Granted Patent B1

US 7,809,663 · App. 11/751,680 · Granted Oct 5, 2010

Abstract

Claims (54)

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.

2. The method of incorporating said machine learning solution, as claimed in claim 1 , wherein said persistence policy comprises a set of computer-executable instructions encoding how, when, and where said machine learning model should be persisted.

3. The method of incorporating said machine learning solution, as claimed in claim 1 , further comprising accessing said synchronization policy through a synchronization policy interface.

4. The method of incorporating said machine learning solution, as claimed in claim 3 , wherein said synchronization policy interface comprises computer-executable instructions for invoking said synchronization policy for said machine learning model.

5. A machine learning system comprising:

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.

6. The machine learning system, as claimed in claim 5 , wherein said application interface component, provided by the machine learning context, is configured to expose a particular machine learning algorithm.

7. The machine learning system, as claimed in claim 5 , wherein said application interface component, provided by the machine learning context, is configured to expose a general purpose interface.

8. The machine learning system, as claimed in claim 5 , wherein said application interface component, provided by the machine learning context, is configured to expose a focused interface to be applied to a specific task.

9. The machine learning system, as claimed in claim 5 , wherein said synchronization manager is configured to propagate an updated prototypical model to a plurality of models comprising said model and at least one model hosted remotely from said updated prototypical model.

10. The machine learning system, as claimed in claim 9 , wherein said propagation of said updated prototypical model to said plurality of models is executed in accordance with a synchronization policy associated with each of said plurality of models.

11. The machine learning system, as claimed in claim 5 , wherein said model pool manager is configured to retrieve a plurality of models within the model pool based on said request received through said application interface component.

12. A method of utilizing a computerized machine learning solution, comprising a machine learning model, in a transaction processing system, the method comprising:

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.

13. The method of utilizing said machine learning solution, as claimed in claim 12 , wherein said propagation method is a master-slave synchronization policy.

Assignments (8)