Abstract: An Artificial Intelligence (AI) & Generative AI-driven cross-domain document analysis system enables accurate and consistent narratives across a longitudinal timeline for an entity regarding communications in different operational aspects. The document analysis and insight system includes an Artificial Intelligence (AI) powered Search Interface (AIPS) and an Advanced Intelligent Knowledge Engine (AIKE). The AIPS is configured to pre-process documents from structured and unstructured data sources to generate data taxonomies and custom synonym files. The AIKE generates a preliminary evaluation of the various Large Language Models (LLMs) and uses the data taxonomies and custom synonym files to generate prompts that are configured to address limitations of the various LLMs to obtain accurate replies to user requirements.

Abstract: A document processing system processes a document image to identify document image regions including floating images, structured data units, and unstructured floating text. A first masked image is generated by deleting any floating images from the document image and a second masked image is generated by deleting any structured data units from the first masked image. The structured data units and the unstructured floating text are thus identified serially one after another. Textual data is extracted from the structured data units and the unstructured floating text by processing the corresponding document image regions via optical character recognition (OCR). Entities are extracted from the textual data using natural language processing (NLP) techniques.

Abstract: A device may receive target user category data identifying a target user, daily user data associated with the target user, real-time user data associated with the target user, and content data. The device may convert the target user category data, the daily user data, the real-time user data, and the content data into embedded target user category data, embedded daily user data, embedded real-time user data, and embedded content data. The device may process the embedded target user category data, the embedded daily user data, and the embedded real-time user data, with a neural network model, to determine a real-time user state and may determine a real-time user memory score. The device may process the embedded content data, the real-time user state, and the real-time user memory score, with the neural network model, to determine a memorability score and may perform one or more actions based on the memorability score.

Abstract: Systems and methods for deep technology innovation management by cross-pollinating innovations dataset are disclosed. A system extracts context-based keyword from an innovation dataset by transforming the innovation dataset to a vector. Further, the system searches semantically relevant keywords for the extracted context-based keyword, by extracting an entity and a key phrase from the extracted a context-based keyword. Furthermore, system clusters the vector, by identifying frequent keywords in the semantically relevant keywords to obtain cluster centroids of the frequent keywords. Thereafter, the system determines weighted keywords in each cluster using the obtained cluster centroids, and classifies the weighted keywords to identify emerging innovation trends relevant to the innovation in the innovation dataset. The system forms cohorts of innovators to explore the reuse of innovations, assets, code, and build focused monetization model.

Abstract: The present invention describes an automatic propagation material sampling system, a propagation material sampling automation process and propagation material sampling equipment. More specifically, the present invention comprises a machine for anti-contaminating captation of propagation material samples in a non-destructive way, that is, maintaining the ability of subsequent propagation of the sampled propagation material, transports and tracks the sample without its contamination with residues of other materials, as well as tracks samples with desirable characteristics. The present invention is located in the fields of agronomic engineering and automation engineering, focused on the area of sample testing automation.

Abstract: Implementations are directed to methods, systems, and apparatus for ontology-based risk propagation over digital twins. Actions include obtaining knowledge graph data defining a knowledge graph including nodes and edges between the nodes, the nodes including asset nodes representing assets and process nodes representing processes; each edge representing a relation between nodes; determining, from the knowledge graph, an aggregated risk for a first process represented by a first process node, including: identifying, for the first process node, a set of incoming nodes, each incoming node comprising an asset node or a process node and being connected to the first process node by a respective edge; determining a direct risk for the first process; and determining an indirect risk for the first process; and generating, based on the aggregated risk for the first process node, a mitigation recommendation including actions for reducing the aggregated risk for the first process node.

Abstract: Implementations are directed to receiving graph data representative of a process-aware AAG that is representative of potential lateral movement of adversaries within a computer network, receiving risk profile data representative of a risk profile of an enterprise with respect to two or more risk aspects, generating, by a process-aware risk assessment module, a risk assessment based on the process-aware AAG and the risk profile, and generating, by a mitigation simulator module, a mitigation list based on the process-aware AAG, the risk profile, and the risk assessment, the mitigation list comprising a prioritized list of two or more facts of the process-aware AAG. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support optimized product design processes. During the design process, information identifying a set of features for a product design are received and evaluated against machine learning logic to identify a set of components that includes components corresponding to the set of features. One or more candidate components may be identified as alternatives to one or more set of components based on the characteristics, and modifications to optimize (e.g., reduce cost, weight, etc.) the set of components may be determined based on at least one design metric and the one or more candidate components. A final set of components that are optimized with respect to the at least one design metric may be output.

Abstract: A computer-implemented method, system, and non-transitory, computer-readable medium that performs operations including obtaining alerts representing signals in a computing system and corresponding feedback indicators, indicating an association of the alert for the represented signal. The computing system can connect to a computing platform that includes a data mining engine. The operations include identifying a first subset of negative alerts, determining a first set of alert attributes, determining a type of model to analyze the alert attributes for signals represented by the alerts and analyzing, by the model, the first set of alert attributes to identify a subset of alert attributes with likelihoods representing alert attributes that caused the negative association of the alert. The operations include filtering the alerts to exclude a second subset of the alerts based on the likelihood of negative association, and providing for output, a set of alerts that exclude the second subset of the alerts.

Abstract: Blockchain distribution of computer data is disclosed. Computer data can comprise computer code, a computer code segment, a computer command, or a block of computer data, which can be employed by a device to patch software, change a device state, or synchronize data between devices. Blockchain distribution can provide benefits in a heterogeneous device environment, facilitate ad hoc device synchronization, and embody a distributed patch and communications network. Devices can receive a blockchain block from another device and, in some embodiments, enable other devices to access the block from the device. In some embodiments, devices can discard irrelevant blocks, however, an entire blockchain can be reconstructed where partial blockchains can be received from more than one device. Additionally, checkpoint blocks can enable devices to navigate the blockchain efficiently by skipping over known irrelevant blocks.

Abstract: A device may receive business requirements data for implementing in a cloud data fusion system, and may process the business requirements data, with a machine learning model, to generate a transformation map for transforming the business requirements data. The device may generate code for the cloud data fusion system based on the transformation map, and may identify one or more differences between the code and previous code of the cloud data fusion system. The device may modify the code based on the one or more differences and to generate modified code, and may validate the modified code for transformation logic of the cloud data fusion system to generate final code. The device may perform one or more actions based on the final code.

Abstract: This disclosure relates generally to ASR and is particularly directed to automatic, efficient, and intelligent detection of transcription bias in ASR models. Contrary to a tradition approach to the testing of ASR bias, the example implementations disclosed herein do not require actual test speeches and corresponding ground-truth texts. Instead, test speeches may be machine-generated from a pre-constructed reference textual passage according short speech samples of speakers using a neural voice cloning technology. The reference passage may be constructed according to a particular target domain of the ASR model being tested. Bias of the ASR model in various aspects may be identified by analyzing transcribed text from the machine-generated speeches and the reference textual passage. The underlying principles for bias detection may be applied to evaluation of general transcription effectiveness and accuracy of the ASR model.

Abstract: The proposed systems and methods provide a fixed set of intelligent, general APIs to manage access to enterprise data stored in a cloud-based data lake. These systems and methods allow a fixed set of APIs to respond to all queries regarding the stored enterprise data by using a cached reference table that locates the container and document in which the requested data is held. The proposed systems and methods provide a framework for a minimal API service code with the capacity for responding to dynamic queries while maintaining stringent privacy control protections.

Abstract: A device may process the surveillance video data to segment vehicles, and may utilize a segmentation guided attention network model with the vehicles to determine traffic density count data. The device may process an image segmentation map, with a regression analysis model, to derive traffic signal timing. The device may process the surveillance video data, with a deep learning model, to identify objects, and may utilize a YOLO model, with the objects, to determine object types. The device may utilize a curriculum loss model with the objects to determine crowd count data, and may process the surveillance video data, with a video analytics model, to identify first events. The device may process the surveillance video data, with a classifier and deep network models, to identify second events, and may process the determined information, with a dynamic text-based explanation model, to generate a text-based explanation and/or a failure prediction.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support optimization of communications transmitted over a plurality of communication mediums. Historical communications data may be analyzed to identify clusters of users and a model may be constructed based on the clusters. Candidate sequences of communications over a period of time (e.g., sequences of communication successfully triggering events) are identified using metrics (e.g., probabilities, attribution penalties, etc.) derived from the model or other information. The candidate sequences of communications may be determined at a group or cluster level and then tuned or optimized (e.g., using transition sequences, harmonization, entity priors, etc.) for individual users to produce optimized sequences of communications. The optimized sequences of communications may then be transmitted to individual users according to each user's optimized sequence of communications.

Abstract: Embodiments provide systems, method, and computer-readable storage media for performing robotic tasks in a distributed and coordinated manner. A fleet of robots may use a sequence of messages to appoint supervisors for a set of tasks, where the supervisor robots are responsible for ensuring that their supervised tasks are completed by other robots of the fleet. The supervisors may solicit requests from other robots to perform available tasks and select a robot to perform an available task. Once a robot is appointed, the supervisor and the worker may use messaging sequences to monitor the status of the task and participating robots (e.g., the supervisor and the worker). The monitoring may enable the supervisor to detect a failed worker and enable other robots to detect a failed supervisor.

Abstract: A contract platform may receive, from data structures, historical contract data; read, from the historical contract data, numerical categorical data; process the numerical categorical data, with one or more artificial intelligence models, to convert the numerical categorical data into vector format; generate a taxonomy in vector format based on the converted numerical categorical data; read, from the historical contract data, nonnumerical noncategorical data; process the nonnumerical noncategorical data, with the models, to convert the nonnumerical noncategorical data into vector format; generate a knowledge graph in vector format based on the converted nonnumerical noncategorical data.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support detection and mitigation of defects occurring in outputs of a process. A modelling engine creates and trains models to identify defects occurring in outputs of a process or system. A set of causation rules may be created and trained to identify causes of different defects identifiable via the models and a set of control rules may be created and trained to generate control data that may mitigate the causes of identified defects. A process monitoring device incorporating the trained models and rules may then be used to monitor the process and detect defects using the models. Once a defect is detected, the rules may be applied to determine the cause of the defect and then control data may be generated to modify the process such that further occurrences of the defect are reduced or eliminated.

Abstract: In some implementations, a system may determine, based on a qualification model, a prediction output of an analysis of user information. The system may determine, based on a generator model, a plurality of counterfactual explanations associated with the prediction output and the user information. The system may cluster, according to a clustering model, the plurality of counterfactual explanations into clusters of counterfactual explanations. The system may select, based on a classification model, a counterfactual explanation from a cluster of the clusters of counterfactual explanations. The system may provide a request for feedback associated with the counterfactual explanation. The system may receive feedback data associated with the request for feedback. The system may update a data structure associated with the clustering model based on the feedback data and the counterfactual explanation to form an updated data structure. The system may perform an action associated with the updated data structure.

Abstract: The present disclosure relates to a system, a method, and a product for intent discovery. The system includes a processor in communication with a memory storing instructions. When the processor executes the instructions, the instructions are configured to cause the processor to: obtain documents comprising a set of utterances, extract the set of utterances from the documents, generate a set of utterance embeddings based on the set of utterances, clusterize the set of utterance embeddings to obtain a plurality of clusters, obtain a cluster label for each cluster, encode each document based on a number of times each utterance cluster identifier (ID) appears to obtain an encoded document, perform latent Dirichlet allocation (LDA) on the encoded documents to obtain K topics, and each topic corresponding to a list of key clusters with cluster IDs, and for each topic, replace the cluster IDs with the cluster labels.