Systems and methods for managing, distributing and deploying a recursive decisioning system based on continuously updating machine learning models
AssigneeSavvi Ai Inc.
InventorAlex Muller
Life of this patent
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Abstract
The present disclosure relates generally to the generation and deployment of a machine learning-enabled decision engine (MLDE). The MLDE includes decision options that are composed of a discrete list of selectable options. Further, the MLDE includes data inputs that can be used to influence decisions made by the machine learning models of the MLDE. Controls are applied to the MLDE to overlay and bound the decisioning within guidelines established by an operator of the MLDE. Once the MLDE is established, the MLDE is validated and deployed for use by software applications to make decisions.
Claims24
A computer-implemented method comprising:
- receiving a set of use cases for generating different ranked lists of decision options (RLDOs) from a set of decision options, wherein the set of use cases includes different influencing data for generating the different RLDOs;
- automatically generating a set of GET application programming interface (API) calls to generate the different RLDOs for the set of use cases, wherein the set of GET API calls includes the different influencing data;
- transmitting the set of GET API calls, wherein when the set of GET API calls are received by a microservice that implements a machine learning-enabled decisioning engine (MLDE), the microservice processes the set of GET API calls through the MLDE to obtain the different RLDOs from the set of decision options for the set of use cases;
- receiving the different RLDOs for the set of use cases, wherein the different RLDOs are received as the microservice processes the different influencing data through the MLDE;
- and
- presenting the different RLDOs as the different RLDOs are received.
A system, comprising:
- one or more processors;
- and
- memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
- receive a set of use cases for generating different ranked lists of decision options (RLDOs) from a set of decision options, wherein the set of use cases includes different influencing data for generating the different RLDOs;
- automatically generate a set of GET application programming interface (API) calls to generate the different RLDOs for the set of use cases, wherein the set of GET API calls includes the different influencing data;
- transmit the set of GET API calls, wherein when the set of GET API calls are received by a microservice that implements a machine learning-enabled decisioning engine (MLDE), the microservice processes the set of GET API calls through the MLDE to obtain the different RLDOs from the set of decision options for the set of use cases;
- receive the different RLDOs for the set of use cases, wherein the different RLDOs are received as the microservice processes the different influencing data through the MLDE;
- and
- present the different RLDOs as the different RLDOs are received.
A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:
- receive a set of use cases for generating different ranked lists of decision options (RLDOs) from a set of decision options, wherein the set of use cases includes different influencing data for generating the different RLDOs;
- automatically generate a set of GET application programming interface (API) calls to generate the different RLDOs for the set of use cases, wherein the set of GET API calls includes the different influencing data;
- transmit the set of GET API calls, wherein when the set of GET API calls are received by a microservice that implements a machine learning-enabled decisioning engine (MLDE), the microservice processes the set of GET API calls through the MLDE to obtain the different RLDOs from the set of decision options for the set of use cases;
- receive the different RLDOs for the set of use cases, wherein the different RLDOs are received as the microservice processes the different influencing data through the MLDE;
- and
- present the different RLDOs as the different RLDOs are received.
Dependencies are inferred from the claim text.