In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

Disclosed herein are embodiments of systems, methods, and products comprising a data power server for secure storage and retrieval of trade data. The server receives a request from a user to review or confirm one or more trade documents via a webserver. The server communicates with a connector grid server to retrieve the user's accessible documents. The connector grid server determines the electronic file IDs that are accessible to the user based on the accessibility policy. The server instructs a digital library server to download the electronic files containing the requested trade data. The digital library server retrieves and downloads the electronic files based on the file IDs. The webserver renders a GUI displaying the electronic files on an electronic client device operated by the user. Upon receiving the confirmation response from the user, the server instructs the digital library server to update the trade status.

This disclosure relates to methods, non-transitory computer readable media, and systems that provide an attribution user interface that integrates attribution models as native components within the interface to configure analytics visualizations. By integrating attribution models and corresponding functions as native components of a user interface, the disclosed methods, non-transitory computer readable media, and systems can implement attribution models as parameters of attribution distributions or of any attribution visualizations, where the attribution models function as event categories. For instance, the disclosed methods, non-transitory computer readable media, and systems can provide analytics tools to generate visualizations of different attribution distributions of events across dimension values (or other visualizations) based on different attribution models. In some implementations, the disclosed methods, non-transitory computer readable media, and systems can also modify an attribution-distribution visualization extemporaneously given user inputs for a new event category, new dimension, new segment, or other parameter for the visualization.

An unsupervised multi-hop search across a corpus of documents in a database or other data resource permits the identification of relationships between two entities mentioned in the corpus in cases where the two entities are not co-mentioned within any documents in the corpus (or not mentioned within document(s) with sufficient frequency or proximity to infer the relationship). The search can employ a beam search algorithm anchored by word embeddings and an A* graph traversal to calculate semantic distance between the entities as different paths through the corpus for different entity co-mentions are evaluated.

A Codex system of computers linked into a neural network continuously scans and gathers information from, understands, and interacts with, an environment, an optimizer software executing software instructions based on rules of grammar and semantics searches a encyclopedia of human knowledge to transform input into a search pattern. Then the Codex monetizes and commercializes each transformed input and corresponding optimal output. An artificial intelligence interaction software, hereinafter referred to as virtual maestro, uses the search pattern and optimal output to interact and engage scripted communication with the end user.

A computer-implemented method, includes obtaining unstructured data content from one or more data sources; extracting analytics associated with the unstructured data content; determining presentation parameters for presenting the unstructured data content based on the extracted analytics; and displaying data representing the unstructured data content in a map view based on the presentation parameters.

Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving a source document, applying one or more pre-processes to the source document to produce contextual information representative of the structure and content of the source document, and transforming the source document, based on the contextual information, to generate a question-and-answer searchable document.

In accordance with some aspects of the present disclosure, an apparatus is disclosed. In some embodiments, the apparatus includes a processor and a memory. In some embodiments, the memory includes programmed instructions that, when executed by the processor, cause the apparatus to receive, from a backend, content represented as unstructured data, parse the content into content groups, wherein each content group has common characteristics, map each content group to a defined field based on the common characteristics of the content group, generate structured data in which each content group complies with constraints of the corresponding defined field, and send the structured data to a frontend to be displayed on a website.

A system and method for automatically extracting and visualizing topics and information from a database of unstructured text documents. The method including: mapping each text document in the database into a latent vector in a latent space using a trained machine learning model; receiving a query from a user; mapping the query to the latent space; determining a predetermined set of text documents in the document database nearest to the query using a similarity metric on the latent vectors of each document; using a trained clustering machine learning model, determining cluster labels for the query and the set of the documents nearest to the query, the clustering labels representative of topics; and displaying a visualization of the query, the documents nearest to the query, and the cluster labels.

At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.