How Amazon's Patent Portfolio Secures §101 Eligibility by Patenting Infrastructure, Not Abstractions

A client-facing thought-leadership article by anovIP

Executive Summary

In the post-Alice patent landscape, many software and AI patents fail not because they lack innovation, but because they are framed as abstract ideas divorced from technical implementation. A close review of recently published patent applications assigned to Amazon (late-2025 to January-2026) shows a markedly different approach.

Amazon consistently designs and claims inventions as infrastructure-level systems—rooted in control planes, storage engines, networking primitives, cryptographic boundaries, and orchestration logic. As a result, Amazon's portfolio is structurally resilient under 35 U.S.C. §101, even when it touches AI, data analytics, or emerging technologies like quantum computing.

This article applies the Alice/Mayo two-step framework to Amazon's patent categories, illustrating why these claims are more likely to survive eligibility challenges.

1. Control Planes & Client-Directed Service Placement

(Where workloads run, and who controls the decision)

Representative publication

[US 20260032168] – Client-Directed Placement of Remotely-Configured Service Instances

Publication Number: US20260032168A1

Publication Date: January 29, 2026

Applicant: Amazon Technologies, Inc.

Abstract: Methods and apparatus for client-directed placement of remotely configured service instances are described. One or more placement target options are selected for a client of a network-accessible service based on criteria such as service characteristics of the placement targets. The selected options, including a particular placement target that includes instance hosts configurable from remote control servers, are indicated programmatically to the client. A determination is made that a service instance is to be configured at the particular placement target on behalf of the client. A remote control server is configured to issue administrative commands to an instance host at the particular placement target to configure the service instance.

First Indepedendent Claim: A system, comprising: one or more computing devices configured to:

indicate, via one or more programmatic interfaces, a plurality of placement target options to a client of a network-accessible service, including a first placement target and a second placement target, wherein the first placement target comprises a set of instance hosts configurable to receive administrative commands to configure service instances from a control server located at the second placement target, and wherein the first placement target differs from the second placement target in an expected response latency range for one or more types of operations;

receive, via a particular programmatic interface of the one or more programmatic interfaces, an indication that the client has selected the first placement target to configure one or more service instances; select a particular control server located at the second placement target to coordinate configuration of the one or more service instances;

select a particular instance host of the first placement target on which to configure a particular service instance of the one or more service instances; establish a secure communication channel between the particular control server and the particular instance host; and

in response to a configuration request for the particular service instance, transmit, from the selected control server, one or more administrative commands to the particular instance host to configure the particular service instance.

Section 101 Analysis
a) Alice Step 1 – Abstract idea?

Even if framed as "resource allocation," the claims recite specific control-plane mechanisms (remote configuration servers, placement criteria evaluation, instance hosts).

Why it survives

Amazon frames placement as a technical orchestration problem, not a business decision.

anovIP insight: This category is low-risk under §101 because it improves how distributed systems are provisioned, a core computer-technology function.

2. Experiment Management & MLOps Infrastructure

(Patenting experimentation systems, not experiments)

Representative publications

[US 20260030323] – Experiment Management Service

Publication Number: US20260030323A1

Publication Date: January 29, 2026

Applicant: Amazon Technologies, Inc.

Abstract: Artifacts, including parameters are data sets, associated with experiment tasks are stored at an experiment management service. A query specifying a particular value of a parameter and a particular data set is received, and an indication of an experiment result associated with the particular data set and the particular parameter value is provided.

First Indepedendent Claim: A system, comprising:

one or more computing devices;

wherein the one or more computing devices include instructions that upon execution on or across one or more computing devices cause the one or more computing devices to: store, in response to one or more requests obtained via one or more programmatic interfaces of an experiment management service, metadata indicating relationships of a group of machine learning tasks, wherein the group comprises a first experiment which includes one or more trials using respective versions of one or more machine learning models, wherein at least one machine learning task of the group is performed using resources of at least a first storage service of a provider network, and wherein at least one machine learning task of the group is performed using resources of at least a first computing service of the provider network; store, in one or more repositories, respective records of artifacts associated with individual ones of the machine learning tasks of the group, including indications of (a) the respective versions of the machine learning models, (b) results obtained using the respective versions, (c) hyper-parameters associated with the respective versions, (d) data sets used to train the respective versions and (e) names of provider network services used for the machine learning tasks;

receive a query via the one or more programmatic interfaces, wherein one or more parameters of the query indicate at least (a) a value of a hyper-parameter, (b) a particular provider network service and (c) an identifier of a first data set; and

in response to the query, provide an indication via the one or more programmatic interfaces of a change in model results achieved between a first and a second version of a machine learning model utilized in the group of machine learning tasks, wherein the value of the hyper-parameter was used to train at least one version of the first or second versions using the first data set, and wherein the particular provider network service was used for at least one version of the first and second versions.

[US 20250328765] – Resource-Efficient Techniques for Repeated Hyper-Parameter Optimization

Publication Number: US20250328765A1

Publication Date: October 23, 2025

Applicant: Amazon Technologies, Inc.

Abstract: A particular hyper-parameter combination (HPC) that was recommended for a first task is included in a collection of candidate HPCs evaluated for a second task. Hyper-parameter analysis iterations are conducted for the second task using the collection. In one of the iterations, the second task is executed using a first iteration-specific set of HPCs, including the particular HPC and one or more other members of the collection. One or more of the HPCs of the first iteration-specific set of HPCs are pruned to generate a second iteration-specific set of HPCs for a subsequent iteration. HPCs are selected for pruning based on a comparison of their results with the results obtained from the particular HPC that was recommended for the first task. A recommended HPC for the second task is identified based on results of the analysis iterations.

First Indepedendent Claim: A computer-implemented method, comprising:

obtaining, via one or more programmatic interfaces at a cloud computing environment, one or more messages indicating that a first set of values of at least a first hyper-parameter for one or more tasks for which iterative hyper-parameter optimization is to be performed at the cloud computing environment is to be selected from a first search space using a non-uniform selection technique;

identifying, at the cloud computing environment in response to the one or more messages, a first hyper-parameter combination for executing a first task of the one or more tasks, wherein the first hyper-parameter combination comprises a first value of the first hyper-parameter and a second value of a second hyper-parameter of the first task, wherein the first value is selected from the first search space using the non-uniform selection technique; and

performing, at the cloud computing environment during a particular iteration of the iterative hyper-parameter optimization, the first task using the first hyper-parameter combination.

Section 101 Analysis

- Step 1: While experimentation and optimization can sound abstract, Amazon's claims are tied to concrete services that store artifacts, manage parameters, and reuse optimization results.

-Step 2: The inventive concept lies in how experiments are structured, queried, and reused, reducing computational overhead.

Why it survives

Amazon avoids claiming "optimizing a model" in the abstract. Instead, it claims systems that manage experiment metadata, parameters, and execution cycles.

anovIP insight: Amazon patents MLOps primitives, which courts increasingly recognize as technical improvements to computing efficiency.
   

3. Orchestration Agents with Long-Term Memory

(Persistent system intelligence, not conversational AI)

Representative publication

[US 20260017487] – Generating Long-Term Memory for Orchestration Agent Sessions

Publication Number: US20260017487A1

Publication Date: January 15, 2026

Applicant: Amazon Technologies, Inc.

Abstract: Long-term memory data objects may be generated for orchestrations agents. When a session completes or ends, a long-term memory data object may be generated according to a specified long-term memory type based on turn inputs during the session. When a new session is started, the long-term memory data object may be used as part of inputs to a generative machine learning model to perform or respond to turn inputs of the new session.

First Indepedendent Claim: A system, comprising:

a plurality of computing devices, respective comprising at least one processor and a memory, configured to implement at natural language generative service of a provider network, comprising: one or more orchestration agents, created and deployed via one or more requests received via an interface of the natural language generative service, wherein the one or more orchestration agents provide access for an application to a foundation model, wherein the foundation model is a large language model (LLM) trained to generate natural language;

wherein the one or more orchestration agents are configured to: receive one or more text inputs in one or more turns in a first chat session between the application and the LLM;

upon completion of the first chat session, generate a long-term memory data object according to a long-term memory type specified for the application via the interface of the natural language generative service based, at least in part, on one or more turn inputs of the first session provided to the generative machine learning model via the orchestration agent;

store the long-term memory data object to a data store accessible to the one or more orchestration agents;

upon initiation of a second chat session of the application: identify the long-term memory data object as associated with the second session of the application;

obtain the long-term memory data object from the data store; and provide the long-term memory data object as part of one or more inputs to the generative machine learning model received via the second session of the application.

Section 101 Analysis

a) Step 1: Not an abstract mental process. Humans cannot generate, classify, and reinject long-term memory objects across sessions at cloud scale.

b) Step 2: Claims specify memory object generation, memory types, session boundaries, and generative ML integration.

Why it survives

The invention improves automation systems and orchestration engines, not human reasoning.

anovIP insight: Amazon positions "AI agents" as stateful infrastructure components, dramatically strengthening §101 defensibility.

4. Distributed Storage Internals (Record-Aware Systems)

(Concurrency, recovery, and correctness at scale)

Representative publications

[US 20260003838] – Scalable Garbage Collection for Distributed Storage System for Database Management Applications

Publication Number: US20260003838A1

Publication Date: January 1, 2026

Applicant: Amazon Technologies, Inc.

Abstract: Scalable garbage collections is performed by a distributed storage system. Garbage collection events are detected by a distributed storage system for different portions of a table. Garbage collection is performed for individual ones of the different portions of the table responsive to detecting the garbage collection events, including identifying one or more versions of a record to reclaim from the different portions of the table based on transaction status information and reclaiming the one or more versions of the record.

First Indepedendent Claim: A system, comprising:

a first plurality of nodes, respectively comprising at least one processor and a memory, that implement a database service that provides access to databases on behalf of clients of the database service;

a second plurality of nodes, respectively comprising at least one further processor and a further memory, that implement a distributed storage service that stores tables of the databases on behalf of the database service;

wherein the database service comprises a data access node, configured to: perform a plurality of client requests to update a table stored in the distributed storage service;

wherein the distributed storage service is configured to: detect respective garbage collection events for a plurality of portions of the table, wherein a first one of the respective garbage collection events for a first one of the plurality of portions is detected independent of a second one of the respective garbage collection events for a second one of the plurality of portions;

perform garbage collection for individual ones of the plurality of portions of the table responsive to the detection of the respective garbage collection events, wherein to perform the garbage collection, the distributed storage service is configured to: evaluate transaction status information corresponding to the one portion of the table to identify one or more versions of a record to reclaim from one of the plurality of portions of the table for storing additional data in the table; and reclaim the one or more versions of the record.

[US 20260003856] – Record-Level Transaction Conflict Detection at Storage Nodes of a Record Aware Distributed Data Storage System

Publication Number: US20260003856A1

Publication Date: January 1, 2026

Applicant: Amazon Technologies, Inc.

Abstract: Record-level transaction conflicts may be detected at storage nodes of a database back-end storage service. When a prepare statement is received, a storage node can determine whether another prepare statement acting on at least one of the same records has succeeded. If so, then the storage node can indicate to the database access application that there is a conflict, allowing the database access application to wait to try again or abort the transaction.

First Indepedendent Claim: A system, comprising:

a first plurality of nodes, respectively comprising at least one processor and a memory, that implement a database service that provides access to databases on behalf of clients of the database service;

a second plurality of nodes, respectively comprising at least one further processor and a further memory, that implement a distributed storage service that stores tables of the databases on behalf of the database service;

wherein the database service comprises a first data access node and a second data access node, wherein the first data access node sends one or more write requests as part of a first transaction to a storage node of the distributed storage service via a first storage service engine, and wherein the second data access node sends one or more further write requests as part of a second transaction to the storage node via a second storage service engine;

wherein the storage node is configured to: receive a request to prepare the first transaction from the first data access node; identify a first version of a record associated with the first transaction and a first transaction time associated with the first version of the record;

identify a rowblock that has a record identifier range that includes the record and a time value range that includes a first transaction time; evaluate transaction status information maintained at the storage node and the rowblock to determine that the first transaction conflicts with the second transaction with an acknowledged prepare request for a second version of the record with a second transaction time that is also stored in the rowblock; and

send a response indicating that the request to prepare the first transaction conflicts with the second transaction.

[US 20260003987] – Rowblock Modifications for Record-Aware Distributed Storage Systems

Publication Number: US20260003987A1

Publication Date: January 1, 2026

Applicant: Amazon Technologies, Inc.

Abstract: Rowblock modifications may be performed at record-aware distributed storage systems. Record-aware distributed storage systems may implement a storage service engine that can evaluate access requests to a table and make rowblock modification determinations based on the evaluation. The storage service engine can instruct storage nodes of the record-aware distributed storage system to perform the rowblock modifications.

First Indepedendent Claim: A system, comprising:

a first plurality of nodes, respectively comprising at least one processor and a memory, that implement a database service that provides access to databases on behalf of clients of the database service;

a second plurality of nodes, respectively comprising at least one further processor and a further memory, that implement a distributed storage service that stores tables of the databases on behalf of the database service;

wherein the database service comprises at least one data access node that is configured to send a plurality of access requests to a storage service engine for the distributed storage service to access at least a portion of a table stored at one or more storage nodes of the distributed storage service;

wherein the storage service engine is configured to: evaluate the plurality of access requests according to different rowblock store adaptation criteria to determine a rowblock store modification that changes a location of a current or future version of one or more records in the portion of the table in the rowblock store; instruct the one or more storage nodes to perform the rowblock store modification at the rowblock store at the one or more storage nodes; and

update a rowblock map at the storage service engine to reflect the rowblock storage modification, wherein one or more subsequent access requests received from the at least one data access node are performed using the updated rowblock map.

[US 20260003745] - Scalable Recovery for Record-Aware Distributed Storage Systems

Publication Number: US20260003745A1

Publication Date: January 1, 2026

Applicant: Amazon Technologies, Inc.

Abstract: Scalable recovery of a database is performed by a distributed data store. Storage nodes can monitor connection status of database access applications that have attached respective portions of a table stored at the storage nodes to provide access to the table. A connection failure of a database access application can be detected. Transactions associated with the failed database access application can be identified and determination of transactions that are not committed made. The storage nodes can remove versions of records of the table associated with the transaction as part of recovering the respective portions of the table from the connection failure of the database access application.

First Indepedendent Claim: A system, comprising:

a first plurality of nodes, respectively comprising at least one processor and a memory, that implement a database service that provides access to databases on behalf of clients of the database service;

a second plurality of nodes, respectively comprising at least one further processor and a further memory, that implement a distributed storage service that stores tables of the databases on behalf of the database service; wherein the database service comprises one or more data access nodes that have attached respective portions of a table stored at the one or more storage nodes of the distributed storage service to provide access to a table;

wherein the one or more storage nodes are configured to: monitor connection status of the one or more database access nodes with the one or more storage nodes;

based on the monitoring, detect a connection failure of one of the one or more data access nodes;

recover the respective portions of the table at the one or more storage nodes, wherein to perform the recovery, the one or more storage nodes are configured to: evaluate transaction status information maintained at the one or more storage nodes corresponding to the respective portions of the table to identify one or more transactions associated with the one data access node;

determine that a transaction of the one or more transactions is not committed; and

remove one or more versions of one or more records of the table associated with the transaction.

[US 20260003993] – Record Request Performance of a Record-Aware Distributed Storage System

Publication Number: US20260003993A1

Publication Date: January 1, 2026

Applicant: Amazon Technologies, Inc.

Abstract:Requests for records are performed at a record-aware distributed storage system. A request at a storage service engine for records received from a database access application has a time value identified corresponding to a state of a table and storage nodes that store the records. Requests are sent to the storage nodes to obtain the records. The storage nodes may identify rowblocks with respective record identifier ranges and time value ranges corresponding to the records and the time value for the access request. A result may be returned to the database access application based on the records received from the storage nodes.

First Indepedendent Claim:A system, comprising:

a first plurality of nodes, respectively comprising at least one processor and a memory, that implement a database service that provides access to databases on behalf of clients of the database service;

a second plurality of nodes, respectively comprising at least one further processor and a further memory, that implement a distributed storage service that stores tables of the databases on behalf of the database service;

wherein the database service comprises a data access node that implements a query engine for the database service and a storage service engine for the distributed storage service, wherein the storage service engine is configured to:

receive a request from the query engine to obtain one or more records of a table to perform an access request at the query engine;

identify a time value for the access request corresponding to a state of the table and one or more storage nodes of the distributed storage service that store the one or more records;

send respective requests to the one or more storage nodes to obtain the one or more records according to the time value for the access request; and

wherein the one or more storage nodes are configured to:

determine one or more rowblocks with respective record identifier ranges and time value ranges that include respective record identifiers for the one or more records and the time value;

access one or more rowblock stores to retrieve the one or more records; and

send the one or more records to the storage service engine; and

wherein the storage service engine is further configured to:

return a response, to the request to obtain the one or more records, to the query engine based, at least in part, on the one or more records received from the one or more storage nodes.

Section 101 Analysis

a) Step 1: These claims are squarely directed to database and storage system operations.

b) Step 2: They recite detailed mechanisms for garbage collection, conflict detection, recovery, and performance optimization.

Why it survives

Courts consistently uphold patents that improve database internals and distributed system behavior.

anovIP insight: This is one of Amazon's strongest §101 categories—the claims operate at the heart of computer architecture.

5. Security, Encryption & Cross-Region Replication

(Trust as a system property)

Representative publication

[US 20260003977] – Independent Encryption for Cross-Region Data Set Replication

Publication Number: US20260003977A1

Publication Date: January 1, 2026

Applicant: Amazon Technologies, Inc.

Abstract: A write to a data set may be replicated across regions using independent encryption. Replicated writes are encrypted using payload keys specific to regions. Payload keys can be encrypted and be shared between regions using respective hierarchies of keys for each region, including shared public keys of public-private key pairs.

First Indepedendent Claim: A system, comprising:

a plurality of computing devices, respectively comprising at least one processor and a memory, that implement a database service of a provider network;

wherein the database service is configured to: perform a write to a table replicated across different regions of the provider network, the write being received at a first region of the different regions, and the table being writeable via requests received at individual ones of the different regions;

replicate the write to the table to the different regions of the provider network, wherein to replicate the write the database service is configured to: perform the write on an item of the table in the first region;

encrypt, in the first region, the item of the table with a payload key for the first region; append the encrypted item of the table to a multi-region append-only log;

decrypt, in a second region of the different regions, the encrypted item obtained from the multi-region append-only log, using the payload key for the first region, wherein the payload key for the first region was decrypted in the second region using a private key of a public-private key pair for the second region to decrypt the payload key, the payload key having been previously encrypted using a public key of the public-private key pair at the first region; and

update the table in the second region using the decrypted item.

Section 101 Analysis

a) Step 1: Cryptographic replication across regions is not abstract—it involves concrete encryption hierarchies and key management.

b) Step 2: Independent payload keys and region-specific encryption provide a technical security improvement.

Why it survives

Security and cryptography patents that define how data is protected through independent encryption hierarchies and asymmetric key exchange for cross-region replication are routinely found eligible.

anovIP insight: Amazon patents cryptographic architecture, not compliance rules—making claims both technical and durable.

6. Networking, Timing & Memory Access

(Below the application layer)

Representative publications

[US 20260025304] – Techniques for accessing local networks via a virtualized gateway

Publication Number: US20260025304A1

Publication Date: January 22, 2026

Applicant: Amazon Technologies, Inc.

Abstract: Disclosed are various embodiments for receiving, via a network, a request from a client to establish a network tunnel over the network. Various embodiments can create a virtual network comprising a virtual network gateway in response to receiving a service call. Various embodiments can further allocate an available computing resource to the virtual network gateway to augment a first computing resource. Allocating the available computing resource can be performed in response to a usage of the first computing resource assigned to the virtual network gateway.

First Indepedendent Claim: A system, comprising:

a computing device comprising a processor and a memory; and

machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: create, in response to receiving a service call, a virtual network comprising a virtual network gateway; create an encrypted network tunnel associated with the virtual network; and

allocate, in response to a usage of a first computing resource assigned to the virtual network gateway, an available second computing resource to the virtual network gateway to augment the first computing resource, wherein the second computing resource is assigned to the encrypted network tunnel.

[US 20250348455] – Flexible remote direct memory access

Publication Number: US20250348455A1

Publication Date: November 13, 2025

Applicant: Amazon Technologies, Inc.

Abstract: Apparatus and methods are disclosed herein for remote, direct memory access (RDMA) technology that enables direct memory access from one host computer memory to another host computer memory over a physical or virtual computer network according to a number of different RDMA protocols. In one example, a method includes receiving remote direct memory access (RDMA) packets via a network adapter, deriving a protocol index identifying an RDMA protocol used to encode data for an RDMA transaction associated with the RDMA packets, applying the protocol index to a generate RDMA commands from header information in at least one of the received RDMA packets, and performing an RDMA operation using the RDMA commands.

First Indepedendent Claim: An apparatus for processing remote direct memory access (RDMA) packets encoded in one of a plurality of RDMA protocols, the apparatus comprising: a host coupled to memory;

a network adapter configured to transmit and receive RDMA packets via a network connection; and

an RDMA controller coupled to the memory and the network adapter, the controller configured to: produce a protocol index for the received RDMA packets, the protocol index indicating an RDMA protocol in which the received RDMA packets are encoded, based on reading a memory using the protocol index, produce field extraction control information for extracting fields from a header of one of the received RDMA packets, and based on the produced field extraction control information, performing a generic RDMA operation with the memory based on the control information.

[US 20250385744] – Transmitting Clock Signals in Network Packets

Publication Number: US20250385744A1

Publication Date: December 18, 2025

Applicant: Amazon Technologies, Inc.

Abstract: A clock signal in a clock distribution network is transmitted using network packets with the clock signal embedded within a bit of the network packets. The network packets can include a preamble used to create phase alignment between a timing pulse and a bit position of a Start of Frame Delimiter (SFD) within the packet. The clock signal associated with the packet occurs when the SFD is detected. In one example, the SFD is detected when two consecutive bits of equal value are received and the timing of the clock signal is such that the clock signal occurs when the second consecutive bit is received. By including a clock signal within a packet, additional information can be transmitted with the clock signal. For example, authentication and validation information can be included, a time stamp, a message type, a frame check sequence, a clock status, etc.

First Indepedendent Claim: An Integrated Circuit (IC), comprising:

a plurality of timing ports for receiving clock input signals from different sources, for receiving packets associated with the clock input signals, and for inserting metadata into the packets;

and a plurality of distribution ports, each coupled to all of the plurality of timing ports, the plurality of distribution ports each including a register for selecting which of the clock input signals from the plurality of timing ports passes to outputs of the IC.

Section 101 Analysis

a) Step 1: These inventions are directed to network hardware–software interaction.

b) Step 2: Claims recite packet structures, gateways, adapters, timing alignment, and protocol handling.

Why it survives

These patents operate at the physical and protocol layer, far removed from abstract ideas.

anovIP insight: Latency, timing, and memory semantics are among the most §101-secure domains in software patents.

7. Quantum Computing as an Abstraction Layer

(Compatibility, not algorithms)

Representative publication

[US 20260024029] – Quantum Computing Task Translation Supporting Multiple Quantum Computing Technologies

Publication Number: US20260024029A1

Publication Date: January 22, 2026

Applicant: Amazon Technologies, Inc.

Abstract: A quantum computing service provides a quantum algorithm development kit that enables a customer to define a quantum task, a quantum algorithm, or a quantum circuit using an intermediate representation. The quantum computing service is then configured to automatically translate the quantum task, quantum algorithm, or quantum circuit into a specific representation specific to a particular quantum computing technology selected by the customer to be used to execute the customer's quantum task, quantum algorithm, or quantum circuit.

First Indepedendent Claim: A system comprising:

one or more computing devices configured to implement a quantum computing service configured to: receive a quantum computing object defined in an intermediate representation;

translate the quantum computing object into a selected quantum circuit format for a particular quantum computing technology, wherein the selected quantum circuit format for the particular quantum computing technology is one of a plurality of quantum circuit formats for a plurality of different quantum computing technologies supported by the quantum computing service, wherein to translate the quantum computing object into the selected quantum circuit format, the one or more computing devices are configured to: identify portions of the quantum computing object corresponding to quantum operators in the intermediate representation; substitute the quantum operators of the intermediate representation with quantum operators of the quantum circuit format of the particular quantum computing technology; and perform one or more optimizations to reduce an overall number of quantum operators in a translated quantum circuit that is a translated version of the received quantum computing object;

provide the translated quantum circuit for execution at a quantum hardware provider that uses the particular quantum computing technology; receive, from the quantum hardware provider, results of the execution of the translated quantum circuit; and

provide a notification to a customer of the quantum computing service that the quantum computing object has been executed.

Section 101 Analysis

a) Step 1: The claims are not about mathematical algorithms in the abstract, but about translation systems within a quantum computing service.

b) Step 2: The inventive concept lies in intermediate representations and automated translation pipelines.

Why it survives

Amazon frames quantum computing as a systems-integration problem, not a theoretical exercise.

anovIP insight: This mirrors Amazon's cloud playbook: patent the abstraction layer, not the underlying science.

Portfolio-Level Section 101 Assessment

| Patent Category | §101 Risk | Rationale |

|----------------|-----------|-----------|

| Service placement & control planes | Low | Core distributed-system orchestration |

| Experiment management / MLOps | Low–Medium | Strong when framed as infrastructure |

| Orchestration agents & memory | Low | Stateful automation systems |

| Distributed storage internals | Very Low | Database architecture improvements |

| Encryption & replication | Low | Cryptographic system design |

| Networking & RDMA | Very Low | Protocol-level engineering |

| Quantum task translation | Low–Medium | Systems abstraction, not math |

Conclusion: Why Amazon's Patents Endure Under §101

Amazon does not ask the patent system to protect ideas.

It asks the patent system to protect machines, networks, storage engines, and orchestration frameworks that work better because of the invention.

Applications may raise §101 risk. Infrastructure dramatically lowers it.

By consistently anchoring claims in technical environments and system behavior, Amazon has built one of the most §101-resilient software patent portfolios in the world.

anovIP Perspective

At anovIP, we help clients replicate this strategy:

1. Identify system-level primitives in their architecture

2. Draft claims around technical effect and control planes

3. Future-proof AI and software patents against eligibility challenges

Amazon's portfolio is not just a case study—it is a blueprint for Section 101-ready innovation.

How anovIP Can Assist

As cloud, AI, and infrastructure patents face heightened scrutiny under 35 U.S.C. §101, success increasingly depends on how innovations are framed, structured, and claimed—not merely on their technical novelty. anovIP assists technology companies, cloud providers, and deep-tech innovators in adopting a system-first patent strategy similar to Amazon's.

anovIP works closely with engineering and R&D teams to identify infrastructure-level primitives—control planes, data planes, orchestration layers, storage internals, security boundaries, and abstraction frameworks—and translates them into Section 101-resilient patent claims. Our support spans invention harvesting, claim architecture design, eligibility risk assessment, prosecution strategy, and litigation-readiness reviews, ensuring that patents emphasize technical effect, system behavior, and measurable performance improvements.

For clients operating in AI, cloud computing, distributed systems, and emerging technologies, anovIP helps build long-lived IP portfolios that protect platforms rather than features—creating defensible assets that scale across products, customers, and decades.