PATENT CLAIM ANALYSIS

Application Number: 15936736
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-10
Patent Classification: ["358", "001130"]

Abstract:
Embodiments of the disclosure provide for assisting with troubleshooting of a Multi-Function Printer (MFP) by receiving a text or voice message expressed in natural language from a user of the MFP, extracting text data or audio data of the received text or voice message, forwarding the extracted text data or audio data to one or more servers, receiving, from the servers, entities or intents related to the text or voice message based on processing of the text or audio data by the servers, processing the received entities or intents, sending information indicating one or more errors to the servers, receiving, from the servers, troubleshooting information directed to the errors, and providing at least a portion of the received troubleshooting information to the user of the MFP through the mobile device of the user of the MFP.

Claim (Index 17):
The method of  claim 16 , further comprising:\n saving, by the one or more servers, the information indicating the one or more errors; building, by the one or more servers, a history of error codes and the provided troubleshooting information; and applying, by the one or more servers, machine learning to the built history, wherein extracting troubleshooting information directed to the one or more errors is based on previously applied machine learning.

Metadata:
- Claim Count in Document: 23.0
- Percentile: 90.0
- Lexical Diversity: 2.58621
- Patent Class: 358.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15936653', '14851145', '15457692', '15063757', '15661516']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4784286541711221
- 35 USC 102 Novelty (BERT): 0.5331963161580906
- Combined Prediction Score: 0.483905420369819
- Mean Citation Score: 197.621416
- Max Citation Score: 353.42255
- Similarity Product: 213.2357202663988

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

Dataset: test