PATENT CLAIM ANALYSIS

Application Number: 16166593
Application Type: Utility
Filing Date: 2018-10
Publication Date: 2019-06
Patent Classification: ["714", "015000"]

Abstract:
Various embodiments relate generally to electrical and electronic hardware, computer software and systems for controlling a data stream processor configured to detect and/or resolve anomalies in data streams including message data. In particular, a system, a device and a method may be configured to access multiple data streams and to detect an anomaly, in real-time or in substantially real-time, that is associated with at least one of the data streams accessed by a data stream processor. In some examples, a method can include one or more of receiving message data to facilitate a computerized rental of property, classifying subset of messages, fetching the classified messages to form multiple data streams, accessing the data stream to indemnity a stream characteristic, detecting an anomaly based on an identified stream characteristic, and generating anomaly resolution data to counteract the detected anomaly.

Claim (Index 37):
The system of  claim 35 , wherein to determine the plurality of parametric values, the one or more processors are further configured by the executable instructions to identify, in the first data stream, parametric data regarding one or more of: a computer identifiers a transit time, or a message type.

Metadata:
- Claim Count in Document: 26.0
- Percentile: 97.0
- Lexical Diversity: 1.87654
- Patent Class: 714.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14935215', '14935209', '16009721', '14968701', '15084343']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3149248446553231
- 35 USC 102 Novelty (BERT): 0.5161763243252875
- Combined Prediction Score: 0.3350499926223195
- Mean Citation Score: 206.332288
- Max Citation Score: 319.14493
- Similarity Product: 229.55614555615605

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

Dataset: test