System and method for supporting the utilization of machine language
First Claim
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1. A computerized method of incorporating a machine learning solution, comprising a machine learning model, into a transaction processing system, the method comprising:
- a) configuring an application interface component to access a set of functionality associated with the machine learning solution;
b) configuring a model management component to access at least one instance of said machine learning model according to a request received from the application interface component, wherein configuring said model management component comprises the steps of;
configuring a synchronization policy associated with said model management component;
configuring a persistence policy associated with said model management component; and
configuring a versioning policy associated with said model management component;
wherein said versioning policy is implemented according to a versioning policy interface which encodes how a machine learning model version in memory and a machine learning model version in persistent storage should be synchronized.
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Abstract
A system and method is disclosed which integrates a machine learning solution into a large scale, distributed transaction processing system using a supporting architecture comprising a combination of computer hardware and software. Methods of using a system comprising such supporting architecture provide application designers access to the functionality included in a machine learning solution, but might also provide additional functionality not supported by the machine learning solution itself.
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Citations
13 Claims
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1. A computerized method of incorporating a machine learning solution, comprising a machine learning model, into a transaction processing system, the method comprising:
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a) configuring an application interface component to access a set of functionality associated with the machine learning solution; b) configuring a model management component to access at least one instance of said machine learning model according to a request received from the application interface component, wherein configuring said model management component comprises the steps of; configuring a synchronization policy associated with said model management component; configuring a persistence policy associated with said model management component; and configuring a versioning policy associated with said model management component; wherein said versioning policy is implemented according to a versioning policy interface which encodes how a machine learning model version in memory and a machine learning model version in persistent storage should be synchronized. - View Dependent Claims (2, 3, 4)
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5. A machine learning system comprising:
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a computer comprising; a) an application interface component further comprising; a machine learning engine; a machine learning context; and a machine learning controller; b) a model management component further comprising; a model pool; a model pool manager; and a synchronization manager; c) an algorithm management component further comprising; an algorithm manager; an algorithm instance map; an algorithm instance map; an algorithm factory; an algorithm implementation; and an algorithm interface; wherein the machine learning engine, in combination with the machine learning context, provides said application interface component, accessible by an application program, and wherein said machine learning engine communicates a request to said algorithm management component and/or said model management component;
wherein the algorithm interface defines how an algorithm instance of an algorithm implementation is accessed;wherein the algorithm manager is configured to create, retrieve, update and/or delete an algorithm instance, through said algorithm factory, and enter said algorithm instance into said algorithm instance map based on said request received through said application interface component; wherein a model is stored within said model pool and wherein a synchronization policy is associated with said model; wherein said model pool manager is configured to create, retrieve, update and/or delete said model within the model pool based on said request received through said application interface component; wherein said synchronization manager executes said synchronization policy associated with said model; wherein said machine learning controller binds an algorithm instance with said model; wherein said synchronization manager is configured to update a prototypical model only if said request contains an appropriate learning event; and wherein said appropriate learning event comprises that said model, associated with said request, and said prototypical model comprise an identical vendor, an identical algorithm structure, and a set of identical model parameter attribute types. - View Dependent Claims (6, 7, 8, 9, 10, 11)
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12. A method of utilizing a computerized machine learning solution, comprising a machine learning model, in a transaction processing system, the method comprising:
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a) using a machine learning engine of an application interface component to connect said machine learning solution to an on-line retailer'"'"'s website; b) coding a classification method, in said transaction processing system, to be invoked when a customer visits the website; c) coding said classification method to send a message to a machine learning controller; d) requesting a model manager and an algorithm manager from said machine learning controller for an instance of an associated algorithm and an instance of an associated model; e) determining a recommendation from a set of available products based on processing said instance of said associated algorithm, said instance of said associated model and an output from said classification method; f) coding a learn method to be called when a purchase takes place; g) passing a purchase message, regarding said purchase, to said machine learning controller as a learning event; h) passing said learning event to an update method exposed by a synchronization policy interface of said instance of said associated model via said model manager; i) sending said learning event to a prototypical model; j) creating an updated version of said prototypical model; and k) propagating said updated prototypical model to a plurality of distributed servers according to a propagation method exposed by said synchronization policy interface. - View Dependent Claims (13)
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Specification