View source for Amazon Technologies, Inc. patent applications on 2025-06-19

=Patent Applications by Amazon Technologies, Inc. on June 19th, 2025=

[[:Category:Amazon Technologies, Inc.|Amazon Technologies, Inc.]]: 15 patent applications[[Category:Amazon Technologies, Inc.]]

Amazon Technologies, Inc. has applied for patents in the areas of [[:Category:CPC_C12Q1/6869|C12Q1/6869]] (Methods for sequencing, 1), [[:Category:CPC_G06F3/067|G06F3/067]] ({Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]}, 1), [[:Category:CPC_G06F9/547|G06F9/547]] (Interprogram communication, 1), [[:Category:CPC_G06F11/3688|G06F11/3688]] ({for test execution, e.g. scheduling of test suites}, 1), [[:Category:CPC_G06F16/31|G06F16/31]] (Indexing; Data structures therefor; Storage structures, 1), [[:Category:CPC_G06F21/6245|G06F21/6245]] ({Protecting personal data, e.g. for financial or medical purposes}, 1), [[:Category:CPC_G06F40/40|G06F40/40]] (ELECTRIC DIGITAL DATA PROCESSING  (computer systems based on specific computational models ), 1), [[:Category:CPC_G06N5/013|G06N5/013]] ({Automatic theorem proving}, 1), [[:Category:CPC_G06N10/40|G06N10/40]] (Physical realisations or architectures of quantum processors or components for manipulating qubits, e.g. qubit coupling or qubit control, 1), [[:Category:CPC_G06T17/00|G06T17/00]] (Three dimensional [3D] modelling, e.g. data description of 3D objects, 1)

With keywords such as: from, methods, biological, predicted, immunogenicity, samples, disclosed, herein, scoring, neoantigens in patent application abstracts.

Top Inventors:
* [[:Category:Haibao TANG of Cupertino CA US|Haibao TANG of Cupertino CA US]] (1 patents)
* [[:Category:Anta IMATA SAFO of Seattle WA US|Anta IMATA SAFO of Seattle WA US]] (1 patents)
* [[:Category:Alena HARLEY of Los Gatos CA US|Alena HARLEY of Los Gatos CA US]] (1 patents)
* [[:Category:Frank Wilhelm SCHMITZ of Seattle WA US|Frank Wilhelm SCHMITZ of Seattle WA US]] (1 patents)
* [[:Category:Antje HEIT of Seattle WA US|Antje HEIT of Seattle WA US]] (1 patents)

====Patent Applications by Amazon Technologies, Inc.====

[[20250197924. METHODS SELECTION COMBINATION SEQUENCING RESULTS BIOLOGICAL SAMPLES NEOANTIGEN SCORING (Amazon Technologies, .)]]

Abstract: disclosed herein are methods of scoring predicted immunogenicity of neoantigens from biological samples of a subject. methods can include the steps of preparing biological samples for nucleic acid sequencing; nucleic acid sequencing; evaluating the initial sequencing results by analyzing (e.g., comparing) sequencing parameters of the results; based on an analysis (e.g., a comparison) of sequencing parameters, combining the initial sequencing results to yield union sequencing results or selecting a representative biological sample; and scoring the predicted immunogenicity of neoantigens in the biological samples based on either the union sequencing results or the sequencing results of the representative sample. methods can further include the step of comparing sequencing parameters of union sequencing results and the initial sequencing results. methods can further include the steps of generating a neoantigen vaccine that contains or encodes for a neoantigen scored for predicted immunogenicity and administering the neoantigen vaccine to a subject.
[[Category:amazon technologies, inc.]]

[[20250199727. RESERVATION PERSISTENCE DISTRIBUTED BLOCK STORAGE SYSTEMS (Amazon Technologies, .)]]

Abstract: a storage object and an associated permissions record is stored at a storage server. the permissions record indicates that some storage consumers are not permitted to perform a type of i/o operation on the storage object. in response to detecting that an event of a deletion triggering type with respect to the records, a modified version of the permissions record is stored at the server, indicating that the storage consumers remain prohibited from performing the i/o operations. in response to receiving a command to perform a particular i/o at the server after the modified version has been stored, the modified version is used to process the command.
[[Category:amazon technologies, inc.]]

[[20250199893. MULTI-TENANT RADIO-BASED APPLICATION PIPELINE PROCESSING SYSTEM (Amazon Technologies, .)]]

Abstract: connectivity between a radio-based application pipeline processing server and a control plane of a provider network is verified. based on requests received at the control plane, a first isolated request handler, a second isolated request handler and an offloading manager are launched at the server. the offloading manager causes a first network function for which a request is received from the first request handler to be executed at a first network function accelerator of the server, and a second network function for which a request is received from the second request handler to be executed at a second network function accelerator of the server.
[[Category:amazon technologies, inc.]]

[[20250199944. SOFTWARE TESTING SERVICE AUTOMATED FAILURE REPRODUCTION ROOT CAUSE ANALYSIS (Amazon Technologies, .)]]

Abstract: a cloud-based software testing service orchestrates the testing of pieces of software, such as software to be deployed to a vehicle. also, the cloud-based software testing service, in response to detecting a failure, automatically instantiates multiple virtual machines configured to emulate a testing environment for testing one or more of the pieces of software, with which the detected failure is associated. these virtual machines allow for rapid execution of multiple instances of re-testing to be performed to determine a reproducibility measure for the failure. based on the reproducibility measure, additional re-testing may be performed. expanded testing logs generated during the re-testing are provided to a trained machine learning model that automatically determines, for reproducible failures, a root cause of the failure.
[[Category:amazon technologies, inc.]]

[[20250200080. DYNAMIC TEXT TOKENIZATION INDEX-BASED SEARCHING ANNOTATED DATA ASSETS USING KEYWORD-BASED TEXT SEARCHING (Amazon Technologies, .)]]

Abstract: devices, systems, and methods for tokenizing search attributes and terms of a search query for an index-based search. a method may include receiving, by a search service of a provider network, a first search query to search a first searchable document set, the first search query including a first search term in a first language; applying a first tokenization rule to identify the first search term in the first search query; determining that the first search term is in the first language; applying a second tokenization rule to tokenize the first search term based on the first search term being in the first language; causing a launch of a search instance by a managed compute service of the provider network, the search instance to execute a search function for a keyword-based text search using the tokenized first search term.
[[Category:amazon technologies, inc.]]

[[20250200212. GENERATING KEYWORDS PRODUCE SYNTHETIC DOCUMENTS WHILE MAINTAINING DATA PRIVACY (Amazon Technologies, .)]]

Abstract: a service may generate keywords to produce synthetic documents, while maintaining data privacy for the original documents. a client may extract keyword sequences from locally stored documents, embed the keyword sequences into vectors, and generate a dp-kde distribution based on the vectors. the dp-kde distribution preserves data privacy for the original documents. the service receives the dp-kde distribution, obtains a particular vector from the dp-kde (e.g., based on a calculated score for the dp-kde using random gaussian completions), decodes the particular vector into a sequence of synthetic keywords, and uses the sequence of synthetic keywords to prompt an llm to produce one or more synthetic documents.
[[Category:amazon technologies, inc.]]

[[20250200293. NATURAL LANGUAGE GENERATION (Amazon Technologies, .)]]

Abstract: techniques for using a model to generate a response to a user input, where the response is associated with a personality determined to be relevant to the user input, are described. the system receives a user input and context data associated with the user input. using the user input data and/or the context data, the system determines a personality (e.g., including a personality type and/or personality characteristics) relevant to the user input. the system generates a prompt instructing a model to generate a response to the user input that corresponds to the personality. the model processes the prompt to generate a response to the user input that corresponds to the personality. in some embodiments, the model generates a request for another component of the system to generate information responsive to the user input. the model may transform the responsive information into the personality-associated response.
[[Category:amazon technologies, inc.]]

[[20250200392. LARGE LANGUAGE MODEL VERIFICATION (Amazon Technologies, .)]]

Abstract: verifying large language model responses involves obtaining a query and its corresponding answer from a large language model. this conversational text is then fed into a second large language model, which translates the answer into first-order logic. the verification process uses an automated theorem prover. it checks the validity of this logic translation by determining the unsatisfiability of two scenarios: one where the negation of the logic translation and domain-specific logic formulas are combined, and another where the logic translation itself is combined with these formulas. based on this analysis, the theorem prover ascertains whether the translated answer is valid, invalid, or neither. the final step is communicating this verification status through an appropriate output medium, such as a graphical user interface, a database, or a report, providing a structured and methodical approach to assessing the accuracy and reliability of language model responses.
[[Category:amazon technologies, inc.]]

[[20250200410. PERFORMANCE READOUT RESET FLUXONIUM QUBITS (Amazon Technologies, .)]]

Abstract: techniques for performing readout and reset of fluxonium qubits are disclosed. when fluxonium hardware components are coupled to a quantum metamaterial through a readout resonator, said components may be dispersively coupled such that a quantum state of the corresponding fluxonium qubit is read out through the quantum metamaterial, and then the state of the fluxonium qubit is subsequently reset in order to proceed with a quantum computation to be performed. alternatively, when fluxonium hardware components are coupled directly to a quantum metamaterial, a quantum state of a fluxonium qubit is read out using resonance fluorescence, and then may be subsequently reset back to its ground state, also using resonance fluorescence. a width of a passband of the quantum metamaterial, along with frequencies of the control sequences used, may be tuned such that either readout or reset is selectively activated.
[[Category:amazon technologies, inc.]]

[[20250200873. TWO DIMENSIONAL IMAGE PROCESSING GENERATE THREE DIMENSIONAL MODEL DETERMINE TWO DIMENSIONAL PLAN (Amazon Technologies, .)]]

Abstract: techniques for two-dimensional (2d) image processing to generate a three-dimensional (3d) model and determine a 2d plan are described herein. in an example, a 3d model of a room can be generated by using a video file portion of a video file as a first input to a first machine learning (ml) model. semantic segmentation of the room can be generated by using the video file portion as a second input to a second ml model. the semantic segmentation may indicate that an object having an object type is shown in a first image frame of the video file portion. a 3d representation of the object in the 3d model can be determined. the 3d model can be corrected by setting a property of the 3d representation to a predefined value. a 2d floor plan of the room can be generated based on the corrected 3d model.
[[Category:amazon technologies, inc.]]

[[20250201230. SENDING MEDIA COMMENTS USING NATURAL LANGUAGE INTERFACE (Amazon Technologies, .)]]

Abstract: a system may provide a voice user interface (vui) for sending a media comment (e.g., brief clips of audio data representing speech) to a media content creator such as a podcaster, talk show, music app, etc. the system can identify a destination for the media comment based on context (e.g., an identifier corresponding to media content currently or recently output by a user device) and/or via voice dialog with the user. content creators can invite, receive, and play users' media comments on the show, thereby increasing audience engagement. a media comment may include a request for or dedication of a song, a “shout out” to another listener, a story/opinion, a question, a response to a poll, a contest entry, etc.
[[Category:amazon technologies, inc.]]

[[20250202573. SYSTEM SATELLITE DATA TRAFFIC SHAPING (Amazon Technologies, .)]]

Abstract: a constellation of satellites provides communication services to user terminals (uts). downstream data addressed to a ut is received at a point-of-presence (pop) and tokenized before sending to a satellite serving the ut. tokens are associated with resource blocks (rbs), each rb indicative of a particular combination of downlink frequency and timeslot. tokens are then allocated to downstream data. this tokenized downstream data is sent to the satellite. untokenized downstream data may be buffered for later tokenization or discarded. a satellite may use information in the token to schedule transmission on a downlink to the ut. the supply of tokens may be based on shaper input data such as gateway queue depth, estimated latency from the pop to the satellite, estimated time to empty a buffer onboard the satellite, and so forth. the supply of tokens may be adjusted to minimize data loss during handovers from one satellite to another.
[[Category:amazon technologies, inc.]]

[[20250202872. SECURITY PROTOCOL HANDSHAKE OFFLOADING (Amazon Technologies, .)]]

Abstract: contents of client-initiated handshake messages of a security protocol are obtained at a handshake processing offloader configured for an application. the offloader uses a first security artifact (which is inaccessible from a front-end request processor of the application) and the contents of the handshake messages to generate a second security artifact. the second security artifact is transmitted to the front-end request processor, which uses it to perform cryptographic operations for client-server interactions of the application.
[[Category:amazon technologies, inc.]]

[[20250202897. USER-DEFINED NETWORK CONNECTORS BETWEEN SERVERLESS FUNCTIONS ISOLATED CLOUD RESOURCES (Amazon Technologies, .)]]

Abstract: systems and methods are described for facilitating network traffic between serverless function executions and isolated cloud resources within virtualized network environments. virtualized network environments, by default, may be isolated such that external traffic is not permitted to enter the environment. permissions for traffic that may enter the environment are often set on the basis of network addresses. in the context of serverless functions, such permissions may be difficult to establish because executions of serverless functions can occur on a dynamically selected environment without a fixed network address. the present disclosure provides for creation of user-defined connectors that facilitate routing of network traffic between executions of serverless functions and user virtualized network environments without requiring that routing occur on the bases of network addresses.
[[Category:amazon technologies, inc.]]

[[20250202974. MANAGING REPLICATION COMPUTING NODES PROVIDED COMPUTER NETWORKS (Amazon Technologies, .)]]

Abstract: techniques are described for providing managed computer networks, such as for managed virtual computer networks overlaid on one or more other underlying computer networks. in some situations, the techniques include facilitating replication of a primary computing node that is actively participating in a managed computer network, such as by maintaining one or more other computing nodes in the managed computer network as replicas, and using such replica computing nodes in various manners. for example, a particular managed virtual computer network may span multiple broadcast domains of an underlying computer network, and a particular primary computing node and a corresponding remote replica computing node of the managed virtual computer network may be implemented in distinct broadcast domains of the underlying computer network, with the replica computing node being used to transparently replace the primary computing node in the virtual computer network if the primary computing node becomes unavailable.
[[Category:amazon technologies, inc.]]