Trainable Classifiers Or Pattern Recognizers (e.g., Adaline, Perceptron) Patents (Class 382/159)
  • Patent number: 12380709
    Abstract: Systems and methods analyze a data set including a plurality of images. In one implementation, at least one processor receives a plurality of images acquired by one or more cameras associated with at least one vehicle; and analyzes the plurality of images using an active learning system configured to determine a relative priority ranking among the plurality of images. The relative priority ranking indicates an ordered sequence for the plurality of images, and is determined based on at least one indicator, determined for each of the plurality of images, of a complexity level and a diversity level associated with representations of one or more objects represented in the plurality of images. The at least one processor then outputs information indicating the relative priority ranking among the plurality of images.
    Type: Grant
    Filed: October 22, 2021
    Date of Patent: August 5, 2025
    Assignee: Mobileye Vision Technologies Ltd.
    Inventor: Galit Levin
  • Patent number: 12380715
    Abstract: A platform for data collection, and in particular image collection, and model building therefrom is disclosed. In examples, received media content data, including image data, may be assigned a context category, and one or more context-specific models may be used to automatically annotate the image. Accuracy monitoring of the image annotations may indicate a need to manually annotate images for subsequent training. A priority may be assigned to one or more images, such that images may be queued for additional annotation. Such additional annotations may be used for model retraining. In some instances, a separate classification model may be used to identify a context category for image data from among predetermined contexts.
    Type: Grant
    Filed: December 21, 2022
    Date of Patent: August 5, 2025
    Assignee: Target Brands, Inc.
    Inventors: Arun Patil, Snigdha Samal, Mohit Sethi, Neha Dixit, Anil Prasad, Mohammed Jafer
  • Patent number: 12380199
    Abstract: Computing systems of a multi-tenant trusted domain collect metadata describing data stored in data sources of a set of tenant trusted domains. The computing systems of the multi-tenant trusted domain use the metadata to process natural language questions based on data stored in data sources of a tenant trusted domain. The computing systems of the multi-tenant trusted domain identify a set of data sources of the tenant trusted domain that are relevant for processing the natural language question and generate an execution plan for answering the natural language question. The computing systems of the multi-tenant trusted domain send the execution plan to one or more computing systems of the tenant trusted domain. The computing systems of the tenant trusted domain execute the execution plan and send the result of executing the execution plan to a client device that sent the natural language question.
    Type: Grant
    Filed: June 1, 2022
    Date of Patent: August 5, 2025
    Assignee: Promethium, Inc.
    Inventors: Shuo Yang, Xicheng Chang, Himangshu Das, Azary Smotrich, Puneet Gupta, Kaycee Kuan-Cheng Lai
  • Patent number: 12373202
    Abstract: A method, a system, and computer program product for managing upgrades of cloud-based software applications are provided. A compatibility of planned changes to a cloud-based software application with a cloud-based system hosting the application is determined. An inclusion of a past mitigation in the planned changes to the cloud-based software application is verified. A test of the one or more planned changes to the cloud-based software application is executed. Modified planned changes to the cloud-based software application are generated based on a result of the test. An upgrade score of the modified planned changes to the cloud-based software application is determined and used for managing a deployment, to a productive system, of the modified planned changes to the cloud-based software application.
    Type: Grant
    Filed: February 22, 2023
    Date of Patent: July 29, 2025
    Assignee: SAP SE
    Inventors: Mukesh Kumar, Narasimhan Balasubramanian, Tara Reapy, Chandan Potukuchi
  • Patent number: 12367656
    Abstract: A system determines an input video and a first annotated image from the input video which identifies an object of interest. The system initiates a tracker based on the first annotated image and the input video. The tracker generates, based on the first annotated image and the input video, information including: a sliding window for false positives; a first set of unlabeled images from the input video; and at least two images with corresponding labeled states. A semi-supervised classifier classifies, based on the information, the first set of unlabeled images from the input video. If a first unlabeled image is classified as a false positive, the system reinitiates the tracker based on a second annotated image occurring in a frame prior to a frame with the false positive. The system generates an output video comprising the input video displayed with tracking on the object of interest.
    Type: Grant
    Filed: September 8, 2022
    Date of Patent: July 22, 2025
    Assignee: Xerox Corporation
    Inventors: Matthew A. Shreve, Robert R. Price, Jeyasri Subramanian, Sumeet Menon
  • Patent number: 12361244
    Abstract: Methods, systems, and media for validating objects within a container, such that a validation system confirms that objects within a container are valid i.e., ready to be shipped from a data center for reuse or ready to be destroyed. The validation system can be used to identify objects, e.g., media, disks, etc., and determine if valid objects are at the correct processing location to be shipped or destroyed. The methods may include identifying the objects, locating an identifier on the object, comparing the identifiers to known identifiers and determining, based on the comparison, if valid objects are at the correct processing location to be shipped or destroyed. The validation system may also use image processing techniques, such as blob detection, to identify invalid objects in the container. The system may reject the entire container if invalid objects are detected.
    Type: Grant
    Filed: April 26, 2024
    Date of Patent: July 15, 2025
    Assignee: Google LLC
    Inventors: Samuel Gardner Garrett, Peter Sarossy, Rachel Soukup, Avinash Panga, Yong Hoon Kim, Addison Hammer, Jingwen Mao
  • Patent number: 12361280
    Abstract: To train a machine learning routine (BNN), a sequence of first training data (PIC) is read in through the machine learning routine. The machine learning routine is trained using the first training data, wherein a plurality of learning parameters (LP) of the machine learning routine is set by the training. Furthermore, a value distribution (VLP) of the learning parameters, which occurs during the training, is determined and a continuation signal (CN) is generated on the basis of the determined value distribution of the learning parameters. Depending on the continuation signal, the training is then continued with a further sequence of the first training data or other training data (PIC2) are requested for the training.
    Type: Grant
    Filed: July 29, 2019
    Date of Patent: July 15, 2025
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Markus Michael Geipel, Stefan Depeweg, Christoph Tietz, Gaby Marquardt, Daniela Seidel
  • Patent number: 12354324
    Abstract: The present disclosure relates to utilizing a style-matching image generation system to generate large datasets of style-matching images having matching styles and content to an initial small sample set of input images. For example, the style-matching image generation system utilizes a selection of style-mixed stored images with a generative machine-learning model to produce large datasets of synthesized images. Further, the style-matching image generation system utilizes the generative machine-learning model to conditionally sample synthesized images that accurately match the style, content, characteristics, and patterns of the initial small sample set and that also provide added variety and diversity to the large image dataset.
    Type: Grant
    Filed: December 12, 2022
    Date of Patent: July 8, 2025
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Maurice Diesendruck, Harsh Shrivastava
  • Patent number: 12347017
    Abstract: There is provided an apparatus for generating a 3D object texture map. The apparatus may comprise: a memory; and a processor, wherein the processor is configured to: generate a partial texture image by mapping object information in an input image into a texture space; obtain a sampling image by inputting the partial texture image to a sampler network trained according to a curriculum for selecting at least one of a training partial texture image, an aligned partial image in which the training partial texture image is aligned in the texture space, or an augmented partial image augmented from the aligned partial image; obtain a blending mask and a refined image by inputting the sampling image to a refiner network; and generate a 3D object texture map by blending the sampling image and the refined image based on the blending mask.
    Type: Grant
    Filed: February 17, 2023
    Date of Patent: July 1, 2025
    Assignee: Korea Advanced Institute of Science and Technology
    Inventors: Junyong Noh, Sihun Cha, Kwanggyoon Seo, Amirsaman Ashtari
  • Patent number: 12340547
    Abstract: An electronic device for detecting a target object is configured to obtain a plurality of first candidate boxes corresponding to a first object and a plurality of second candidate boxes corresponding to a second object by applying an image is provided. The electronic device includes the first object and the second object to an artificial intelligence model, wherein the artificial intelligence model is trained to use a loss function for reducing a size difference between candidate boxes corresponding to two adjacent objects to determine sizes of the candidate boxes.
    Type: Grant
    Filed: December 15, 2022
    Date of Patent: June 24, 2025
    Assignee: Samsung Electronics Co., Ltd.
    Inventors: Juyong Song, Byeongcheol Kang, Youngchul Sohn, Ilgu Kang, Sunghyun Choi
  • Patent number: 12340570
    Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for image segmentation. The method may be executed by a trained image segmentation model. The method includes: obtaining a to-be-processed image, wherein the to-be-processed image includes objects of a plurality of categories. The method further includes: selecting a to-be-discarded category in the to-be-processed image according to a recall rate of each of the plurality of categories obtained in advance. The method further includes: processing the to-be-processed image based on a plurality of remaining categories in the plurality of categories other than the to-be-discarded category to obtain a segmented image. Through the method, computing resources required for a segmentation processing task can be greatly reduced, a processing amount of image data can be reduced, and an image processing speed can be increased.
    Type: Grant
    Filed: June 1, 2022
    Date of Patent: June 24, 2025
    Assignee: Dell Products L.P.
    Inventors: Zijia Wang, Jiacheng Ni, Zhen Jia
  • Patent number: 12332857
    Abstract: Methods, systems, and devices for providing computer implemented services are disclosed. To provide the computer implemented services, a storage space in a data management system may be managed. To manage the storage system, data may be obtained for storage. The data may be classified as being related to at least one topic, and quality requirements may be identified based on the at least one topic. A determination may be made, based on the quality requirements, regarding whether the data meets the quality requirements. If the quality requirements are not met by the data, then storage of the data may be denied temporarily. Otherwise, if the quality requirements are met by the data, then the data may be stored.
    Type: Grant
    Filed: August 30, 2023
    Date of Patent: June 17, 2025
    Assignee: Dell Products L.P.
    Inventors: Prem Pradeep Motgi, Dharmesh M. Patel, Manpreet Singh Sokhi
  • Patent number: 12333432
    Abstract: This disclosure describes an activity recognition system for asymmetric (e.g., left- and right-handed) activities that leverages the symmetry intrinsic to most human and animal bodies. Specifically, described is 1) a human activity recognition system that only recognizes handed activities but is inferenced twice, once with input flipped, to identify both left- and right-handed activities and 2) a training method for learning-based implementations of the aforementioned system that flips all training instances (and associated labels) to appear left-handed and in doing so, balances the training dataset between left- and right-handed activities.
    Type: Grant
    Filed: May 3, 2023
    Date of Patent: June 17, 2025
    Assignee: Hinge Health, Inc.
    Inventors: Colin Brown, Andrey Tolstikhin
  • Patent number: 12333439
    Abstract: A method for generating an optimised domain-generalisable model for re-identification of a target in a set of candidate images. The method optimises a local feature embedding model for domain-specific feature representation at each client of a plurality of clients, then receives, at a central server, information on changes to the local feature embedding model at each respective client resulting from the optimising step, and then updates a global feature embedding model based on the changes to the local feature embedding model. The method further receives, at each client from the central server, information representative of the updates to the global feature embedding model, then maps, at each client, on to the respective local feature embedding model at least a portion of the received updates, and subsequently updates, at each client, the respective local feature embedding model based on the mapped updates.
    Type: Grant
    Filed: January 29, 2021
    Date of Patent: June 17, 2025
    Assignee: VERITONE, INC.
    Inventors: Shaogang Gong, Guile Wu
  • Patent number: 12327430
    Abstract: A method for training a neural network for facial expression recognition includes recognizing a plurality of digital human face models. For each of the plurality of digital human face models, a plurality of simulated facial expressions are simulated. Simulated capacitance measurements for an array of simulated radio frequency (RF) antennas are found for each of the plurality of simulated facial expressions. The simulated capacitance measurements for each simulated facial expression are provided as input training data to a neural network configured to output facial expression parameters based on input capacitance measurements.
    Type: Grant
    Filed: June 28, 2022
    Date of Patent: June 10, 2025
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Jouya Jadidian, Calin Cristian, Petre-Alexandru Arion
  • Patent number: 12322160
    Abstract: An image classification model training method and apparatus are provided. Classification results of each image outputted by an image classification model are obtained. When the classification results outputted by the image classification model do not meet a reference condition, a reference classification result is constructed based on the classification results outputted by the image classification model. Because the reference classification result can indicate a probability that images belong to each class, a parameter of the image classification model is updated to obtain a trained image classification model based on a total error value between the classification results of the each image and the reference classification result.
    Type: Grant
    Filed: October 12, 2022
    Date of Patent: June 3, 2025
    Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
    Inventors: Donghuan Lu, Junjie Zhao, Kai Ma, Yefeng Zheng
  • Patent number: 12314855
    Abstract: This disclosure describes an activity recognition system for asymmetric (e.g., left- and right-handed) activities that leverages the symmetry intrinsic to most human and animal bodies. Specifically, described is 1) a human activity recognition system that only recognizes handed activities but is inferenced twice, once with input flipped, to identify both left- and right-handed activities and 2) a training method for learning-based implementations of the aforementioned system that flips all training instances (and associated labels) to appear left-handed and in doing so, balances the training dataset between left- and right-handed activities.
    Type: Grant
    Filed: May 3, 2023
    Date of Patent: May 27, 2025
    Assignee: Hinge Health, Inc.
    Inventors: Colin Brown, Andrey Tolstikhin
  • Patent number: 12299627
    Abstract: Provided are embodiments for providing analytics indicative of object detection or fill-level detection at or near real-time based on video data captured during an unloading or loading process. A computerized system may detect and classify, using an object-detection machine learning (ML) model, an object based on the video data. A computerized system may further determine, using a fill-level ML model, a fill-level of the storage compartment based on a comparison of edges of the storage compartment to a total dimension corresponding to the edge. In this manner, the various implementations described herein provide a technique for computing systems employing image processing and machine learning techniques to a video data stream to generate analytics associated with the unloading or loading process at or near real-time.
    Type: Grant
    Filed: June 29, 2022
    Date of Patent: May 13, 2025
    Assignee: UNITED PARCEL SERVICE OF AMERICA, INC.
    Inventors: Youngjun Choi, Po-Nien Lin, Hyunki Lee
  • Patent number: 12299953
    Abstract: The present application relates to the technical field of artificial intelligence, and discloses a visual positioning method and apparatus, a device, and a medium. The method includes: performing feature splicing on an image encoding feature and a text encoding feature; performing feature fusion on spliced encoding features to obtain a first fused encoding feature; performing noise correction on the first fused encoding feature and the text encoding feature on the basis of a preset cross-attention mechanism to obtain a corrected fused feature and a corrected text encoding feature, and performing feature fusion on the spliced encoding feature and the corrected text encoding feature to obtain a second fused encoding feature; and correcting a preset frame feature using a target encoding feature on the basis of the corrected fused feature and the second fused encoding feature to predict a regional position coordinate of a target visual object.
    Type: Grant
    Filed: September 28, 2022
    Date of Patent: May 13, 2025
    Assignee: SUZHOU METABRAIN INTELLIGENT TECHNOLOGY CO., LTD.
    Inventors: Xiaochuan Li, Rengang Li, Yaqian Zhao, Zhenhua Guo, Baoyu Fan
  • Patent number: 12299599
    Abstract: A method for lifelong machine learning using boosting includes receiving a new task and a learning sample for the new task. A distribution of weights is learned over the learning sample using previously learned classifiers from old tasks. A set of task-specific classifiers are learned for the new task using a boosting algorithm and the distribution of weights over the learning sample, whereby the distribution of weights over the learning sample is updated using the task-specific classifiers for the new task.
    Type: Grant
    Filed: December 6, 2023
    Date of Patent: May 13, 2025
    Assignee: NEC CORPORATION
    Inventors: Anil Goyal, Ammar Shaker, Francesco Alesiani
  • Patent number: 12293573
    Abstract: The present invention relates to an apparatus and method for generating learning data for an artificial intelligence model, which generate learning data for learning of an artificial intelligence model that detects anomalies of a plant facility, and the apparatus and method collect at least one among structured data and unstructured data, and generate a learning data set for learning of an artificial intelligence model that predicts and diagnoses anomalies of a plant facility using at least one among the structured data and the unstructured data.
    Type: Grant
    Filed: November 22, 2022
    Date of Patent: May 6, 2025
    Assignee: KOREA INSTITUTE of CIVIL ENGINEERING and BUILDING TECHNOLOGY
    Inventors: Seung Ki Ryu, Yeo Hwan Yoon, Young Seok Kim
  • Patent number: 12288667
    Abstract: A method of imaging a sample includes acquiring one or more first images of a region of the sample at a first imaging condition with a charged particle microscope system. The one or more first images are applied to an input of a trained machine learning model to obtain a predicted image indicating atom structure probability in the region of the sample. An enhanced image indicating atom locations in the region of the sample based on the atom structure probability in the predicted image is caused to be displayed in response to obtaining the predicted image.
    Type: Grant
    Filed: June 2, 2022
    Date of Patent: April 29, 2025
    Assignee: FEI Company
    Inventors: Pavel Potocek, Bert Henning Freitag, Maurice Peemen
  • Patent number: 12288379
    Abstract: An exemplary method for detecting deepfake images and providing customized analysis comprises: receiving, from a user, a textual user inquiry regarding an image; inputting the textual inquiry and the image into a deepfake detection model, wherein the deepfake detection model comprises: an image encoder for generating a plurality of image embeddings based on the image; a text encoder for generating a plurality of textual embeddings based on the textual inquiry; one or more layers for generating a plurality of answer embeddings; and a language model for generating a textual analysis based on the plurality of answer embeddings; and outputting the textual analysis, wherein the textual analysis includes a classification result of whether the image is fake and further includes one or more visual features in the image and one or more attributes of the one or more visual features that contribute to the classification result.
    Type: Grant
    Filed: June 21, 2024
    Date of Patent: April 29, 2025
    Assignee: Reality Defender, Inc.
    Inventors: Gaurav Bharaj, Yue Zhang, Ben Colman, Ali Shahriyari
  • Patent number: 12288603
    Abstract: Examples disclosed herein may involve a computing system that is configured to (i) identify a cannabinoid formulation for which to model efficacy for a given health condition shared by a plurality of individuals, (ii) receive respective efficacy information indicating the efficacy of the cannabinoid formulation for the plurality of individuals, (iii) receive respective genetic information for the plurality of individuals, (iv) receive respective biometric information for the plurality of individuals, (v) apply machine learning techniques to group the plurality of individuals into one or more groups based on their (a) respective efficacy information and (b) similarities in their respective genetic information and respective biometric information, and (vi) embody the one or more groups into a machine learning model that functions to (a) receive, as input data, information for a given individual and (ii) based on an evaluation of the received input data, output an efficacy prediction for the given individual.
    Type: Grant
    Filed: April 11, 2022
    Date of Patent: April 29, 2025
    Assignee: Endocanna Health, Inc.
    Inventors: Len May, Eric Kaufman
  • Patent number: 12282548
    Abstract: A system and method for intrusion detection on automotive controller area networks. The system and method can detect various CAN attacks, such as attacks that cause unintended acceleration, deactivation of vehicle's brakes, or steering the vehicle. The system and method detects changes in nuanced correlations of CAN timeseries signals and how they cluster together. The system reverse engineers CAN signals and detect masquerade attacks by analyzing timeseries extracted from raw CAN frames. Specifically, anomalies in the CAN data can be detected by computing timeseries clustering similarity using hierarchical clustering on the vehicle's CAN signals and comparing the clustering similarity across CAN captures with and without attacks.
    Type: Grant
    Filed: April 21, 2022
    Date of Patent: April 22, 2025
    Assignee: UT-Battelle, LLC
    Inventors: Robert A. Bridges, Kiren E. Verma, Michael Iannacone, Samuel C. Hollifield, Pablo Moriano, Jordan Sosnowski
  • Patent number: 12282963
    Abstract: Methods and systems described herein are directed to automatedly generating and issuing accident data gathering recommendations for a vehicle accident occurrence. In response to a recommendation system detecting an occurrence of a vehicle accident, the system can retrieve initial accident data for the occurrence. Using the initial accident data, the system can generate accident data gathering recommendations to obtain additional accident data via mapping to one or more characteristics for the vehicle accident occurrence. The recommendations, when executed, can yield additional accident data that can be compiled with the initial accident data into a vehicle accident evidence package.
    Type: Grant
    Filed: February 8, 2022
    Date of Patent: April 22, 2025
    Assignee: United Services Automobile Association (USAA)
    Inventors: Angelica Nichole White, Sean Carl Mitchem, Sean Michael Wayne Craig, Subhalaskshmi Selvam, Christopher Russell, Brian Howard Katz, Roberto Virgillio Jolliffe
  • Patent number: 12283091
    Abstract: The objective of the present invention is to provide an image classification device and a method therefor with which suitable teaching data can be created. An image classification device that carries out image classification using images which are in a class to be classified and include teaching information, and images which are in a class not to be classified and to which teaching information has not been assigned, said image classification device being characterized by being provided with: an image group input unit for receiving inputs of an image group belonging to a class to be classified and an image group belonging to a class not to be classified; and a subclassification unit for extracting a feature amount for each image in an image group, clustering the feature amounts of the images in the image group belonging to a class not to be classified, and thereby dividing the images into sub-classes.
    Type: Grant
    Filed: November 6, 2020
    Date of Patent: April 22, 2025
    Assignee: Hitachi High-Tech Corporation
    Inventors: Sota Komatsu, Masayoshi Ishikawa, Fumihiro Bekku, Takefumi Kakinuma
  • Patent number: 12277745
    Abstract: A training apparatus includes a processor which acquires an image to which identification information of a subject included in the image is attached as a first correct label the image being included in an image dataset for training and being an image for use in supervised training. The processor further attaches, to the acquired image, classification information based on a feature amount of the acquired image as a second correct label, trains a classifier using the acquired image and the second correct label attached thereto, and updates training content used in the training using the acquired image and the first correct label after training the classifier.
    Type: Grant
    Filed: January 6, 2022
    Date of Patent: April 15, 2025
    Assignee: CASIO COMPUTER CO., LTD.
    Inventor: Yoshihiro Teshima
  • Patent number: 12277193
    Abstract: Synthesizing training data for training a change detection model includes receiving a patched image comprising a background image with a patch pasted into the background image. It further includes synthesizing a harmonized patched image at least in part by harmonizing the patched image using a machine learning model trained on the background image. It further includes providing as output a synthetic training sample usable to train a change detection model. The synthetic training sample includes a reference image, at least a portion of the harmonized patched image, and a corresponding mask.
    Type: Grant
    Filed: August 26, 2021
    Date of Patent: April 15, 2025
    Assignee: EarthDaily Analytics USA, Inc.
    Inventor: Manuel Weber
  • Patent number: 12272120
    Abstract: A perception system may be used to generate bounding boxes for objects in a vehicle scene. The perception system may receive images and feature maps corresponding to the received images. The perception system may generate multiple pluralities of windows and use the multiple pluralities of windows to enrich semantic data of the feature maps. The perception system may use the enriched semantic to generate one or more bounding boxes for objects in the vehicle scene.
    Type: Grant
    Filed: August 19, 2022
    Date of Patent: April 8, 2025
    Assignee: Motional AD LLC
    Inventors: Jongwoo Park, Apoorv Sing, Varun Kumar Reddy Bankiti
  • Patent number: 12271975
    Abstract: A machine learning (ML) model is trained using pairs of images. Each pair includes an image of a human face and a duplicate of the image with a computer game headset overlaid on the face using computer graphics. The ML model subsequently can be used to receive an image of a gamer wearing a headset and output a full-face image of the gamer for use in, e.g., social network settings related to the game.
    Type: Grant
    Filed: November 30, 2022
    Date of Patent: April 8, 2025
    Assignee: Sony Interactive Entertainment Inc.
    Inventors: Rathish Krishnan, Deepali Arya, Manoj Srivastava, Seema Kataria
  • Patent number: 12272119
    Abstract: Aspects of the subject technology relate to systems, methods, and computer-readable media for image classification through a two-stage classifier. Raw image data of an image gathered by a sensor associated with an AV during operation of the AV is accessed. A first stage of a two-stage classifier is applied. The first stage is trained by first raw AV data captured at varying values of one or more capture parameters associated with one or more sensors of the AV in capturing the first raw AV data. A second stage of the two-stage classifier is applied to the raw image data to generate a final classification output. The second stage of the two-stage classifier if formed by a plurality of image calibration classifiers that are trained by second raw AV data at varying values of one or more image calibration parameters.
    Type: Grant
    Filed: November 29, 2022
    Date of Patent: April 8, 2025
    Assignee: GM Cruise Holdings LLC
    Inventors: Victor Wang, Yumin Shen, Zayra Lobo
  • Patent number: 12266209
    Abstract: A system to generate an image classifier and test it nearly instantaneously is described herein. Image embeddings generated by an image fingerprinting model are indexed and an associated approximate nearest neighbors (ANN) model is generated. The embeddings in the index are clustered and the clusters are labeled. Users can provide just a few images to add to the index as a labeled cluster. The ANN model is trained to receive an image embedding as input and return a score and label of the most similar identified embedding. The label may be applied if the score exceeds a threshold value. The image classifier can be tested efficiently using Leave One Out Cross Validation (“LOOCV”) to provide near-instantaneous quality indications of the image classifier to the user. Near-instantaneous indications of outliers in the provided images can also be provided to the user using a distance to the centroid calculation.
    Type: Grant
    Filed: February 26, 2024
    Date of Patent: April 1, 2025
    Assignee: Netskope, Inc.
    Inventors: Jason B. Bryslawskyj, Yi Zhang, Emanoel Daryoush, Ari Azarafrooz, Wayne Xin, Yihua Liao, Niranjan Koduri
  • Patent number: 12263864
    Abstract: A mobile object control device acquires, as learning history data, driving history data obtained when a learning mobile object is operated in a risk-free environment; performs learning for imitating driving of the learning mobile object in the risk-free environment using the learning history data as training data and generates an imitation learning model; acquires, as training history data, driving history data obtained when a mobile object for generating training data is operated in the same environment as the environment in which the learning history data has been acquired; estimates whether the training history data matches the learning history data using the training history data as input to the imitation learning model and assigns a label related to risks; and infers a result for controlling a mobile object to be controlled using at least the label related to risks as training data, based on sensor information of the mobile object.
    Type: Grant
    Filed: March 12, 2019
    Date of Patent: April 1, 2025
    Assignee: Mitsubishi Electric Corporation
    Inventor: Takayuki Itsui
  • Patent number: 12264920
    Abstract: A system and method for detecting tracking vehicles are provided. The method includes determining that a second vehicle is a tracking vehicle for a first vehicle by applying at least one tracking vehicle determination rule to data captured by the first vehicle with respect to the second vehicle, wherein each tracking vehicle determination rule defines a combination of parameters such that the second vehicle is determined to be the tracking vehicle when the data captured by the first vehicle includes the combination of parameters for at least one of the at least one tracking vehicle determination rule; generating a notification upon determination that the second vehicle is the tracking vehicle; and executing a resolution process based on determination that the second vehicle is the tracking vehicle.
    Type: Grant
    Filed: January 28, 2022
    Date of Patent: April 1, 2025
    Assignee: TRATIX INTELLIGENCE LTD
    Inventor: Sharon Rashty
  • Patent number: 12263594
    Abstract: The present disclosure describes systems, robots, and methods for organizing and selecting classifiers of a library of classifiers. The classifiers of the library of classifiers can be organized in a relational model, such as a hierarchy or probability model. Instead of storing, activating, or executing the entire library of classifiers at once by a robot system, computational resource demand is reduced by executing subset of classifiers to determine context, and the determined context is used as a basis for selection of another subset of classifiers. This process can be repeated, to iteratively refine context and select more specific subsets of classifiers. A selected subset of classifiers can eventually be specific to a task to be performed by the robot system, such that the robot system can take action based on output from executing such specific classifiers.
    Type: Grant
    Filed: December 12, 2022
    Date of Patent: April 1, 2025
    Assignee: Sanctuary Cognitive Systems Corporation
    Inventors: Suzanne Gildert, William G. Macready, Thomas Mahon
  • Patent number: 12265459
    Abstract: Implementations of this disclosure provide an anomaly detection system that automatically tunes parameters of a forecasting detector that detects anomalies in a metric time series. The anomaly detection system may implement a three-stage process where a first stage tunes a historical window parameter, a second stage tunes a current window parameter, and a third stage tunes the number of standard deviation different from historical mean required to trigger an alert. The tuned historical window length determined by the first stage may be provided to the second stage as input. Both the tuned historical window length and the tuned current window length may be provided to the third stage as input as use in determining the tuned number of standard deviations.
    Type: Grant
    Filed: January 31, 2023
    Date of Patent: April 1, 2025
    Assignee: Splunk LLC
    Inventors: Joseph Ari Ross, Abraham Starosta
  • Patent number: 12267570
    Abstract: A visible light image generation model learning unit generates a trained visible light image generation model that generates a visible light image in a second time zone from a far-infrared image in a first time zone. The visible light image generation model learning unit includes a first learning unit that machine-learns the far-infrared image in the first time zone and a far-infrared image in the second time zone as teacher data and generates a trained first generation model that generates the far-infrared image in the second time zone from the far-infrared image in the first time zone, and a second learning unit that machine-learns the far-infrared image in the second time zone and the visible light image in the second time zone as teacher data and generates a trained second generation model that generates the visible light image in the second time zone from the far-infrared image in the second time zone.
    Type: Grant
    Filed: February 24, 2023
    Date of Patent: April 1, 2025
    Assignee: JVCKENWOOD Corporation
    Inventors: Yincheng Yang, Shingo Kida, Hideki Takehara
  • Patent number: 12259945
    Abstract: A method may include executing a neural network to extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features; executing the model using a third set of features from a candidate image to generate a candidate image performance score; and generating a record identifying the candidate image performance score.
    Type: Grant
    Filed: October 21, 2024
    Date of Patent: March 25, 2025
    Assignee: VIZIT LABS, INC.
    Inventors: Elham Saraee, Jehan Hamedi, Zachary Halloran
  • Patent number: 12260333
    Abstract: A semi-supervised model incorporates deep feature learning and pseudo label estimation into a unified framework. The deep feature learning can include multiple convolutional neural networks (CNNs). The CNNs can be trained on available training datasets, tuned using a small amount of labeled training samples, and stored as the original models. Features are then extracted for unlabeled training samples by utilizing the original models. Multi-view clustering is used to cluster features to generate pseudo labels. Then the original models are tuned by using an updated training set that includes labeled training samples and unlabeled training samples with pseudo labels. Iterations of multi-view clustering and tuning using an updated training set can continue until the updated training set is stable.
    Type: Grant
    Filed: October 18, 2023
    Date of Patent: March 25, 2025
    Assignee: DeepNorth Inc.
    Inventors: Jinjun Wang, Xiaomeng Xin
  • Patent number: 12254707
    Abstract: Embodiments of the present disclosure relate to a method, device and computer readable storage medium of scene text detection. In the method, a first visual representation of a first image is generated with an image encoding process. A first textual representation of a first text unit in the first image is generated with a text encoding process based on a first plurality of symbols obtained by masking a first symbol of a plurality of symbols in the first text unit. A first prediction of the masked first symbol is determined with a decoding process based on the first visual and textual representations. At least the image encoding process is updating according to at least a first training objective to increase at least similarity of the first prediction and the masked first symbol.
    Type: Grant
    Filed: September 28, 2022
    Date of Patent: March 18, 2025
    Assignees: LEMON INC., BEIJING YOUZHUJU NETWORK TECHNOLOGY CO., LTD.
    Inventors: Chuhui Xue, Wenqing Zhang, Yu Hao, Song Bai
  • Patent number: 12254421
    Abstract: A method for lifelong machine learning using boosting includes receiving a new task and a learning sample for the new task. A distribution of weights is learned over the learning sample using previously learned classifiers from old tasks. A set of task-specific classifiers are learned for the new task using a boosting algorithm and the distribution of weights over the learning sample, whereby the distribution of weights over the learning sample is updated using the task-specific classifiers for the new task.
    Type: Grant
    Filed: December 6, 2023
    Date of Patent: March 18, 2025
    Assignee: NEC CORPORATION
    Inventors: Anil Goyal, Ammar Shaker, Francesco Alesiani
  • Patent number: 12254681
    Abstract: Systems and methods are provided for multi-modal test-time adaptation. The method includes inputting a digital image into a pre-trained Camera Intra-modal Pseudo-label Generator, and inputting a point cloud set into a pre-trained Lidar Intra-modal Pseudo-label Generator. The method further includes applying a fast 2-dimension (2D) model, and a slow 2D model, to the inputted digital image to apply pseudo-labels, and applying a fast 3-dimension (3D) model, and a slow 3D model, to the inputted point cloud set to apply pseudo-labels. The method further includes fusing pseudo-label predictions from the fast models and the slow models through an Inter-modal Pseudo-label Refinement module to obtain robust pseudo labels, and measuring a prediction consistency for the pseudo-labels.
    Type: Grant
    Filed: September 6, 2022
    Date of Patent: March 18, 2025
    Assignee: NEC Corporation
    Inventors: Yi-Hsuan Tsai, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Sparsh Garg, Ramin Moslemi, Inkyu Shin
  • Patent number: 12254667
    Abstract: A multiple scenario-oriented item retrieval method and system. The method includes the steps of extracting, by Hashing learning, image features from an image training set to train a pre-built item retrieval model; when an image is in a scenario of hard samples, introducing an adaptive similarity matrix, optimizing the similarity matrix by an image transfer matrix, constructing an adaptive similarity matrix objective function in combination with an image category label; constructing a loss quantization objective function between the image and a Hash code according to the image transfer matrix; when the image is in a scenario of zero samples, introducing an asymmetric similarity matrix, constructing an objective function by taking the image category label as supervisory information in combination with equilibrium and decorrelation constraints of the Hash code; and training the item retrieval model based on the above objective function to obtain a retrieved result of a target item image.
    Type: Grant
    Filed: August 5, 2022
    Date of Patent: March 18, 2025
    Assignee: Shandong Jianzhu University
    Inventors: Xiushan Nie, Yang Shi, Jie Guo, Xingbo Liu, Yilong Yin
  • Patent number: 12246452
    Abstract: The present disclosure describes systems, robots, and methods for organizing and selecting classifiers of a library of classifiers. The classifiers of the library of classifiers can be organized in a relational model, such as a hierarchy or probability model. Instead of storing, activating, or executing the entire library of classifiers at once by a robot system, computational resource demand is reduced by executing subset of classifiers to determine context, and the determined context is used as a basis for selection of another subset of classifiers. This process can be repeated, to iteratively refine context and select more specific subsets of classifiers. A selected subset of classifiers can eventually be specific to a task to be performed by the robot system, such that the robot system can take action based on output from executing such specific classifiers.
    Type: Grant
    Filed: October 7, 2022
    Date of Patent: March 11, 2025
    Assignee: Sanctuary Cognitive Systems Corporation
    Inventors: Suzanne Gildert, William G. Macready, Thomas Mahon
  • Patent number: 12245828
    Abstract: Disclosed herein are systems and methods for using a robotic surgical system comprising a GUI and a robotic arm.
    Type: Grant
    Filed: January 29, 2024
    Date of Patent: March 11, 2025
    Assignee: Globus Medical, Inc.
    Inventors: Albert Kim, Gregory Pellegrino
  • Patent number: 12249134
    Abstract: The second multi-dimensional feature vectors 92a of sample image data 34a having instruction signals that are converted by a feature converter 27 are read in (Step S10), two-dimensional graph data for model 36a is generated based on the read second multi-dimensional feature vectors 92a to be stored (Step S12), two-dimensional model graphs Og and Ng are generated based on the generated two-dimensional graph data for model 36a, to be displayed on the window 62 (Step S14). The second multi-dimensional feature vectors 92a are indicators appropriate for visualization of the trained state (individuality) of a trained model 35. Thus, it is possible to visually check and evaluate whether the trained model 35 is in an appropriately trained state (individuality) or not.
    Type: Grant
    Filed: April 1, 2021
    Date of Patent: March 11, 2025
    Assignee: ROXY CORP.
    Inventors: Takayuki Ishiguro, Hitoshi Hoshino
  • Patent number: 12243309
    Abstract: The present disclosure describes approaches for evaluating interest points for localization uses based on a repeatability of the detection of the interest point in images capturing a scene that includes the interest point. The repeatability of interest points is determined by using a trained repeatability model. The repeatability model is trained by analyzing a time series of images of a scene and determining repeatability functions for each interest point in the scene. The repeatability function is determined by identifying which images in the time series of images allowed for the detection of the interest point by an interest point detection model.
    Type: Grant
    Filed: April 27, 2022
    Date of Patent: March 4, 2025
    Assignee: Niantic, Inc.
    Inventors: Dung Anh Doan, Daniyar Turmukhambetov, Soohyun Bae
  • Patent number: 12243297
    Abstract: A computer-implemented method can include a training phase and a hemorrhage detection phase. The training phase can include: receiving a first plurality of frames from at least one original computed tomography (CT) scan of a target subject, wherein each frame may or may not include a visual indication of a hemorrhage, and further wherein each frame including a visual indication of a hemorrhage has at least one label associated therewith; and using a fully convolutional neural network (FCN) to train a model by determining, for each of the first plurality of frames, whether at least one sub-portion of the frame includes a visual indication of a hemorrhage and classifying the sub-portion of the frame based on the determining.
    Type: Grant
    Filed: July 20, 2020
    Date of Patent: March 4, 2025
    Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
    Inventors: Jitendra Malik, Pratik Mukherjee, Esther L. Yuh, Wei-Cheng Kuo, Christian Haene
  • Patent number: 12242469
    Abstract: Provided are computing systems, methods, and platforms for generating training and testing data for machine-learning models. The operations can include receiving signal extraction information that has instructions to query a data store. Additionally, the operations can include accessing, using Structured Query Language (SQL) code generated based on the signal extraction information, raw data from the data store. Moreover, the operations can include processing the raw data using signal configuration information to generate a plurality of signals. The signal configuration information can have instructions on how to generate the plurality of signals from the raw data. Furthermore, the operations can include joining, using SQL code, the plurality of signals with a first label source to generate training data and testing data. Subsequently, the operations can include processing the training data and the testing data to generate the input data. The input data being an ingestible for a machine-learning pipeline.
    Type: Grant
    Filed: December 16, 2022
    Date of Patent: March 4, 2025
    Assignee: GOOGLE LLC
    Inventors: Madhav Datt, Sukriti Ramesh