PATENT CLAIM ANALYSIS

Application Number: 16014120
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2019-03
Patent Classification: ["705", "038000"]

Abstract:
A system that automatically manages a collateral risk and a business performance of an entity is provided. The system generates a customizable database to store critical accounting and ancillary business information. The system automatically updates the customized database at predefined time periods based on new accounting and ancillary business information. The system automatically enables the customized database for scalable assessment of a collateral risk and a business performance of an entity. The system determines the collateral risk and business performance of an entity and provides the collateral risk, and business performance of the entity and an analytical summary of the accounting and the ancillary business information to a machine learning model. The system recommends actions to be performed to reduce the collateral risk and to improve the business performance based on the accounting and ancillary business information. The system enables a user to perform the actions to reduce the collateral risk and to improve the business performance of the entity.

Claim (Index 21):
A method for automatically determining collateral risk using a business intelligence tool by training a machine learning model, and generating a recommendation using the machine learning model, said method comprising:\n (a) generating a customizable database to store critical accounting and ancillary business information, wherein said database is generated by\n extracting current accounting period information associated with a plurality of accounts in a general ledger (GL) along with its groupings with reference to financial statements, wherein said current accounting period information associated with said plurality of accounts is obtained from a general ledger accounts data system; \n extracting current accounting period information associated with a plurality of collateral accounts in a borrowing base structure along with its groupings with reference to covenants with stakeholders that are linked in a plurality of lender's or stakeholder's books in case of external funding; \n extracting current accounting period information associated with a plurality of is customers, wherein said current accounting period information associated with said plurality of customers is obtained from a customer account receivable data system that are linked to the respective accounts in said GL and the respective collateral accounts in said plurality of lender's or stakeholder's books for collateral management; \n extracting current accounting period information associated with various types of inventories that comprise at least one of broad categories, sub-categories or locations, wherein said current accounting period information associated with various types of inventories is obtained from an inventory data system that are linked to the respective accounts in said GL and the respective collateral accounts in said plurality of lender's or stakeholder's books for collateral management; \n extracting current accounting period information associated with a plurality of vendors, wherein said current accounting period information associated with said plurality of vendors is obtained from a vendor accounts data system and accruals that are linked to the respective accounts in said GL; \n extracting current accounting period information associated with a plurality of treasury accounts in said GL with its groupings with reference to financial statements and said plurality of collateral accounts in said plurality of lender's or stakeholder's books for collateral management, wherein said current accounting period information associated with said plurality of treasury accounts is obtained from a treasury accounts data system; \n automatically processing said extracted current accounting period information associated with said plurality of accounts, said plurality of collateral accounts, said plurality of customers, said inventories and said plurality of vendors to correct data extraction errors using a quality control system; \n automatically determining one or more parameters from said extracted current accounting period information associated with said plurality of accounts, said plurality of collateral accounts, said plurality of customers, said inventories and said plurality of vendors by analyzing said current accounting period information using a data analysis technique; \n automatically generating memory blocks in said database for each of said one or more parameters using a database generation technique upon receiving one or more inputs from a user; and \n mapping current accounting period information associated with each of said one or more parameters with the respective memory blocks to obtain a customized database, wherein said current accounting period information comprises at least one of account receivable, inventory and account payable information; \n (b) automatically updating said customized database at predefined time periods based on new accounting and ancillary business information received from the respective data system; (c) automatically enabling said customized database for scalable assessment of a collateral risk and a business performance of an entity, wherein said scalable assessment of said collateral risk and business performance comprises\n analyzing said accounting and ancillary business information associated with said plurality of collateral accounts to generate customizable analytical reports for assessing said collateral risk and said business performance; \n implementing a summarization technique to generate a summary of said accounting and ancillary business information based on said analytical report, wherein said summary of said accounting and ancillary business information comprises financial information that is critical and non-financial information to assess said collateral risk and business performance; and \n generating an analytical summary comprises at least one of trends, variances and swings in reconciling items on a consistent pattern related to said account receivable, said inventory, said accounts payable and accruals, treasury accounts and government enacted legal liabilities with relevant accounting, said non-financial information and said ancillary business information that is critical to assess said collateral risk, and said business performance or stability; \n (d) determining, using a business intelligence tool, said collateral risk, and said business performance of an entity and providing said collateral risk, and said business performance of said entity and said analytical summary of said accounting and the ancillary business information to a machine learning model, wherein said machine learning model is generated by providing (i) a summary of accounting and ancillary business information of different entities comprising at least one of trends or variances in at least one of (A) reconciling items related to account receivable, inventory, accounts payable and accruals, treasury accounts, or government enacted legal liabilities with accounting, (B) non-financial information and ancillary business information that is critical to assess collateral risk, or (C) a business performance and a stability of different entities, (ii) calculated collateral risk and business performance of different entities, and (iii) a financial expert inputs on the summary, the calculated collateral risk, and the business performance of different entities, as training data; (e) recommending, using the machine learning model, one or more actions to be performed to reduce said collateral risk and to improve said business performance based on said accounting and ancillary business information; and (f) enabling a user to perform the one or more actions to reduce said collateral risk and to improve said business performance of said entity.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 94.0
- Lexical Diversity: 2.94737
- Patent Class: 705.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['10329309', '12854402', '12534475', '12534545', '09391774']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1105011638877825
- 35 USC 102 Novelty (BERT): 0.5305562493997475
- Combined Prediction Score: 0.152506672438979
- Mean Citation Score: 302.563298
- Max Citation Score: 348.4437
- Similarity Product: 246.45614230411647

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test