PATENT CLAIM ANALYSIS

Application Number: 15859698
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-09
Patent Classification: ["716", "102000"]

Abstract:
The embodiments herein discloses a system and method for designing SoC by using a reinforcement learning processor. An SoC specification input is received and a plurality of domains and a plurality of subdomains is created using application specific instruction set to generate chip specific graph library. An interaction is initiated between the reinforcement learning agent and the reinforcement learning environ lent using the application specific instructions. Each of the SoC sub domains from the plurality of SoC sub domains is mapped to a combination of environment, rewards and actions by a second processor. Further, interaction of a plurality of agents is initiated with the reinforcement learning environment for a predefined number of times and further Q value, V value, R value, and A value is updated in the second memory module. Thereby, an optimal chip architecture for designing SoC is acquired using application-domain specific instruction set (ASI).

Claim (Index 1):
A system for designing SoC, the system comprising;\n a first processor configured to create at least one reinforcement learning agent and at least one corresponding reinforcement learning environment, and wherein said first processor is further configured to assign a reinforcement learning agent ID to said reinforcement learning agent, and a reinforcement learning environment ID to said reinforcement learning environment; a first memory module communicably coupled to said first processor, and wherein said first memory module is configured to store an application-domain specific instruction set (ASI)

Metadata:
- Claim Count in Document: 28.0
- Percentile: 86.0
- Lexical Diversity: 2.03797
- Patent Class: 716.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15697803', '15499832', '15455126', '14097862', '13953457']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3463982616876475
- 35 USC 102 Novelty (BERT): 0.5693307022466072
- Combined Prediction Score: 0.3686915057435435
- Mean Citation Score: 296.904798
- Max Citation Score: 494.74124000000006
- Similarity Product: 456.34636975604064

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