Abstract: Here is schema generation from a data table and reference documentation, including inferential analysis and generation of natural language. A large language model (LLM) inferentially generates an initial column summary that consists of natural language that describes exactly one column of a data table. From a vector store that contains many fixed-size portions of many reference documents, a few nearest neighbor portions are dense retrieved that are semantically similar to the initial column summary. From the initial column summary and the nearest neighbor portions, the LLM inferentially generates an expanded column summary that describes the column, including inferring that some nearest neighbor portions should be excluded. From the expanded column summary, the LLM inferentially generates a) a data schema that consists of semi-structured text that describes the data table, including a column comment that contains natural language that describes the column.

Abstract: Methods and systems for providing customized services to users of data processing systems are disclosed. To provide the customized services, a query may be obtained from a user. Contextual information for the query may be obtained by sequentially searching different tiers of information in a first data source, the different tiers being searched in an order defined by ascribed levels of importance specified by the user to the different tiers until the contextual information meets contextual information criteria, at least one portion of the contextual information being stored in the first data source in accordance with a retention plan that limits durations of time that the at least one portion of contextual information is associated with the different tiers. The query may be serviced using the contextual information and the query as input to an artificial intelligence model to obtain an output usable to facilitate provisioning of computer-implemented services.

Abstract: The system generates a recommendation of content for use in collaboration, allowing relevant content to be used as base content. The system identifies a content item, and retrieves reviews for the content item from one or more sources or forums. The system filters the reviews to generate a reduced set of reviews based on text of the respective reviews, profile information associated with the reviews, and reference information. A recommendation metric is determined for the content item based on the reduced set of reviews and based on the one or more recommendation criteria. The recommendation criteria specify which aspects of the content impact recommendation, and how those aspects impact recommendation. The recommendation metric indicates whether the content item is recommended as base content, to be used for generating collaborative content. The system generates a recommendation indicator indicative of the recommendation metric, and outputs the indicator for display, storage, or both.

Abstract: Disclosed is a two-dimensional barcode-based gym equipment information system, comprising: a plurality of gym equipment, a plurality of two-dimensional barcode labels, and a web server. The web server hosts a plurality of gym equipment information web pages, each storing gym equipment information about the corresponding gym equipment. The gym equipment information web pages are respectively linked to the two-dimensional barcode labels. A user can scan the two-dimensional barcode label on the gym equipment using a mobile device to access the corresponding gym equipment information web page and conveniently obtain the corresponding gym equipment information.

Abstract: Provided are systems, methods, and computer program products for generating a structured document. The system includes a computing device configured to extract a plurality of information prompts from a document, display, in an interactive graphical user interface, the plurality of information prompts and at least one selectable option configured to modify the plurality of information prompts, verify the plurality of information prompts, in response to verifying the plurality of information prompts, automatically retrieve a plurality of data elements corresponding to the plurality of information prompts from a plurality of different data sources, generate at least one inference request based on the plurality of information prompts and the plurality of data elements, and generate a structured document based on processing the at least one inference request with at least one machine-learning model.

Abstract: A deployment infrastructure for sharing usage metrics with customers is described. An example method includes transforming a usage view file into an anonymous block build file. The usage view file corresponds to a view of customer usage data. The method includes embedding the anonymous block build file into a task, and executing the task to produce a share object that is based on the anonymous block build file. The method includes deploying the share object to a customer account, which enables the customer account to access the view of the customer usage data.

Abstract: A BCI-based music inferring method according to an embodiment of the present disclosure is a BCI-based music inferring method in which at least part of each operation is performed by a processor, and includes receiving brain signal data of a subject imagining a melody, and estimating a melodic contour of the melody on the basis of the brain signal data.

Abstract: A method for capturing and processing field data in construction materials testing and inspection workflows uses a field AI Field Assistant to integrate data from various sources, such as testing machines, photographs, and AI-generated outputs. The method first collects field sample test data, which is then scrubbed with an AI module to remove anomalies and normalize formats. Human voice input is used to verify a portion of the scrubbed data, creating a validated data set that feeds into the field AI Field Assistant. The Field Assistant guides users through work order and test selections, dynamically generates data entry prompts, and captures voice-driven data that is converted to text. The system securely records and stores this information, maintains an audit trail with timestamps and user identification, and synchronizes data with a central web application.

Abstract: Described are techniques for back-calculating one or more values of a new, real-time aggregate before sufficient data to calculate the new, real-time aggregate has been collected, wherein the back-calculating is based on data collected for one or more aggregates that have been executing prior to start of execution of the new, real-time aggregate.

Abstract: Techniques for retrieving and presenting content items using a fuzzy cache are presented herein. A computing system can include a fuzzy cache having a plurality of cluster identifiers. Each cluster identifier in the plurality of cluster identifiers can have a result associated with a previously executed request. Additionally, the computing system can include a first machine-learned model configured to generate a vector embedding, and a second machine-learned model configured to generate a cluster identifier. Moreover, the system can receive, from a user device, a first request having a first feature. Furthermore, the system can process, using the first machine-learned model, the request to generate a first vector embedding associated with the first request. Subsequently, the system can process, using the second machine-learned model, the first vector embedding to determine a first cluster identifier. The system can retrieve, from the fuzzy cache, a first result on the first cluster identifier.

Abstract: A computer implemented method includes maintaining information indicative of whether a data replication relationship between a dataset associated with the local CG (CG1) and a mirror copy of the dataset stored on a remote CG (CG2) of a remote distributed storage system is in an in-synchronization (InSync) state or an out-of-synchronization (OOS) state, determining whether a CG storage expansion process is in progress, and in response to determining the OOS state and whether the primary storage site has a failure, initiating an automatic unplanned failover (AUFO) workflow without manual intervention on the original volumes in the CG1 of a first storage cluster of the primary storage site and the original volumes in the CG2 of a second storage cluster when the CG storage expansion process is in progress with a new source volume to be a member of CG1 and a new destination volume to be a member of CG2.

Abstract: Computing systems methods, and non-transitory storage media are provided for receiving a first representation of an unstructured data entity. The first representation includes an indication of a detection. The unstructured data entity is part of a corpus. Next, second representations of the unstructured data entity are received and resolved according to a consensus. Next, any discrepancies between the first representation and the resolved second representations are determined. The any discrepancies include any difference in an existence or an absence of the detection, in a relative position of the detection, or in a type or a classification of the detection. Next, feedback regarding the any discrepancy is received. Next, the first representation is selectively modified, or selectively prompted to be modified, based on the any discrepancy and the feedback.

Abstract: A method and apparatus for semanticization is provided. The method includes: ascertaining a target coordinate of a to-be-semanticized location; ascertaining, through a pre-built regional spatial index tree, a target region to which the target coordinate of the to-be-semanticized location belongs; and ascertaining semantic information of the to-be-semanticized location based on semantic information of the target region.

Abstract: Bitmask encoding-based personalized style generation is described. A personalization system presents different instances of digital content in a user interface and prompts a user to indicate interest or disinterest in an individual digital content items. Based on the input indicating interest or disinterest, a bitmask encoding is generated and returned to the personalization system. The personalization system queries a database to identify respective instances of digital content that correspond to each binary value included in the bitmask encoding, which is then used to generate a prompt that causes a machine learning system to generate a personalized style for the user from which the favorable or unfavorable indications were received. The machine learning system generates a personalized style for an individual and identifies different instances of digital content of interest to the individual, and the personalized style is used to generate a user interface for the individual.

Abstract: An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes various executable nodes that communicate with each other by way of messages. The executable graph-based model further includes a message node for each message associated with the overlay system. Each message node is associated with one or more analytics overlay nodes that execute a corresponding set of analytics operations on composition of the corresponding message node to generate one or more analytic insights. The composition includes data and transactional information associated with the corresponding message node. A publisher overlay node associated with the one or more analytics overlay nodes may generate and publish an analytics outcome based on an output of the execution of the set of analytics operations. The analytics outcome is indicative of performance of the overlay system.

Abstract: Systems and methods are disclosed for a resource efficient partial bootstrap of one or more databases. Instead of the typical requirement of deleting all data from a destination database and performing a full bootstrap when deletions in data at a data source occurs, implementations herein disclose a resource efficient partial bootstrap process to identify deletions in data at the data source and update the destination database accordingly without requiring a full bootstrap. To perform partial bootstrapping, a system compares the set of data keys from a source index at the data source with the set of data keys from a destination index at the destination database to identify a difference between the two sets of data keys. The system then deletes the data keys in the destination index that do not appear in the source index (as identified in the difference between the two sets of data keys).

Abstract: A replicated block storage service provides durable and high performance network-attached storage replicated in two or more zones of a single region, and remains available despite a single zone failure. A probe file is generated to determine a health state of a replicated disk. When a disk is degraded, a lease is created indicating which replica is trusted and providing visibility to backend jobs to facilitate reconciliation of data between the first replica and the second replica. Moreover, degraded file markers are generated for use by the backend jobs in quickly identifying the data to be copied.

Abstract: Systems, methods, and apparatuses for query-specific ranking of search results are described herein. A first query may be received from a user device. A set of candidate results associated with the first query may be determined. The set of candidate results may be ranked based on inputting the set of candidate results in a machine learning model. The machine learning model may be trained using data indicating previously selected candidate results output in response to the first query and previously selected candidate results output in response to a second query. The second query may comprise the first query. Output, via the user device, of an indication of the ranked candidate results may be caused.

Abstract: Disclosed are methods, systems, and non-transitory computer readable memory for processing, analyzing, and visualizing complex digital object sets using large language models. For instance, a method may include receiving a set of digital objects; indexing the received set of digital objects to generate indexed data; generating a timeline prompt based on the indexed data; processing the timeline prompt to generate a timeline response; and outputting the timeline response as an interactive timeline of based on the set of digital objects.

Abstract: A device may receive a query identifying one or more events to be captured by a camera associated with a vehicle, video data associated with the vehicle, and location data associated with the vehicle, and may generate a prompt requesting captions of video frames included in the video data. The device may process the video data, with a large language model and based on the prompt, to generate the captions of the video frames included in the video data, and may process the query and the captions, with a phrase model, to determine categories of words provided in the query and the captions. The device may calculate matching scores between the query and the captions based on the categories of words and a dictionary of words, and may identify an event based on the matching scores. The device may perform one or more actions based on the event.