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：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：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.

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：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.