greenwich CT (US) for amazon technologies, inc., Ravi Kiran Chilakapati of Mill Creek WA (US) for amazon technologies, inc., Jeffrey Paul Heckey of Seattle WA (US) for amazon technologies, inc., Jon-Mychael Allen Best of Seattle WA (US) for amazon technologies, inc., Eric M Kessler of New Rochelle NY (US) for amazon technologies, inc.
IPC Code(s): G06N10/80
CPC Code(s): G06N10/80
Abstract: systems and method for implementing quantum circuit compilation as-a-service are disclosed. in some embodiments, a quantum circuit compilation service is configured to compile quantum circuits for a plurality of third-party customers, wherein the compilation service supports compiling quantum circuits to be executed on a plurality of different quantum processing units that utilize various different quantum computing technologies. in some embodiments, the quantum computing service generates a customized compilation job plan for each quantum circuit to be compiled. the compilation job plan may reference modular compilation passes stored in a repository of the quantum circuit compilation service. the modular passes may be mixed and matched as needed to allow for compilation of a wide-variety of quantum circuits to be executed using various different quantum computing technologies.
Inventor(s): Eilon Shitrit of Haifa (IL) for amazon technologies, inc., Soomin Lee of Mountain View CA (US) for amazon technologies, inc., Avihai Mejer of Atlit (IL) for amazon technologies, inc.
IPC Code(s): G06Q30/0601
CPC Code(s): G06Q30/0631
Abstract: an automatic technique is disclosed to enrich presented answers by highlighting relevant shopping recommendations. the shopping recommendations can either be highlighted within the answer itself, or as an auxiliary list of suggestions. a model is described for selecting phrases from the answer text (sequences of consecutive terms called noun phrases) that refer to potential products that likely represent relevant shopping recommendation in context of the question-answer pair. the noun phrases are then ranked in order of importance. the top-ranked noun phrases are used to search products to be displayed in association with the noun phrases. clicking or tapping on a highlighted noun phrase launches a shopping-related flow, such as presenting a widget with product recommendations or running a search in a search engine.
Inventor(s): QIANLI FENG of SEATTLE WA (US) for amazon technologies, inc., RAGHU DEEP GADDE of BOTHELL WA (US) for amazon technologies, inc., ALEIX MARGARIT MARTINEZ of SEATTLE WA (US) for amazon technologies, inc.
IPC Code(s): G06T7/12, G06T7/50, G06T7/70, G06T7/90, G06V10/25, G06V10/56, G06V10/762, G06V10/82
CPC Code(s): G06T7/12
Abstract: to identify sets of pixels in a first image that correspond to different objects or a background, a first image is provided to a generative adversarial network (gan). the gan determines alternate images that retain the structural characteristics of the first image, such as the locations and shapes of objects, while modifying style characteristics, such as the colors of pixels. the images generated by the gan may then be analyzed, such as by using a k-means clustering algorithm, to determine sets of pixels at the same location that change color in a similar manner across the set of images. a set of pixels that changes in a similar manner across the images generated by the gan may be used as a mask representing an object or background to enable modification of the image without interfering with other objects.
Inventor(s): Larry Davis of Brooklyn NY (US) for amazon technologies, inc., Nicolas Heron of New York NY (US) for amazon technologies, inc., Amit Kumar Agrawal of Santa Clara CA (US) for amazon technologies, inc., Nina Mitra Khosrowsalafi of Austin TX (US) for amazon technologies, inc., Osama Makansi of Nufringen (DE) for amazon technologies, inc., Oleksandr Vorobiov of Albstadt (DE) for amazon technologies, inc.
IPC Code(s): G06T11/00, G06T7/12, G06T7/70, G06V10/764
CPC Code(s): G06T11/00
Abstract: systems and methods are described for generating images of synthesized bodies wearing a garment. for instance, a source image of a human or mannequin wearing a garment may be submitted to a synthesized human generation system. in response to receiving the source image, the synthesized human generation system may use a classifier to classify the image as depicting one or more body types or orientations. the synthesized human generation system may also apply segmentation to the source image to segment the garment pixels. the synthesized human generation system may then select one or more body generation machine learning models based on the classification of the source image. the synthesized human generation system may utilize the selected machine learning models to generate one or more output images of synthesized bodies that appear to be wearing the garment, using the segmented garment as input.
20240331686. RELEVANT CONTEXT DETERMINATION_simplified_abstract_(amazon technologies, inc.)
Inventor(s): Kai Wei of Pittsburgh PA (US) for amazon technologies, inc., Thanh Dac Tran of Logan UT (US) for amazon technologies, inc., Grant Strimel of Presto PA (US) for amazon technologies, inc.
IPC Code(s): G10L15/18, G06N3/08, G10L15/06, G10L15/16, G10L15/22, G10L15/28
CPC Code(s): G10L15/1815
Abstract: techniques for determining and storing relevant context information for a user input, such as a spoken input, are described. in some embodiments, context information is determined to be relevant on an audio frame basis. context scores for different types of context data (e.g., prior dialog turn data, user profile data, device information, etc.) are determined for individual audio frames corresponding to a spoken input. based on the corresponding context scores, the most relevant context is stored in a local context cache. the local context cache is updated as subsequent audio frames, of the user input, are processed. the data stored in the context cache is provided to downstream components to perform tasks such as asr, nlu and slu.
20240331821. MEDICAL CONVERSATIONAL INTELLIGENCE_simplified_abstract_(amazon technologies, inc.)
Inventor(s): Vijit Gupta of Mercer Island WA (US) for amazon technologies, inc., Matthew Chih-Hui Chiou of Seattle WA (US) for amazon technologies, inc., Amiya Kishor Chakraborty of Seattle WA (US) for amazon technologies, inc., Anuroop Arora of Seattle WA (US) for amazon technologies, inc., Varun Sembium Varadarajan of Redmond WA (US) for amazon technologies, inc., Sarthak Handa of Seattle WA (US) for amazon technologies, inc., Amit Vithal Sawant of New Brunswick NJ (US) for amazon technologies, inc., Glen Herschel Carpenter of Arvada CO (US) for amazon technologies, inc., Jesse Deng of Seattle WA (US) for amazon technologies, inc., Mohit Narendra Gupta of Seattle WA (US) for amazon technologies, inc., Rohil Bhattarai of Seattle WA (US) for amazon technologies, inc., Samuel Benjamin Schiff of New York NY (US) for amazon technologies, inc., Shane Michael McGookey of Seattle WA (US) for amazon technologies, inc., Tianze Zhang of Long Island City NY (US) for amazon technologies, inc.
IPC Code(s): G16H15/00, G06F40/40, G16H10/60
CPC Code(s): G16H15/00
Abstract: systems and methods for performing medical audio summarizing for medical conversations are disclosed. an audio file and meta data for a medical conversation are provided to a medical audio summarization system. a transcription machine learning model is used by the medical audio summarization system to generate a transcript and a natural language processing service of the medical audio summarization system is used to generate a summary of the transcript. the natural language processing service may include at least four machine learning models that identify medical entities in the transcript, identify speaker roles in the transcript, determine sections of the transcript corresponding to the summary, and extract or abstract phrases for the summary. the identified medical entities and speaker roles, determined sections, and extracted or abstracted phrases may then be used to generate the summary.
Inventor(s): Upendra Bhalchandra Shevade of Washington DC (US) for amazon technologies, inc., James Michael Lamanna of Woodinville WA (US) for amazon technologies, inc., Ethan Joseph Torretta of Edmonds WA (US) for amazon technologies, inc., Manish Gilani of Los Gatos CA (US) for amazon technologies, inc.
IPC Code(s): H04L45/44, H04L45/02, H04L45/50
CPC Code(s): H04L45/44
Abstract: in response to a programmatic request, configuration information representing a multi-network-segment gateway established on behalf of a customer is stored at a networking service. in response to another programmatic request, a communication session is established between a route signaling node of the gateway and a routing information source located at a customer premise. in response to additional programmatic input, the networking service stores an indication that the gateway is to be used to transfer packets between a cloud-side virtual network and a customer-side virtual network. the routing information exchanged in the session pertains to the cloud-side and customer-side virtual network, and is used to transfer data packets between the two virtual networks.
Inventor(s): Satya Naga Satis Kumar Gunuputi Alluri Venka of Sammamish WA (US) for amazon technologies, inc., John Baker of Bellevue WA (US) for amazon technologies, inc., Shahab Shekari of Seattle WA (US) for amazon technologies, inc., Kartik Natarajan of Shoreline WA (US) for amazon technologies, inc., Ruhaab Markas of The Colony TX (US) for amazon technologies, inc., Ganesh Kumar Gella of Redmond WA (US) for amazon technologies, inc., Santosh Kumar Ameti of Bellevue WA (US) for amazon technologies, inc.
IPC Code(s): H04L47/762
CPC Code(s): H04L47/762
Abstract: based on analysis of a workload associated with a throttling key of a client request directed to a first service, a scale-out requirement of the throttling key is obtained at respective resource managers of a plurality of other services which are utilized by the first service to respond to client requests. the resource managers initiate, asynchronously with respect to one another, resource provisioning tasks at each of the other services to fulfill the scale-out requirement. a throttling limit associated with the throttling key is updated to a second throttling key after the resource provisioning tasks are completed by the resource managers, and the updated limit is used to determine whether to accept another client request associated with the throttling key.
Inventor(s): Baihu Qian of Chicago IL (US) for amazon technologies, inc., Bashuman Deb of Aldie VA (US) for amazon technologies, inc., Justin Lin Hsieh of Chicago IL (US) for amazon technologies, inc., Daniel William Dacosta of Saint Paul MN (US) for amazon technologies, inc., Nick Matthews of Westminster CO (US) for amazon technologies, inc., Viktor Heorhiadi of Seattle WA (US) for amazon technologies, inc., Lalith Kumar Ramamoorthi of Brambleton VA (US) for amazon technologies, inc., Anoop Dawani of Redmond WA (US) for amazon technologies, inc., Omer Hashmi of Bethesda MD (US) for amazon technologies, inc., Thomas Nguyen Spendley of Rockville MD (US) for amazon technologies, inc.
IPC Code(s): H04L9/40, H04L12/46, H04L41/0893, H04L45/24, H04L47/20
CPC Code(s): H04L63/205
Abstract: systems and methods are provided for obtaining policy data associated with a private network implemented at least partly within a cloud provider network; establishing, based on the policy data, a first segment within the private network, wherein in a first geographic region of the cloud provider network, traffic associated with the first segment is isolated from traffic associated with a second segment of the private network, and wherein in a second geographic region of the cloud provider network, traffic associated with the first segment is isolated from traffic associated with a third segment of the private network; obtaining metadata indicating an isolated network of the cloud provider network is associated with the first segment; and enabling the isolated network to communicate, over the first segment, across the first geographic region and the second geographic region.
Inventor(s): Evan Gerald Statton of Denver CO (US) for amazon technologies, inc., Bryan Michael Samis of Freelton (CA) for amazon technologies, inc., Michael Henry of Bend OR (US) for amazon technologies, inc., Ryan Paul Hegar of Happy Valley OR (US) for amazon technologies, inc., Sydney Dean Lovely of Beaverton OR (US) for amazon technologies, inc.
IPC Code(s): H04L67/1008, H04L1/18, H04L67/1021
CPC Code(s): H04L67/1008
Abstract: systems and methods for providing a seamless automatic repeat request (arq) stream are provided. the system can include a plurality of streaming servers, a load balancer, and an arq streaming service. the arq streaming service obtains encoded content segments and transmits the segments to a plurality of streaming servers. the plurality of streaming servers is configured to transmit the encoded content segments to a client computing device, and a load balancer is implemented between the client computing device and the plurality of streaming servers. when the client computing device sends a response message to the arq streaming service while receiving the encoded content segments from one of the plurality of streaming servers, the arq streaming service may identify a failure in the streaming server. the arq streaming service retransmits lost encoded content segments by switching the streaming path to another streaming server from the plurality of streaming servers.
20240333802. VEHICLE SIGNAL RELAY SERVICE_simplified_abstract_(amazon technologies, inc.)
Inventor(s): Roland Mesde of Cupertino CA (US) for amazon technologies, inc., Alex Bessonov of San Jose CA (US) for amazon technologies, inc., Brian Ewanchuk of Redmond WA (US) for amazon technologies, inc., George Sherif Kamal Hanna of Toronto (CA) for amazon technologies, inc., Nitin Giri of Bothell WA (US) for amazon technologies, inc.
IPC Code(s): H04L67/125, H04L12/40
CPC Code(s): H04L67/125
Abstract: a vehicle signal relay system enables a relay agent in a first zone of a vehicle to send sensor signals having a first link-layer communication protocol to a software application deployed on a compute unit in another zone of the vehicle that is connected using another link-layer communication protocol. the vehicle signal relay system allows the software application to identify target relay agents with access to needed sensor signals. the vehicle signal relay system may further enable one way or mutual attestation. the vehicle signal relay system may also allow filters to be applied to the subscribed vehicle sensor signals, and may allow the software application to determine a communication protocol to be used between the software application and the relay agent.
Amazon Technologies, Inc. patent applications on October 3rd, 2024
- Amazon Technologies, Inc.
- G06F8/65
- CPC G06F8/65
- Amazon technologies, inc.
- G06F11/36
- B60R16/023
- CPC G06F11/3692
- G06F16/23
- G06F11/14
- G06F11/20
- CPC G06F16/2365
- G06F16/27
- CPC G06F16/275
- G06F21/40
- G06F21/55
- CPC G06F21/40
- G06N5/01
- G06N5/045
- CPC G06N5/013
- G06N10/20
- G06N3/092
- CPC G06N10/20
- G06N10/60
- G06N10/40
- CPC G06N10/60
- G06N10/80
- CPC G06N10/80
- G06Q30/0601
- CPC G06Q30/0631
- G06T7/12
- G06T7/50
- G06T7/70
- G06T7/90
- G06V10/25
- G06V10/56
- G06V10/762
- G06V10/82
- CPC G06T7/12
- G06T11/00
- G06V10/764
- CPC G06T11/00
- G10L15/18
- G06N3/08
- G10L15/06
- G10L15/16
- G10L15/22
- G10L15/28
- CPC G10L15/1815
- G16H15/00
- G06F40/40
- G16H10/60
- CPC G16H15/00
- H04L45/44
- H04L45/02
- H04L45/50
- CPC H04L45/44
- H04L47/762
- CPC H04L47/762
- H04L9/40
- H04L12/46
- H04L41/0893
- H04L45/24
- H04L47/20
- CPC H04L63/205
- H04L67/1008
- H04L1/18
- H04L67/1021
- CPC H04L67/1008
- H04L67/125
- H04L12/40
- CPC H04L67/125
