Company patents

FotoNation Limited

FotoNation Limited's patent strategy shows a surprising and significant decline across nearly all categories, with a 100% year-over-year drop in 2025 for core areas like Computer Vision (52.7% of portfolio), Pictorial / Video Communications (47.3% of portfolio), and Machine Learning & AI (21.8% of portfolio). This widespread reduction, including a 61.1% decline in Image Processing patents from 2023 to 2024, suggests a dramatic shift away from active patenting in its traditional technology domains.

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.

55 US filings (since 2023) · 12 categories · 15 themes

Video Enhancement & Object Tracking

Methods and systems for improving the quality of video streams, generating intermediate frames, or continuously locating and following objects within a sequence of images, even under occlusion.

Image Processing
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15since 2023
n/a
Video Quality & Encoding Optimization

Methods and apparatus for improving the visual fidelity, resolution, or compression efficiency of video signals, often through advanced processing, up-scaling, or neural network-based filters.

Pictorial / Video CommunicationsComputer Vision
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14since 2023
n/a
Multi-Sensor Imaging & Synthesis

Systems that combine data from multiple camera sensors or capture multiple images from different perspectives or qualities, often involving image processing techniques like synthesis to create enhanced or comprehensive views.

Pictorial / Video Communications
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11since 2023
n/a
ADAS Perception & Display

Systems utilizing various sensors (e.g., cameras, radar, sonar) to perceive the vehicle's surrounding environment, detect objects, and process/display relevant information to the driver for enhanced awareness, assistance in maneuvers, or safety.

Vehicle Body Fittings
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9since 2023
-50.0%YoY
Efficient Visual Feature Extraction

Algorithms and hardware optimizations for rapidly identifying and characterizing relevant visual features (e.g., objects, motion, gradients) from images or video streams, often integrating machine learning for feature representation and recognition, with a focus on real-time performance and reduced computational cost.

Pattern Recognition & ML Models
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9since 2023
n/a
Multi-modal Sensor Fusion

Techniques for combining data from disparate sensor types (e.g., cameras, radar, mobile device signals) to achieve a more robust and comprehensive understanding of an environment or subject, often leveraging machine learning for interpretation and correlation.

Computer VisionPattern Recognition & ML Models
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8since 2023
-66.7%YoY
Specialized Neural Network Architectures

Development and optimization of novel neural network layers or architectures specifically designed to improve performance or efficiency for computer vision tasks.

Computer Vision
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6since 2023
n/a
Vision-Based Object & Pose Estimationfiltered

Methods and apparatus for detecting objects and determining their three-dimensional position and orientation (pose) using imagery or point cloud data, often for navigation, surveying, or environmental understanding.

Computer Vision
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5since 2023
n/a
Multimodal Data Fusion

Techniques for combining and analyzing information from multiple distinct data modalities (e.g., text, image, video, audio, sensor data) to derive richer insights or improve system performance and decision-making.

Machine Learning & AI
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3since 2023
n/a
Sensor-based Environment Perception

Techniques and hardware for autonomous systems to gather and interpret data about their surroundings, including obstacle detection, object recognition, and depth estimation, to inform control decisions.

Computer Vision
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2since 2023
n/a
Generative AI for Images

Techniques utilizing deep learning models like Generative Adversarial Networks (GANs) or diffusion models to create new images, modify existing ones, or generate synthetic data based on various inputs or conditions.

Computer Vision
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2since 2023
n/a
Automated Visual Inspection

Systems that employ imaging and image processing to automatically detect defects, verify states, or ensure quality control in manufactured goods, printed materials, or industrial processes.

Pictorial / Video Communications
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2since 2023
n/a
Real-time Anomaly Detection

Methods and systems that identify unusual or suspicious patterns in data streams, often leveraging machine learning models trained on normal behavior, to detect threats, faults, or significant events as they occur.

Pattern Recognition & ML Models
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2since 2023
n/a
Large Model Text Generation

Techniques for generating human-like text or other content using large pre-trained models, often involving prompt engineering, speculative decoding, or multi-modal inputs for content creation.

Machine Learning & AI
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1since 2023
n/a
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.

Computer Security
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1since 2023
n/a

Patents

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US 20210063141 A1APPLICATION
G01B11/22

Systems and Methods for Estimating Depth from Projected Texture using Camera Arrays

Filed:2020-09-07Pub:2021-03-04
Applicant:FotoNation Limited

Systems and methods in accordance with embodiments of the invention estimate depth from projected texture using camera arrays. One embodiment of the invention includes: at least one two-dimensional array of cameras comprising a plurality of cameras; an illumination system configured to illuminate a scene with a projected texture; a processor; and memory containing an image processing pipeline application and an illumination system controller application. In addition, the illumination system controller application directs the processor to control the illumination system to illuminate a scene with a projected texture. Furthermore, the image processing pipeline application directs the processor to: utilize the illumination system controller application to control the illumination system to illuminate a scene with a projected texture capture a set of images of the scene illuminated with the projected texture; determining depth estimates for pixel locations in an image from a reference viewpoint using at least a subset of the set of images. Also, generating a depth estimate for a given pixel location in the image from the reference viewpoint includes: identifying pixels in the at least a subset of the set of images that correspond to the given pixel location in the image from the reference viewpoint based upon expected disparity at a plurality of depths along a plurality of epipolar lines aligned at different angles; comparing the similarity of the corresponding pixels identified at each of the plurality of depths; and selecting the depth from the plurality of depths at which the identified corresponding pixels have the highest degree of similarity as a depth estimate for the given pixel location in the image from the reference viewpoint.

US 20210034864 A1APPLICATION
G06K9/00

IRIS LIVENESS DETECTION FOR MOBILE DEVICES

Filed:2020-10-16Pub:2021-02-04
Applicant:FotoNation Limited

An approach for an iris liveness detection is provided. A plurality of image pairs is acquired using one or more image sensors of a mobile device. A particular image pair is selected from the plurality of image pairs, and a hyperspectral image is generated for the particular image pair. Based on, at least in part, the hyperspectral image, a particular feature vector for the eye-iris region depicted in the particular image pair is generated, and one or more trained model feature vectors generated for facial features of a particular user of the device are retrieved. Based on, at least in part, the particular feature vector and the one or more trained model feature vectors, a distance metric is determined and compared with a threshold. If the distance metric exceeds the threshold, then a first message indicating that the plurality of image pairs fails to depict the particular user is generated. It is also determined whether at least one characteristic, of one or more characteristics determined for NIR images, changes from image-to-image by at least a second threshold. If so, then a second message is generated to indicate that the plurality of image pairs depicts the particular user of a mobile device. The second message may also indicate that an authentication of an owner to the mobile device was successful. Otherwise, a third message is generated to indicate that a presentation attack on the mobile device is in progress.