UnfoldIPThemes

Company patents

Zuora, Inc.

Zuora, Inc. demonstrates a patent strategy heavily focused on core infrastructure, with Databases & Information Retrieval (49.2% of portfolio) and Business Methods & Fintech (46.0%) dominating, yet both show significant year-over-year fluctuations, including a -58.3% and -50.0% decline respectively so far in 2026. While Machine Learning & AI saw a 200.0% growth in 2024, its subsequent decline suggests a shifting priority away from rapid expansion in this area, contrasting with the strong rebound in Databases & Information Retrieval in 2025 (YoY +300.0%).

Patent Trend by Technology Area

Yearly patent publications since 2023

Product themes

Product-level themes inferred from filings since 2023, with category chips showing where each theme appears. Select a theme to filter the patents below.

63 US filings (since 2023) · 12 categories · 5 themes

Specialized Data Integration

Methods and systems for integrating, transforming, and managing complex or domain-specific data from disparate sources into a unified structure, often for specific applications like social networks, genomics, or business forms.

Databases & Information Retrieval
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35since 2023
+120.0%YoY
Access Control & Identity Management

Systems and methods for authenticating users, devices, or applications, authorizing their access to resources based on policies, and managing digital identities across various platforms.

Network Security & Access ControlWeb & Cloud Service ProtocolsComputer Security
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8since 2023
n/a
Automated Transaction Systems

Systems designed to streamline and automate various commercial transactions, including mobile-enhanced processes, secure online checkouts, customer service interactions, and privilege issuance, often leveraging digital authentication.

Business Methods & Fintech
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7since 2023
-50.0%YoY
Data Resiliency & Recovery

Encompasses strategies and technologies to ensure the availability, integrity, and recoverability of data and systems, including robust backup, replication, error correction, and efficient data restoration.

System Reliability & Diagnostics
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5since 2023
new
Document & Information Extractionfiltered

Methods and systems for identifying, extracting, and structuring specific entities, relationships, or insights from text-based documents, often involving techniques like named entity recognition, relation extraction, or summarization.

Natural Language Processing
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2since 2023
n/a

Patents

Showing 1-4 of 4

Document & Information Extraction
Page 1 of 1
US 12050871 B2GRANTED
G06F40/295

Dynamically trained models of named entity recognition over unstructured data

Filed:2021-08-03Pub:2024-07-30
Applicant:Zuora, Inc.

A computing server configured to process data of a domain from unstructured data sources to generate natural language phrases describing relationships between entities identified from the unstructured data. The computing server may receive master data schema and domain knowledge ontology of a domain including relationship definitions in the domain. The computing server may identify targeted types of named entities of the domain from the master data schema according to the relationship definitions in the domain knowledge ontology. The computing server may extract a plurality of named entities from unstructured data of the domain. The computing server may generate one or more sequences of named entities and assign entity labels to the named entities. The computing server may, based on the entity labels, generate natural language phrases describing relationships of sets of named entities.

US 12001469 B2GRANTED
G06F16/35

Generation of process models in domains with unstructured data

Filed:2021-08-04Pub:2024-06-04
Applicant:Zuora, Inc.

A computing server configured to process data of a domain from heterogeneous data sources. A domain may store data and schema, domain knowledge ontology such as resource description framework, and unstructured data. The computing server may extract objects from the unstructured data. The computing server may convert the extracted named entities and activities to word embeddings and input the word embeddings to a machine learning model to generate an activity time sequence. The machine learning model may be a long short-term memory. A process model may be generated from the time sequence. The computing server may identify outliers in the process model based on metrics defined by the domain. The computing server may convert transactions without outliers as word embeddings and generate signatures of the transactions using cosine similarity. The computing server may augment the results with the domain knowledge ontology.

US 20210390128 A1APPLICATION
G06F16/35

GENERATION OF PROCESS MODELS IN DOMAINS WITH UNSTRUCTURED DATA

Filed:2021-08-04Pub:2021-12-16
Applicant:Zuora, Inc.

A computing server configured to process data of a domain from heterogeneous data sources. A domain may store data and schema, domain knowledge ontology such as resource description framework, and unstructured data. The computing server may extract objects from the unstructured data. The computing server may convert the extracted named entities and activities to word embeddings and input the word embeddings to a machine learning model to generate an activity time sequence. The machine learning model may be a long short-term memory. A process model may be generated from the time sequence. The computing server may identify outliers in the process model based on metrics defined by the domain. The computing server may convert transactions without outliers as word embeddings and generate signatures of the transactions using cosine similarity. The computing server may augment the results with the domain knowledge ontology.

US 20210374348 A1APPLICATION
G06F40/295

DYNAMICALLY TRAINED MODELS OF NAMED ENTITY RECOGNITION OVER UNSTRUCTURED DATA

Filed:2021-08-03Pub:2021-12-02
Applicant:Zuora, Inc.

A computing server configured to process data of a domain from unstructured data sources to generate natural language phrases describing relationships between entities identified from the unstructured data. The computing server may receive master data schema and domain knowledge ontology of a domain including relationship definitions in the domain. The computing server may identify targeted types of named entities of the domain from the master data schema according to the relationship definitions in the domain knowledge ontology. The computing server may extract a plurality of named entities from unstructured data of the domain. The computing server may generate one or more sequences of named entities and assign entity labels to the named entities. The computing server may, based on the entity labels, generate natural language phrases describing relationships of sets of named entities.

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