PATENT CLAIM ANALYSIS

Application Number: 16027109
Application Type: Utility
Filing Date: 2018-07
Publication Date: 2019-01
Patent Classification: ["705", "039000"]

Abstract:
Methods, systems, and computer programs are presented for reconciling a transaction against data in a database to identify the transaction parameters based on text descriptors provided for the transaction. One method includes an operation for identifying features for reconciling transactions of a first entity by a machine-learning program. The features include, at least, a description of the transaction, a name of a second entity in the transaction, a location of the second entity, and an account for the transaction. The machine-learning program is trained with training data that includes values of the features for previously reconciled transactions. A received first transaction includes a description, a date, and an amount. The first transaction is input for the machine-learning program, which generates one or more suggestions for reconciling the first transaction. Each suggestion includes the name of the second entity in the first transaction and an account.

Claim (Index 2):
The method as recited in  claim 1 , wherein the features for reconciling transactions further comprise a location of the first entity, an industry of the first entity, and a location of the second entity.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 95.0
- Lexical Diversity: 2.31429
- Patent Class: 705.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['13385494', '12030824', '16014120', '10643514', '15444898']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.115642875404078
- 35 USC 102 Novelty (BERT): 0.4865929883988473
- Combined Prediction Score: 0.1527378867035549
- Mean Citation Score: 155.995004
- Max Citation Score: 165.87852
- Similarity Product: 107.20008271626948

Labels:
- Claim Label 101: 0
- Claim Label 102: 0
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test