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 19):
A system comprising:\n one or more computing devices providing resources as part of a cloud computing environment; and a hybrid resource allocation module implemented by another one or more computing devices, the hybrid resource allocation module comprising instructions which, when executed by the one or more computing devices, cause the hybrid resource allocation module to:\n receive, from ones of a plurality of applications, requests for resource availability information of the resources provided by the one or more computing devices; \n determine, based at least in part on a use of a machine learning model, for an application that provided a request whether the application is categorized into an optimistic cluster of applications or into a pessimistic cluster of applications; and \n when the requesting application is in the optimistic cluster, provide the resource availability information to the requesting application and ensure that a lock is set 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): 0.2304455809514988
- 35 USC 102 Novelty (BERT): 0.595904710917156
- Combined Prediction Score: 0.2669914939480645
- Mean Citation Score: 276.577038
- Max Citation Score: 537.193
- Similarity Product: 443.1777186989784

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