Pwc Product Sales LLC

Pricewaterhousecoopers Llp

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Joseph David Voyles

Chung-Sheng Li

Winnie Cheng

Scott Likens

Mark Paich

Thomas Joseph Foth

Todd Christopher Morrill

Yuan Wan

Joseph Doyle Harrington

Jonathan B Rhine

Saverio Fato

Alexander Nicholas Boldizsar

Paul Sheward

William Spotswood Seward

Lyle Wallis

Ilana Alexandra Golbin

Kris Douglas Kersey

Bo Li

Yifeng Wang

Samuel Pierce Burns

Anand Srinivasa Rao

Kevin Ma Leong

Mark John Flavell

Nancy Alayne Lizotte

Chakkarapani Vijayakumar

Vasudeva Sankaranarayanan

Raymund Anthony Florand Beltran

Matthew Lawrence Messina

Kazi Islam

Saurabh Mohanty

Gaurav Kumar

Craig Sharples

Matthew F Connelly

Xiaoying Chen

Waqar Sarguroh

Lacey A Woolf

Kanak Dahake

Nicholas John Hamer

Rhodri Davies

Jeffrey Kevin Dicker

Luisa Breidt

Ali Rusta-sallehy

Mansi Arora

Zhen Qi

Thomas Vincent Giacomucci

Timothy Marco

Sindy Ma

Kyungha Lim

Kevin Paul

Ilana Golbin

An entity trust index relating to an entity may indicate a trustworthiness level of the entity. To generate an entity trust index, entity information may be received from one or more information sources. Using the entity information and one or more machine learning models, a performance index, an innovation index, and a sustainability index for the entity may be generated. The entity trust index may be generated based on the performance index, the innovation index, and the sustainability index. One or more actions for the entity that will modify the entity trust index may then be determined.

Systems and methods for measuring trust

The present disclosure relates generally to storing computer models, and more specifically to a platform for achieving replicability of a computer model (e.g., a trained machine-learning algorithm) by storing and providing access to data associated with the computer model using an immutable and decentralized ledger system (e.g., a blockchain ledger) and a distributed database. An exemplary computer-enabled method for storing a computer model, the method comprises: receiving data associated with the computer model; generating one or more asset files based on the data associated with the computer model; generating one or more hash values corresponding to the one or more asset files; generating one or more of location trackers corresponding to the one or more asset files; generating a ledger entry comprising the one or more hash values and the one or more location trackers; and adding the ledger entry to a blockchain ledger.

Immutable and decentralized storage of computer models

A system for automatically generating responses to user queries includes one or more processors, operable when executing instructions to: receive a query from a user, determine a first set of similarity scores between the query and a plurality of stored queries stored in a data store, and determine whether a first similarity score of the first set of similarity scores meets a first threshold. In accordance with a determination that the first similarity score meets the first threshold, the processors are further operable to obtain a set of case information for the current case; retrieve case information for at least one precedent case; determine a second set of similarity scores between the current case and the at least one precedent case; determine whether a second similarity score of the second set of similarity scores meets a second threshold; and generate a response to the query.

Method and apparatus for an AI-assisted virtual consultant

Provided herein are methods and systems for identifying a related data values in a plurality of datasets. The method can include extracting data from the plurality of datasets, identifying potential linking categories, and determining a validity of the potential linking categories. One or more linking categories can be selected from the potential linking categories based on the validity, and related data values can be identified between the plurality of datasets based on the selected linking categories. Also provided herein are methods and systems for classifying data values as reconciled or non-reconciled. The method can include identifying a plurality of related data values in a plurality of datasets. For each of the plurality of related data values, a similarity score and confidence score can be determined, and the plurality of related data values can be classified as reconciled or non-reconciled based on the similarity score and/or the confidence score.

AI-augmented composable and configurable microservices for record linkage and reconciliation

A system for processing relational database data using a generative artificial intelligence (AI) language models is provided. The system receives relational database data and generates, based on the relational database data, schema data. The system also receives a user query input. The system generates and provides a first prompt to a generative AI language model, based on the user query input and the schema data. The system responsively receives, from the generative AI language model, an indication of a first subset of the database data identified as relevant to the query. The system generates and provides a second prompt to the generative AI language model, based on the user query input and the identified first subset. The system responsively receives, from the generative AI language model, an output comprising insight data generated based on the user query input and the identified first subset of the database data.

Systems and methods for relational database insight extraction and visualization

An exemplary system for constructing data structures that can perform inferential reasoning to answer input queries may receive input data, extract and cluster entities in the input data into topic clusters, and for a first topic cluster construct a data structure comprising a plurality of nodes, wherein nodes of the data structure respectively represent a topic entity extracted from the input data and grouped into the first topic cluster, and wherein a first node of the data structure is associated with a second node of the data structure based on the first node and the second node respectively representing a first topic entity and a second topic entity associated in the input data with a common one of the one or more identified linguistic modalities. An exemplary system comprising the data structure may receive an input query and generate a response to the input query using the data structure.

Method and apparatus for knowledge representation and reasoning in accounting

System and methods for generating synthetic data according to a plurality of options are provided. According to a first option, synthetic data is generated based on an indication of one or more target distributions. According to a second option, a one or more target distributions are determined based on a sample data set, and synthetic data is generated based on the determined target distribution(s). According to a third option, a temporary synthetic data set is generated based on an indication of one or more target distributions, matches between the temporary synthetic data set and a sample data set are then identified, and a synthetic data set is then generated based on the identified matches. A system may automatically determine which option to leverage based on whether a user provides a sample data set, one or more target distributions, or both.

Systems and methods for generating synthetic data

Systems and methods for generating and utilizing an interactive causal loop diagram using a causal loop designer are provided. In one or more examples, a computer-implemented method for creating a causal loop diagram comprising visually emphasized elements can comprise displaying a first element and a second element, wherein the first element and the second element comprise visually emphasizable elements. In response to receiving a user command to connect the first element to the second element, the method can comprise displaying a connection between the first element and the second element, wherein the connection comprises a visually emphasizable element. In one or more examples, the method comprises displaying a causal loop diagram comprising the first element, the second element, and the connection. In response to a user activating a visual emphasis tool, the method can comprise visually emphasizing one or more of the visually emphasizable elements.

Systems and methods for generating and utilizing an interactive causal loop diagram using a causal loop designer

Described are system, method, and computer-program product embodiments for developing an object detection model. The object detection model may detect a physical object in an image of a real world environment. A system can automatically generate a plurality of synthetic images. The synthetic images can be generated by randomly selecting parameters of the environmental features, camera intrinsics, and a target object. The system may automatically annotate the synthetic images to identify the target object. In some embodiments, the annotations can include information about the target object determined at the time the synthetic images are generated. The object detection model can be trained to detect the physical object using the annotated synthetic images. The trained object detection model can be validated and tested using at least one image of a real world environment. The image(s) of the real world environment may or may not include the physical object.

Systems and methods for training image detection systems for augmented and mixed reality applications

A system for vehicle resource management with a memory, controller, and vehicle. The memory includes instructions. The controller executes the instructions. The vehicle includes a vehicle system, controls device, and can communicate with the controller. The vehicle system generates resource data for a replenishable resource. The controls device commands the vehicle to perform tasks. Moreover, the instructions enable the controller to: receive resource data after a first task; provide a first status for the vehicle when the resource data reflect the resource meets a threshold requirement, or otherwise provide a second status; generate and communicate a first output after the first status is provided, the first output instructing the vehicle to reposition itself to perform a second task; and generate and communicate a second output after the second status is provided, the second output instructing the vehicle to reposition itself to replenish the resource to meet the threshold requirement.

Vehicle resource management system and method

Growing community of inventors

Louisville, KY, United States of America

Joseph David Voyles