PATENT CLAIM ANALYSIS

Application Number: 16417148
Application Type: Utility
Filing Date: 2019-05
Publication Date: 2019-11
Patent Classification: ["706", "021000"]

Abstract:
Systems and methods for generating a slate of ranked items are provided. In one example embodiment, a computer-implemented method includes inputting a sequence of candidate items into a machine-learned model, and obtaining, in response to inputting the sequence of candidate items into the machine-learned model, an output of the machine-learned model that includes a ranking of the candidate items that presents a diverse set of the candidate items at the top positions in the ranking such that one or more highly relevant candidate items can be demoted in the ranking.

Claim (Index 9):
A computer-implemented method to train a machine-learned pointer network for generating an output sequence from a list of candidate items, the method comprising:\n obtaining, by one or more computing devices, data descriptive of the machine-learned pointer network, wherein the machine-learned pointer network comprises an encoder network configured to receive the list of candidate items and provide an output that includes a sequence of latent memory states, a decoder network that operates over a plurality of decoding steps and is configured to receive a previously-selected candidate item for the output sequence and provide an output vector based at least in part on the previously-selected candidate item, and an attention network configured to receive the sequence of latent memory states and a query including the output vector from the decoder network, wherein the attention network is configured to produce a probability distribution associated with a next candidate item to include in the output sequence; training, by the one or more computing devices, the machine-learned pointer network based on a set of training data, wherein training, by the one or more computing devices, the machine-learned pointer network comprises: determining, by the one or more computing devices, a per-step loss for two or more of the plurality of decoding steps, the per-step loss representing a performance evaluation of the machine-learned pointer network based on the set of training data; and modifying, by the one or more computing devices, one or more parameters of the machine-learned pointer network based at least in part on the per-step loss.

Metadata:
- Claim Count in Document: 2.0
- Percentile: 100.0
- Lexical Diversity: 1.92
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15975731', '15469981', '14586202', '15475016', '15381637']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3664965033726198
- 35 USC 102 Novelty (BERT): 0.4821188267691318
- Combined Prediction Score: 0.378058735712271
- Mean Citation Score: 185.953252
- Max Citation Score: 205.69078
- Similarity Product: 149.797111336236

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