Abstract: A computerized system and method may process and detect anomalies in input data using of machine learning models and techniques. A computerized system comprising one or more processors, a memory, and a communication interface to communicate via a communication network with remote computing devices, may be used for assembling a signal based on event data items; calculating an anomaly score for the signal, which may describe a change or difference between the signal and past signals; generating an alert based on the calculated score; presenting the alert on an output computer display; and allowing or reversing data transfers performed over a communication network between physically separate computer systems based on the anomaly score. Some embodiments of the invention may include performing peer anomaly detection context anomaly detection as two separate and distinct anomaly detection procedures, using separate and distinct machine learning models and algorithms.

Abstract: A method for intelligently reading displayed contents by an electronic device is provided. The method includes obtaining a screen representation based on a plurality of contents displayed on a screen of the electronic device. The method includes extracting a plurality of insights comprising at least one of intent, importance, emotion, sound representation and information sequence of the plurality of contents from the plurality of contents based on the screen representation. The method includes generating audio emulating the extracted plurality of insights.

Abstract: The invention relates to substituted 6-substituted isoquinoline oxide compounds of formula (V) and to a process for making them. The compounds of formula (V) can be used as intermediates for making 6-substituted-1-(2H)-isoquinolinone compounds of formula (I) The compounds of formula (I) are inhibitors of the enzyme Rho-kinase, or can be used as intermediates in the preparation of further inhibitors of the Rho-kinase enzyme.

Abstract: Techniques for performing speaker error correction are described. In some examples, speaker error correction is a post-processing task aligned predicted words and predicted one or more speaker identities, wherein the post-processing at least includes: performing predicted speaker error correction on the aligned predicted words and predicted one or more speaker identities to generate a first corrected set of speaker error corrections of the one or more speaker identifiers.

Abstract: A vehicle steering wheel device enabling miniaturizing an airbag module and also a miniaturized horn pad portion while ensuring a damping effect comparable to a conventional damping effect. Each damper has a straight pin, a grip provided at a first end is engaged to a central cored bar through rotate operation around the pin axis, and a connecting tip end part that connects to the module housing is provided at the other end. A plurality of insertion holes through which the connecting tip end part is inserted are arranged in the module housing surrounding the outer edge of the inflator, and the connecting tip end part is formed with a constricted portion having an outer diameter smaller than the diameter of the insertion hole, and the connecting tip end part is connected to the module housing via a set spring supported by the module housing to engage the constriction.

Abstract: Present disclosure relates to a method and a user equipment (UE) for enhancing multimodal input content. The UE obtains text-blocks associated with multimodal input and determines a plurality of features for each of the text-blocks using a natural language processing (NLP) model and a neural network (NN) model. Further, the UE identifies usage pattern of the plurality of features based on historic data associated with the usage of the plurality of features using NN model. Thereafter, the UE generates personalized text for each of the textblocks based on the plurality of features and usage pattern. The UE inserts personalized emoticons based on context of text-blocks and lingo of each word using user lingo database. Thus, the UE eliminates manual correction of texts and enhances the readability of message/text entered by user.

Abstract: The present disclosure is related to the field of digital communication and provides a method and system for providing personalized multimodal objects in real-time. An object predicting system receives a text input from at least one application installed in a user device associated with a user. Thereafter, the object predicting system determines an intent of the user by analyzing the text input, which is then correlated with contextual data to generate a query. Subsequently, the object predicting system performs a unified search in a universal database, based on the query, wherein the universal database comprises multimodal data. Further, a plurality of multimodal objects predicted in response to the unified search are ranked based on at least one of the contextual data and user preferences. Finally, at least one of the predicted plurality of multimodal objected related to the text input are provided to the user based on the ranking.

Abstract: The invention provides a system and method for managing the lifecycle of machine learning models, from development to deployment and ongoing operation, across various environments including on-premises, cloud, and hybrid infrastructures. The system features a model build platform for data processing, feature generation, model development, training, and hyperparameter tuning. A model analytics engine extracts metadata, performs complexity analysis, and generates configuration files specifying environment settings and resource needs. A secure model repository enables version-controlled storage, while a deployment platform retrieves, validates, and deploys models in containerized environments like OpenShift or Kubernetes. The platform dynamically allocates resources, supports real-time and batch scoring, and monitors model performance with guardrails.

Abstract: Provided are a method and system for identity correlation between a transaction applicant (TA) and a watchlist entity (WE). Preexisting watchlist data and other aggregated identity data (AID) are processed to provide for comparison to a collective identity of at least the TA. Using various categorizations for the AID and the collective identity, watchlist tags are generated that can then be matched to the collective identity. As a result of the matching, a watchlist candidacy demonstrating a probability that the identity of the TA does or does not correspond to that of the WE can be generated. Natural language processing may be used in connection with sourced textual information such as from news articles, social media posts and the like to supplement the AID available to the system and to further enhance matching functionality.

Abstract: Techniques for performing speaker error correction are described. In some examples, speaker error correction is a post-processing task to be performed on aligned predicted words and predicted one or more speaker identities to jointly perform speaker identities error correction and at least inverse text normalization, wherein the post-processing at least includes: predicting word and speaker contextual features from the aligned predicted words and predicted one or more speaker identities using an encoder, and predicting, from the word and speaker contextual features, inverse text normalization of the aligned predicted words and corrected speaker identities using a decoder.

Abstract: A computer-implemented method for pushing one or more event messages to one or more cloud-based data service applications. The method comprising: receiving a first publication trigger signal indicating that one or more event messages of a first event type are to be published; identifying the one or more event messages of the first event type in an event storage database; retrieving the one or more event messages of the first event type from the event storage database; identifying a first mapping from a plurality of stored mappings, the first mapping corresponding to the first event type; retrieving the first mapping; updating metadata associated with the one or more event messages of the first event type to indicate that publication has occurred; and pushing the one or more event messages of the first event type to one or more cloud-based data service applications according to the first mapping.

Abstract: Provided are a method and system for identity correlation between a transaction applicant (TA) and a watchlist entity (WE). Preexisting watchlist data and other aggregated identity data (AID) are processed to provide for comparison to a collective identity of at least the TA. Using various categorizations for the AID and the collective identity, watchlist tags are generated that can then be matched to the collective identity. As a result of the matching, a watchlist candidacy demonstrating a probability that the identity of the TA does or does not correspond to that of the WE can be generated. Various implementations of identity correlation results may be leveraged for different use cases including an automobile purchasing service, a corporate financing service, an aircraft monitoring service as well as others. Identities may comprise entities, physical objects, real estate or other items or concepts.

Abstract: The present disclosure provides an example method that includes: (a) obtaining context data descriptive of an environment surrounding an autonomous vehicle, the context data based on map data and perception data; (b) generating, by a proposer and based on the context data: (i) a plurality of candidate trajectories, and (ii) a plurality of actor forecasts for a plurality of actors in the environment; (c) generating, by a ranker and based on the context data, the plurality of candidate trajectories, and the plurality of actor forecasts, a ranking of the plurality of candidate trajectories; and (d) controlling a motion of the autonomous vehicle based on a candidate trajectory selected based on the ranking of the plurality of candidate trajectories, wherein the proposer comprises a first machine-learned model and the ranker comprises a second machine-learned model, and wherein the first machine-learned model and the second machine-learned model use a common backbone architecture.

Abstract: To provide an airbag device provided in a steering wheel, which enables an airbag module to be made smaller so as to be compatible with a steering wheel having a smaller center pad than conventional ones. The airbag device includes an airbag module containing an airbag cushion and a cylindrical inflator that supplies inflator gas to the airbag cushion, a damper unit that dampens vibrations transmitted between the airbag module and the steering wheel, a mounting member formed of a plate material and to which the inflator and the damper unit are assembled, and an elastic member that is provided between the mounting member and the steering wheel and elastically holds the airbag module to the steering wheel. The mounting member is formed with a circular through-hole part for press-fitting the inflator to secure the inflator, and a retaining part for holding the damper unit.

Abstract: A system and method for identifying data connections may submit alert data items of one or more datasets to a machine learning model, wherein the alert data items of each dataset include: an alert rule that initiated an alert for the dataset including a first set of thresholds, one or more data values assessed by the first set of thresholds in the generation of the alert, and an alert categorization of the alert selected from a true positive categorization or a false positive categorization; assess, combinations of the alert categorization in relation to the one or more data values and the alert rule; generate a second set of thresholds for the one or more data values, wherein the second set of thresholds has a reduced false positive categorization of the alerts compared to the first set of thresholds; and update the alert rule to comprise the second set of thresholds.

Abstract: A method for intelligently reading displayed contents by an electronic device is provided. The method includes obtaining a screen representation based on a plurality of contents displayed on a screen of the electronic device. The method includes extracting a plurality of insights comprising at least one of intent, importance, emotion, sound representation and information sequence of the plurality of contents from the plurality of contents based on the screen representation. The method includes generating audio emulating the extracted plurality of insights.

Abstract: A method for training a machine learning (ML) model using a large language model (LLM) is provided. A system for detecting fraud which utilizes the LLM-trained ML model trained is also provided. An artificial intelligence (AI)-based method for monitoring alerts is also provided. The method for training an ML model using an LLM includes receiving tabular data for training the ML model, generating one or more natural-language strings comprising information from the tabular data, generating, via a base LLM, one or more prompts and completions based on the one or more generated natural-language strings, pre-training the base LLM using a plurality of generated prompts and completions, updating the base LLM via supervised learning using a cross-entropy loss function with ground-truth labels, and fine-tuning the updated LLM via reinforcement learning with human feedback using a reward model and a proximal policy optimization model to produce the LLM-trained ML model.

Abstract: An autonomous machine learning (ML) system and methods are provided that are configured to intelligently cluster training data into separate training data sets for customized ML model training. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform model training operations which include accessing training data, determining a set of features used for the customized ML model training, clustering the training data into the separate training data sets according to the set of features, outputting the separate training data sets, training the plurality of ML models, packaging the plurality of ML models in individual data containers, and configuring the ML data processing platform with the individual data containers.

Abstract: A system and method for prioritizing customer data may include a computing device; a memory; and a processor, the processor configured to: use of one or more datasets of tabular customer data to generate one or more analysis prompts; apply the one or more analysis prompts to a machine learning model to generate a vector; and generate a prioritization of the one or more customer datasets by comparing a prioritization value of the vector to threshold values.

Abstract: An autonomous machine learning (ML) system and methods are provided that are configured to intelligently cluster categorical data based on embeddings created by prompting a large language model (LLM). The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform embedding generation operations which include accessing a data set for categorical data, determining a row of the data set, generating a data container corresponding to the row and an instruction to the LLM that requests an embedding for the row, prompting the LLM to create the embedding using the data container, reducing a dimensionality of the embedding, and outputting the reduced dimensionality embedding to an ML training application executing for training an ML clustering model.