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Analysis

1 / 1,247
1.20190295282Stereo depth estimation using deep neural networks
US - 26.09.2019
Int.Class G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
04Architecture, e.g. interconnection topology
 Appl.No 16356439 Applicant NVIDIA Corporation Inventor Nikolai Smolyanskiy

Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.

2.WO/2019/028279METHODS AND SYSTEMS FOR OPTIMIZING ENGINE SELECTION USING MACHINE LEARNING MODELING
WO - 07.02.2019
Int.Class G06N 99/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
99Subject matter not provided for in other groups of this subclass
 Appl.No PCT/US2018/045051 Applicant VERITONE, INC. Inventor STEELBERG, Chad
A system for optimizing selection of transcription engines using a combination of selected machine learning models. The system includes a plurality of preprocessors that generate a plurality of features from a media data set. The system further includes a deep learning neural network model, a gradient boosted machine model and a random forest model used in generating a ranked list of transcription engines. A transcription engine is selected from the ranked list of transcription engines to generate a transcript for the media dataset.
3.4163833DEEP NEURAL NETWORK MODEL DESIGN ENHANCED BY REAL-TIME PROXY EVALUATION FEEDBACK
EP - 12.04.2023
Int.Class G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
04Architecture, e.g. interconnection topology
 Appl.No 22186944 Applicant INTEL CORP Inventor CUMMINGS DANIEL J
The present disclosure is related to artificial intelligence (AI), machine learning (ML), and Neural Architecture Search (NAS) technologies, and in particular, to Deep Neural Network (DNN) model engineering techniques that use proxy evaluation feedback. The DNN model engineering techniques discussed herein provide near real-time feedback on model performance via low-cost proxy scores without requiring continual training and/or validation cycles, iterations, epochs, etc. In conjunction with the proxy-based scoring, semi-supervised learning mechanisms are used to map proxy scores to various model performance metrics. Other embodiments may be described and/or claimed.
4.11900222Efficient machine learning model architecture selection
US - 13.02.2024
Int.Class G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
 Appl.No 16355185 Applicant Google LLC Inventor Jyrki A. Alakuijala

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing a machine learning model that is trained to perform a machine learning task. In one aspect, a method comprises receiving a request to train a machine learning model on a set of training examples; determining a set of one or more meta-data values characterizing the set of training examples; using a mapping function to map the set of meta-data values characterizing the set of training examples to data identifying a particular machine learning model architecture; selecting, using the particular machine learning model architecture, a final machine learning model architecture for performing the machine learning task; and training a machine learning model having the final machine learning model architecture on the set of training examples.

5.20190043487METHODS AND SYSTEMS FOR OPTIMIZING ENGINE SELECTION USING MACHINE LEARNING MODELING
US - 07.02.2019
Int.Class G10L 15/16
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
15Speech recognition
08Speech classification or search
16using artificial neural networks
 Appl.No 15922802 Applicant Veritone, Inc. Inventor Steven Neal Rivkin

A system for optimizing selection of transcription engines using a combination of selected machine learning models. The system includes a plurality of preprocessors that generate a plurality of features from a media data set. The system further includes a deep learning neural network model, a gradient boosted machine model and a random forest model used in generating a ranked list of transcription engines. A transcription engine is selected from the ranked list of transcription engines to generate a transcript for the media dataset.

6.4358393DEEP LEARNING MODELS FOR ELECTRIC MOTOR WINDING TEMPERATURE ESTIMATION AND CONTROL
EP - 24.04.2024
Int.Class H02P 21/00
HELECTRICITY
02GENERATION, CONVERSION, OR DISTRIBUTION OF ELECTRIC POWER
PCONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
21Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
 Appl.No 23197731 Applicant DEERE & CO Inventor THYAGARAJAN LAV
A motor control system includes a motor including a plurality of windings, a first sensor configured to sense a first operating parameter of the motor, a second sensor configured to sense a second operating parameter of the motor, and memory hardware configured to store a machine learning model and computer-executable instructions. The machine learning model is trained to generate a winding temperature estimation output based on motor operating parameter inputs. The motor control system includes processor hardware configured to execute the instructions and use the machine learning model to cause the motor control system to generate a winding temperature estimation output using the machine learning model based on the first operating parameter and the second operating parameter, the temperature estimation output indicative of a predicted temperature of the plurality of windings, and control the motor based on the winding temperature estimation output.
7.WO/2024/035630METHOD AND SYSTEM TO DETERMINE NEED FOR HOSPITAL ADMISSION AFTER ELECTIVE SURGICAL PROCEDURES
WO - 15.02.2024
Int.Class G16H 50/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
30for calculating health indices; for individual health risk assessment
 Appl.No PCT/US2023/029610 Applicant NEW YORK SOCIETY FOR THE RELIEF OF THE RUPTURED AND CRIPPLED, MAINTAINING THE HOSPITAL FOR SPECIAL SURGERY Inventor SHEN, Tony S.
A computer-implemented method includes: accessing electronic healthcare records of a group of patients, wherein each patient has received an elective surgical procedure; extracting a data structure encoding a plurality of features of each patient from the group of patients, wherein a subset of the group of patients undergo at least one hospital-based intervention after receiving the elective surgical procedure; determining, using a machine learning algorithm that operates on the data structure, a Shapley value for each of the features, wherein the Shapley value indicates a likelihood for each patient with a corresponding feature to receive at least one hospital-based intervention; identifying a subset of the plurality of features; and based on the identified subset of features, establishing a predictive tool to predict a combined likelihood for a patient to receive a hospital-based intervention.
8.20240119278TRANSFER LEARNING FOR SENIORITY MODELING LABEL SHORTAGE
US - 11.04.2024
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 17962364 Applicant Microsoft Technology Licensing, LLC Inventor Zheng ZHANG

Techniques for using transfer learning to address label data shortage in seniority modeling for an online service are disclosed herein. In some embodiments, a computer-implemented method comprises training an initialized neural network using training examples comprising profile data and labels for the profile data, where each label comprises a standardized position title, and the training of the initialized neural network forms a pre-trained neural network. Next, the computer system may train the pre-trained neural network using training examples comprising profile data and labels for the profile data, where the labels comprise a position seniority, and the training of the pre-trained neural network forms a fine-tuned neural network. The computer system may then compute the position seniority for a user based on profile data of the user using the fine-tuned neural network, and use the position seniority of the user in an application of an online service.

9.WO/2022/094624MODEL-BASED REINFORCEMENT LEARNING FOR BEHAVIOR PREDICTION IN AUTONOMOUS SYSTEMS AND APPLICATIONS
WO - 05.05.2022
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No PCT/US2021/072157 Applicant NVIDIA CORPORATION Inventor SMOLYANSKIY, Nikolai
In various examples, reinforcement learning is used to train at least one machine learning model (MLM) to control a vehicle by leveraging a deep neural network (DNN) trained on real-world data by using imitation learning to predict movements of one or more actors to define a world model. The DNN may be trained from real-world data to predict attributes of actors, such as locations and/or movements, from input attributes. The predictions may define states of the environment in a simulator, and one or more attributes of one or more actors input into the DNN may be modified or controlled by the simulator to simulate conditions that may otherwise be unfeasible. The MLM(s) may leverage predictions made by the DNN to predict one or more actions for the vehicle.
10.WO/2019/182974STEREO DEPTH ESTIMATION USING DEEP NEURAL NETWORKS
WO - 26.09.2019
Int.Class G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
04Architecture, e.g. interconnection topology
 Appl.No PCT/US2019/022753 Applicant NVIDIA CORPORATION Inventor SMOLYANSKIY, Nikolai
Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.
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