Kevin W Leong, Age 58200 Rector St, New York, NY 10280

2023000, Jan 5, 2023

Jun 30, 2022

17/854337

- New York NY, US
Winnie CHENG - West New York NJ, US
Mark John FLAVELL - Madison NJ, US
Lori Marie HALLMARK - Xenia OH, US
Nancy Alayne LIZOTTE - Saline MI, US
Kevin Ma LEONG - Randolph NJ, US

PricewaterhouseCoopers LLP - New York NY

G06F 16/35
G06F 16/33

Systems and methods for adjudicating AI-augmented automated analysis of documents in order to quickly and efficiently make various adjudications based on the documents are provided, including adjudications as to whether the documents represent underlying data that meets one or more predefined or dynamically-determined criteria. Criteria for adjudication may include commercial-substance criteria, related-party-transaction criteria, and/or collectability criteria. A system may receive a plurality of documents and generate a plurality of feature vectors by applying natural language processing techniques. The system may apply one or more classification models to the plurality of feature vectors to generate output data classifying each of the feature vectors. The system may identify, for each feature vector, a subset of closest matching prior feature vectors. Based on the classification and based on the identified subset, the system may adjudicate each feature vector with respect to commercial substance, including an adjudication classification and an adjudication confidence score.

2023000, Jan 5, 2023

Jun 30, 2022

17/854348

- New York NY, US
Winnie CHENG - West New York NJ, US
Mark John FLAVELL - Madison NJ, US
Lori Marie HALLMARK - Xenia OH, US
Nancy Alayne LIZOTTE - Saline MI, US
Anand Srinivasa RAO - Lexington MA, US
Kevin Ma LEONG - Randolph NJ, US
Di ZHU - Jersey City NJ, US
Timothy DELILLE - New York NY, US
Maria Jesus Perez RAMIREZ - New York NY, US
Yuan WAN - Irvine CA, US
Ratna Raj SINGH - Mumbai, IN
Vishakha BANSAL - Meerut, IN
Shaz HODA - New York NY, US
Amitoj SINGH - New Delhi, IN
Siddhesh Shivaji ZANJ - Mumbai, IN

PricewaterhouseCoopers LLP - New York NY

G06F 16/93

Systems and methods for automated document processing for use in AI-augmented auditing platforms are provided. A system for determining the composition of document bundles extracts substantive content information and metadata information from a document bundle and generates, based on the extracted information regarding a composition of the document bundle. A system for validating signatures in documents extracts data representing a spatial location for respective signatures and generates a confidence level for respective signatures, and determines, based on location and confidence level, whether signature criteria are met. A system for extracting information from documents applies a set of data conversion processing steps to a plurality received documents to generate structured data, and then applies a set of knowledge-based modeling processing steps to the structured data to generating output data extracted from the plurality of electronic documents.

2023000, Jan 5, 2023

Jun 30, 2022

17/854352

- New York NY, US
Winnie CHENG - West New York NJ, US
Mark John FLAVELL - Madison NJ, US
Lori Marie HALLMARK - Xenia OH, US
Nancy Alayne LIZOTTE - Saline MI, US
Kevin Ma LEONG - Randolph NJ, US
Kevin Michael O'ROURKE - New York NY, US
Robert Michael HILL - Glassboro NJ, US
Timothy DELILLE - New York NY, US
Maria Jesus Perez RAMIREZ - New York NY, US
Thomas Vincent GIACOMUCCI - Malvern PA, US

PricewaterhouseCoopers LLP - New York NY

G06N 5/04
G06N 5/02

Systems and methods for providing explainability for processing data through multiple layers are provided. An input layer is configured to receive an evidence data set comprising a plurality of evidence items, apply evidence processing models to the evidence data set to generate evidence understanding data, and generate input-layer explainability data, wherein the input-layer explainability data represents information about the processing of the evidence data set by the input layer. A presentation layer is configured to receive data (the evidence understanding data and/or data generated based on the evidence understanding data), apply one or more presentation generation models to the received data to generate presentation data, and generate presentation-layer explainability data for presentation to the user, wherein the presentation-layer explainability data represents information about the processing of the received data set by the presentation layer.

2023000, Jan 5, 2023

Jun 30, 2022

17/854338

- New York NY, US
Winnie CHENG - West New York NJ, US
Mark John FLAVELL - Madison NJ, US
Lori Marie HALLMARK - Xenia OH, US
Nancy Alayne LIZOTTE - Saline MI, US
Kevin Ma LEONG - Randolph NJ, US

PricewaterhouseCoopers LLP - New York NY

G06Q 10/06
G06Q 30/00

A system for generating risk assessments based on a data representing a plurality of statements and data representing corroborating evidence is provided. The system receives data representing a plurality of statements and data representing corroborating evidence. The system applies one or more integrity analysis models to the first data and the second data in order to generate an assessment of a risk that one or more of the plurality of statements represents a material misstatement. A system for generating an assessment of faithfulness of data is provided. The system compared data representing a statement to data representing corroborating evidence, and generates a similarity metric representing their similarity. Based on the similarity metric, the system generates an output representing an assessment of faithfulness of the first data set.

2023000, Jan 5, 2023

Jun 30, 2022

17/854329

- New York NY, US
Winnie CHENG - West New York NJ, US
Mark John FLAVELL - Madison NJ, US
Lori Marie HALLMARK - Xenia OH, US
Nancy Alayne LIZOTTE - Saline MI, US
Kevin Ma LEONG - Randolph NJ, US
Di ZHU - Jersey City NJ, US
Kevin Michael O'ROURKE - New York NY, US
Eun Kyung KWON - New York NY, US
Vandit NARULA - Monroe Township NJ, US
Weichao CHEN - Secaucus NJ, US
Maria Jesus Perez RAMIREZ - New York NY, US

PricewaterhouseCoopers LLP - New York NY

G06Q 40/00
G06V 30/412
G06V 30/416
G06N 5/04

Systems and methods for determining whether an electronic document constitutes vouching evidence is provided. The system may receive ERP item data and generate hypothesis data based thereon, and may receive electronic document data and extract ERP information therefrom. The system may then apply one or more models to compare the hypothesis data to the extracted ERP information to determine whether the electronic document constitutes vouching evidence for the ERP item. Systems and methods for verifying an assertion against a source document are provided. The system may receive first data indicating an unverified assertion and second data comprising a plurality of source documents. The system may apply one or more extraction models to extract a set of key data from the plurality of source documents and may apply one or more matching models to compare the first data to the set of key data to determine whether vouching criteria are met.