PATENT CLAIM ANALYSIS

Application Number: 15894939
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-08
Patent Classification: ["379", "265060"]

Abstract:
A system and method for comprehensive automated call center customer/agent interaction monitoring and service level agreement (SLA) compliance. The system reduces a massive volume of call center activity into readable data points and SLA metrics for measuring agent and overall call center performance levels. The system allows for the scaling up of the SLA compliance process, which is currently done manually by quality assurance personnel for a limited sample set. With the system, customer calls are computationally sampled for speaker diarization and voice isolation, speech emotion recognition, unique salient feature extraction, reference pattern template matching, and automatic speech recognition. The system is adaptively programmable for recognizing and predicting SLA metrics such as: customer satisfaction, issue resolution, appropriate agent greeting and identification, customer understanding, acknowledgment, abandonment, sales attempts, and customer retention, etc. Rating scores are assigned to SLA metrics by intelligent speech emotion pattern recognition and machine learning algorithm. The system provides for cost-effective SLA metrics and quality assurance at scale, with agent performance statistics, customer satisfaction data, and additional insights, via system generated reports and live activity streams.

Claim (Index 8):
A method for automatically monitoring service level agreement (SLA) compliance in call centers, comprising:\n sampling the agent/customer phone call audio signal data; pre-processing the sample with filtering, noise reduction, diarization, and frame division splicing; performing frame by frame feature extraction with a set of system optimized pattern recognition and identification parameters; grouping the sample frames into an SLA metric defined classification scheme; applying a reference pattern template for programming contextual call center application specific environments; generating a live stream of agent/customer interaction SLA metrics; and adaptively reinforcing call center agent behavior through live SLA metric reporting and suggested means for customer satisfaction improvement; wherein the feature extraction comprises conventional features, global features, voice quality features, other features, and salient contextually relevant features.

Metadata:
- Claim Count in Document: 36.0
- Percentile: 88.0
- Lexical Diversity: 1.74576
- Patent Class: 379.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15795069', '15713283', '10484107', '13098504', '11359356']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5428611755169922
- 35 USC 102 Novelty (BERT): 0.4791618060276567
- Combined Prediction Score: 0.5364912385680587
- Mean Citation Score: 218.629294
- Max Citation Score: 230.206
- Similarity Product: 174.61705051934717

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