PATENT CLAIM ANALYSIS

Application Number: 16235798
Application Type: Utility
Filing Date: 2018-12
Publication Date: 2019-07
Patent Classification: ["706", "015000"]

Abstract:
A computerized neural-network based mechanism for providing an intermediary configured for intervening in searches is described. Corresponding methods, computer-readable media, systems, devices, and apparatuses are also contemplated. The neural network can include a multi-headed attention layer. The intermediary may be, in some embodiments, a human “man in the middle” mechanism invoked where there is low confidence that pre-existing categories map to a user's search string. The mechanism provides a specially configured interface adapted to enable a search specialist to quickly select one or more categories that match or are otherwise associated with the search query from a set of acceptable categories. Received outputs and detected user behaviors are utilized to update a neural network model.

Claim (Index 9):
The system of  claim 8 , wherein the Bayesian neural network is adapted to sample a weight of a connection during forward propagation, and during the training process, a training example is used to generate multiple versions of outputs with different sampled connection weights, and wherein the inputs along with the outputs are utilized to train the neural network during a backpropagation procedure to update both the expectation determination and the variance determination.

Metadata:
- Claim Count in Document: 9.0
- Percentile: 98.0
- Lexical Diversity: 1.47727
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14283929', '13195846', '13110282', '15624329', '14491841']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3261466417695357
- 35 USC 102 Novelty (BERT): 0.5063100112807672
- Combined Prediction Score: 0.3441629787206589
- Mean Citation Score: 191.227542
- Max Citation Score: 194.25804
- Similarity Product: 116.04934971326352

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