Abstract: A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document. A plurality of chunk embeddings are generated from the plurality of chunks. A query is processed using a generative artificial intelligence (AI) model. A query embedding is generated from the query. A plurality of candidate chunk embeddings are identified from the plurality of chunk embeddings based upon, at least in part, a chunk size and a chunk similarity score associated with each chunk and a performance metric associated with the query. A prompt is generated using the query embedding and the plurality of candidate chunk embeddings. The prompt is provided to the generative AI model.

Abstract: Systems and methods for scraping detection include a device which receives a plurality of requests from a client to extract data from a resource. The device may classify activity of the client as activity of an autonomous program based at least on a number of the plurality of requests, and one of i) one or more content types of the requests, or ii) a frequency in which the requests are received. The device may block, responsive to classification of the activity, a subsequent request from the client to extract data from the resource.

Abstract: State of the art systems used for industrial plant monitoring have the disadvantage that they fail to correctly assess reason for dip in performance of the plant and in turn trigger appropriate corrective measures. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a system and method for development and deployment of self-organizing cyber-physical systems for manufacturing industries. The system monitors and collects data with respect to various parameters, from the industrial plant. If any performance dip is detected, the system determines corresponding cause, and also triggers one or more corrective actions to improve performance of the plant and different plant components to a desired performance level.

Abstract: An apparatus comprises at least one processing device configured to obtain a query comprising search text and a context identifying documents to be searched. The processing device is also configured to generate document chunks by parsing the documents, each document chunk comprising a portion of content of the documents, and to determine chunk boosting factors for the document chunks based on document formatting of textual elements within the document chunks. The processing device is further configured to select a subset of the document chunks based on determining a similarity between content of the document chunks and the search text using the determined chunk boosting factors, to generate a prompt for input to a machine learning system comprising the selected subset of the document chunks, to apply the prompt to the machine learning system, and to provide an answer to the query based on an output of the machine learning system.

Abstract: Energy storage device assemblies comprising shaped energy storage devices (e.g., a sticker battery) and adhesive layers are disclosed. The adhesive layer may include a mounting adhesive, and may further include a conductive adhesive. The energy storage device assemblies may be mounted onto and electrically connected to a device in need of an energy storage device. Methods of fabrication and methods of use thereof are also disclosed.

Abstract: In traditional systems, every time a digital twin of a component needs to be generated, behavioral as well as operational data specific to the component needs to be fetched, which has practical difficulties owing to complex nature of processes/equipment the component is associated with. The disclosure herein generally relates to building digital twins, and, more particularly, to a method and system for building digital twin by leveraging existing knowledge. The system determines extent of similarity between two components, and based on the determined extent of similarity, uses different approaches to retrain a ANN data-driven model to obtain a desired accuracy for features of the component for which the digital twin is being generated.

Abstract: A method, computer program product, and computing system for processing a plurality of historical input/output (IO) requests associated with a storage object of a storage system. A plurality of IO features may be generated using the plurality of historical IO requests. An active data set for the storage object may be forecasted for a particular future time interval using a machine learning model based upon, at least in part, the plurality of IO features.

Abstract: A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document, wherein the document includes the plurality of text portions and a plurality of images. Each chunk is indexed using a word embedding. Each of the plurality of images is indexed based upon, at least in part, a position of a respective image relative to a corresponding chunk. An image placeholder is generated for each of the plurality of images. A plurality of image-enhanced embeddings is generated by inserting the image placeholder for each of the plurality of images into a respective word embedding for the corresponding chunk. The plurality of image-enhanced embeddings are provided for processing a query using a generative artificial intelligence (AI) model.

Abstract: A UE may identify an offset period associated with an NTN cell based on a SIB. The UE may identify a TA value associated with the NTN cell for the UE based on a location of the UE and a location of a satellite of the NTN cell. The UE may identify whether the TA value associated with the NTN cell for the UE is less than the offset period associated with the NTN cell. The UE may block the NTN cell for a first duration or until a reboot of the UE if the TA value associated with the NTN cell for the UE is greater than the offset period associated with the NTN cell.

Abstract: A method, computer program product, and computing system for identifying a plurality of headings from a document by processing a hierarchical structure associated with the document including the plurality of headings and a plurality of content portions within the plurality of headings. A plurality of respective chunks are generated using the plurality of headings and a prompt size limitation associated with a prompt of a generative artificial intelligence (AI) model. The plurality of respective chunks are provided for generating a prompt for the generative AI model.

Abstract: A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document. A plurality of chunk embeddings are generated from the plurality of chunks. A query is processed using a generative artificial intelligence (AI) model. A query embedding is generated from the query. A plurality of candidate chunks are identified from the plurality of chunks based upon, at least in part, a similarity between the plurality of chunk embeddings and the query embedding. An amount non-overlapping content of each candidate chunk is determined relative to each other candidate chunk. A subset of the plurality of candidate chunks are selected for inclusion in a prompt with the query based upon, at least in part, the amount of non-overlapping content of each candidate chunk.

Abstract: A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document. A plurality of chunk summaries are generated by generating a summary for each respective chunk of the plurality of chunks. A plurality of chunk summary embeddings are generated by generating an embedding of the summary for each respective chunk. The plurality of chunk summary embeddings are provided for processing a query using the generative AI model.

Abstract: A method, computer program product, and computing system for processing a query using a generative artificial intelligence (AI) model. A topic of the query is extracted. A weighting for the topic of the query is generated. A weighted query topic embedding for the topic of the query is generated. A candidate chunk is identified from a plurality of chunks of a target document by determining a similarity between the weighted query topic embedding and a plurality of chunk embeddings for the plurality of chunks. A prompt is generated using the query and the candidate chunk. The prompt is provided to the generative AI model.

Abstract: A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document. A plurality of chunk embeddings are generated from the plurality of chunks. A query is processed using a generative artificial intelligence (AI) model. A query embedding is generated from the query. A plurality of candidate chunk embeddings are identified from the plurality of chunk embeddings based upon, at least in part, a chunk size and a chunk similarity score associated with each chunk and a performance metric associated with the query. A prompt is generated using the query embedding and the plurality of candidate chunk embeddings. The prompt is provided to the generative AI model.

Abstract: A method, computer program product, and computing system for processing a plurality of input/output (IO) requests associated with a storage object in a storage system. A plurality of IO features are generated using the plurality of IO requests associated with the storage object. A time dependent IO feature is identified from the plurality of IO features. A coefficient for the time dependent IO feature for the storage system is extracted. The time dependent IO feature is calibrated using the coefficient for the time dependent IO feature for the storage system relative to the time dependent IO feature from at least one other storage system.

Abstract: A method, computer program product, and computing system for processing a plurality of input/output (IO) requests associated with a plurality of storage objects of a storage system. A plurality of IO features may be generated using the plurality of IO requests including one or more of: a percentage of overwrite IO requests, a percentage of sequential read IO requests, and a percentage of sequential write IO requests. The plurality of IO features may be processed using a machine learning model. A ransomware attack may be monitored for on the storage system in real-time based upon, at least in part, the processing of the plurality of IO features using the machine learning model.

Abstract: A method, computer program product, and computing system for processing a plurality of historical input/output (IO) requests associated with a plurality of storage objects of a storage system from a plurality of time intervals. The plurality of storage objects may be divided into a plurality of storage activity classes using a classification-based machine learning model and the plurality of historical IO requests. A next access time for each storage object may be forecasted based upon, at least in part, the plurality of storage activity classes.

Abstract: State of the art techniques used for Flue Gas Desulpharization (FGD) process monitoring fail to comprehend the relationship between various process parameters, which is crucial in determining the performance of a FGD process being monitored. The disclosure herein generally relates to industrial process monitoring, and, more particularly, to a method and system for performance optimization of Flue Gas Desulphurization (FGD) Unit. The system identifies Key Performance Indicators (KPIs) associated with the process being monitored, and identifies parameters associated with each KPI. This information is used to generate several predictive models, from which a predictive model having the highest value of composite model score amongst the predictive models is selected as the predictive model for processing the input data, which is then used to perform optimization of the FGD process.

Abstract: A method, computer program product, and computing system for identifying a plurality of headings from a document by processing a hierarchical structure associated with the document including the plurality of headings and a plurality of content portions within the plurality of headings. A plurality of respective chunks are generated using the plurality of headings and a prompt size limitation associated with a prompt of a generative artificial intelligence (AI) model. The plurality of respective chunks are provided for generating a prompt for the generative AI model.

Abstract: A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document, wherein the document includes the plurality of text portions and a plurality of images. Each chunk is indexed using a word embedding. Each of the plurality of images is indexed based upon, at least in part, a position of a respective image relative to a corresponding chunk. An image placeholder is generated for each of the plurality of images. A plurality of image-enhanced embeddings is generated by inserting the image placeholder for each of the plurality of images into a respective word embedding for the corresponding chunk. The plurality of image-enhanced embeddings are provided for processing a query using a generative artificial intelligence (AI) model.