PATENT CLAIM ANALYSIS

Application Number: 16004683
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-10
Patent Classification: ["709", "226000"]

Abstract:
Exemplary techniques for analytics-driven hybrid concurrency control in clouds are disclosed that include a hybrid resource allocation module that can concurrently utilize an optimistic allocation scheme alongside a pessimistic allocation scheme. Machine learning techniques utilizing previous activity history of applications can be used to train a cluster model that is integrated by a hybrid resource allocation module to classify applications in either a pessimistic cluster or an optimistic cluster that identifies under which scheme requests from the applications will be processed.

Claim (Index 1):
A method in a hybrid resource allocation module implemented by one or more computing devices, the method comprising:\n receiving, from a first application of a plurality of applications, a first request for resource availability information of resources provided by a set of computing devices; determining, based at least in part on a use of a machine learning model, that the first application is categorized into an optimistic cluster of applications, wherein the machine learning model categorizes applications into the optimistic cluster or a pessimistic cluster; providing the resource availability information to the first application; and setting a lock to prevent any application that is categorized into the pessimistic cluster from being able to obtain resource availability information while the lock is set, wherein any application that is categorized into the optimistic cluster can still obtain available resource information while the lock is set.

Metadata:
- Claim Count in Document: 53.0
- Percentile: 94.0
- Lexical Diversity: 1.53704
- Patent Class: 709.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14949703', '14667374', '09106166', '15454470', '10708262']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.234074375478451
- 35 USC 102 Novelty (BERT): 0.5836290771787888
- Combined Prediction Score: 0.2690298456484848
- Mean Citation Score: 276.577038
- Max Citation Score: 537.193
- Similarity Product: 390.1088758974671

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