公开(公告)号：US20240086819A1

公开(公告)日：2024-03-14

申请号：US17994136

申请日：2022-11-25

Applicant: Oracle International Corporation

Inventor： Akash BAVISKAR ,  Krishnan RAMANATHAN

IPC: G06Q10/06

CPC classification number: G06Q10/06398 ,  G06Q10/06393

Abstract: Embodiments perform the anomaly detection of expense reports in response to receiving an expense report as input data, the expense report including a plurality of expenses. Embodiments create a plurality of groups of expenses, each group corresponding to a different combination of a category of the expense, a location of the expense and a season of the expense. Embodiments generate and train an unsupervised machine learning model corresponding to each group, and assign each of the expenses of the expense report into a corresponding group and input the expenses into the unsupervised machine learning model corresponding to the group. Embodiments then generate an anomaly prediction at each unsupervised machine learning model for each expense of the expense report.

公开(公告)号：US20240232150A9

公开(公告)日：2024-07-11

申请号：US17971832

申请日：2022-10-24

Applicant: Oracle International Corporation

Inventor： Akash BAVISKAR ,  Krishnan RAMANATHAN

IPC: G06F16/215 ,  G06N20/20

CPC classification number: G06F16/215 ,  G06N20/20

Abstract: Embodiments detect duplicate invoices, each invoice including a plurality of fields. Embodiments generate synthetic training data using a plurality of training invoices and generating one or more modified fields for each of the plurality of training invoices. Embodiments train a machine learning model using the synthetic training data and generate a plurality of candidate invoice pairs. Embodiments input the plurality of candidate invoice pairs to the trained machine learning model and generate, by the trained machine learning model, a prediction of whether each of the candidate invoices pairs is a duplicate invoice pair.

公开(公告)号：US20250117838A1

公开(公告)日：2025-04-10

申请号：US18422321

申请日：2024-01-25

Applicant: Oracle International Corporation

Inventor： Akash BAVISKAR ,  Krishnan RAMANATHAN ,  Vikas AGRAWAL ,  Dipawesh PAWAR

IPC: G06Q30/0601

Abstract: Embodiments classify a product to one of a plurality of product classifications. Embodiments receive a description of the product and create a first prompt for a trained large language model (“LLM”), the first prompt including the description of the product and contextual information of the product. In response to the first prompt, embodiments use the trained LLM to generate a hallucinated product classification for the product. Embodiments word embed the hallucinated product classification and the plurality of product classifications and similarity match the embedded hallucinated product classification with one of the embedded plurality of product classifications. The matched one of the embedded plurality of product classifications is determined to be a predicted classification of the product.

公开(公告)号：US20240273442A1

公开(公告)日：2024-08-15

申请号：US18199645

申请日：2023-05-19

Applicant: Oracle International Corporation

Inventor： Akash BAVISKAR ,  Krishnan RAMANATHAN ,  Kausik MISRA ,  Ramamurthy SHANKARACHETTY

IPC: G06Q10/0637

CPC classification number: G06Q10/06375

Abstract: Embodiments predict a sales order fulfillment of an item. Embodiments receive historical data including past sales orders, and extracts a plurality of machine learning (“ML”) features from the historical data. Embodiments use a portion of the plurality of ML features to train one or more classifiers and generate labeled ML features from the trained classifiers. Embodiments train a ML regression model with the extracted ML features and the labeled ML features. Embodiments then receive a new sales order and generate a prediction on a delivery date for the new sales order using the trained ML regression model.

公开(公告)号：US20240134834A1

公开(公告)日：2024-04-25

申请号：US17971832

申请日：2022-10-23

Applicant: Oracle International Corporation

Inventor： Akash BAVISKAR ,  Krishnan RAMANATHAN

IPC: G06F16/215 ,  G06N20/20

CPC classification number: G06F16/215 ,  G06N20/20

Abstract: Embodiments detect duplicate invoices, each invoice including a plurality of fields. Embodiments generate synthetic training data using a plurality of training invoices and generating one or more modified fields for each of the plurality of training invoices. Embodiments train a machine learning model using the synthetic training data and generate a plurality of candidate invoice pairs. Embodiments input the plurality of candidate invoice pairs to the trained machine learning model and generate, by the trained machine learning model, a prediction of whether each of the candidate invoices pairs is a duplicate invoice pair.

公开(公告)号：US20250014118A1

公开(公告)日：2025-01-09

申请号：US18461713

申请日：2023-09-06

Applicant: Oracle International Corporation

Inventor： Vikas AGRAWAL ,  Krishnan RAMANATHAN ,  Praneeth Medhatithi SHISHTLA ,  Jagdish CHAND

IPC: G06Q40/12 ,  G06N20/00

Abstract: Embodiments predict a target variable for accounts receivable using a machine learning model. For a first customer, embodiments receive a plurality of trained ML models corresponding to the target variable, the plurality of trained ML models trained using the historical data and comprising a first trained model having no grace period for the target variable and two or more grace period trained models, each grace period trained model having different grace periods for the target variable. Embodiments determine a Matthews' Correlation Coefficient (“MCC”) for the first trained model. When the MCC for the first trained model is low, embodiments determine the MCC for each of the grace period trained models, and when one or more MCCs for each of the grace period trained models is higher than the MCC for the first trained model, embodiments select the corresponding grace period trained model having a highest MCC.

公开(公告)号：US20250014097A1

公开(公告)日：2025-01-09

申请号：US18470555

申请日：2023-09-20

Applicant: Oracle International Corporation

Inventor： Vikas AGRAWAL ,  Krishnan RAMANATHAN ,  Praneeth Medhatithi SHISHTLA ,  Jagdish CHAND

IPC: G06Q40/03

Abstract: Embodiments analyze a customer of an organization. Embodiments select the customer and receive historical data corresponding to a plurality of transactions of the customer with the organization, the historical data including, for each of the transactions, a target variable including a number of days of delayed payment for each transaction. Based on the historical data, embodiments determine a cost of a delayed payment from the customer and determine an average delay of payments of the customer. Embodiments convert the cost of delayed payments to a first Z-score and the average delay of payments to a second Z-score. Embodiments then determine a reliability score of the customer comprising determining a Euclidean distance of the first Z-score and the second Z-score.

公开(公告)号：US20250014060A1

公开(公告)日：2025-01-09

申请号：US18236048

申请日：2023-08-21

Applicant: Oracle International Corporation

Inventor： Vikas AGRAWAL ,  Krishnan RAMANATHAN ,  Praneeth Medhatithi SHISHTLA ,  Jagdish CHAND

IPC: G06Q30/0204 ,  G06Q20/08

Abstract: Embodiments predict a target variable for accounts receivable in response to receiving historical data corresponding to a plurality of transactions corresponding to a plurality of customers, the historical data including, for each of the transactions, the target variable. Embodiments segment each of the customers based on the historical data corresponding to each of the customers, the segmenting including determining a variation of the target variable for each customer and, based on the variation, classifying each customer as having a low variation, a medium variation, or a high variation. For each low variation customer, embodiments create a regular ML model without a grace period that is trained and tested using the historical data. For each medium variation customer, embodiments create the regular ML model and create two or more grace period ML models, each grace period ML model adding a different grace period to the target variable.

公开(公告)号：US20240037482A1

公开(公告)日：2024-02-01

申请号：US17903161

申请日：2022-09-06

Applicant: Oracle International Corporation

Inventor： Akash BAVISKAR ,  Krishnan RAMANATHAN ,  Abhilash NEOG ,  Dipawesh PAWAR ,  Karthik BANGALORE MANI

IPC: G06Q10/08 ,  G06F16/35 ,  G06F40/211 ,  G06F40/49

CPC classification number: G06Q10/08 ,  G06F16/35 ,  G06F40/211 ,  G06F40/49

Abstract: Embodiments classify a product to a product category. Embodiments receive a textual description of the product and create an index of product categories. Embodiments use a plurality of classifiers to classify the textual description to one of the product categories, the classifiers including an index based classifier, an encyclopedia based classifier, a Bayes' rule based classifier, and an embeddings classifier.