Cooperatively operating a network of supervised learning processors to concurrently distribute supervised learning processor training and provide predictive responses to input data
A supervised learning processing (SLP) system and non-transitory, computer program product provides cooperative operation of a network of supervised learning processors to concurrently distribute supervised learning processor training, generate predictions, and provide prediction driven responses to input objects, such as NL statements. The SLP system includes SLP stages that are distributed across multiple SLP subsystems. Concurrently training SLP's provides accurate predictions of input objects and responses thereto, the SLP system and non-transitory, computer program product enhance the network by providing high quality value predictions and responses and avoiding potential training and operational delays. The SLP system can enhance the network of SLP subsystems by providing flexibility to incorporate multiple SLP models into the network and train at least a proper subset of the SLP models while concurrently using the SLP system and non-transitory, computer program product in commercial operation.
1. A method of cooperatively operating a network of supervised learning processor subsystems to concurrently distribute supervised learning processor training and provide predictive responses to input objects, the method comprising:
in an electronic, supervised learning processing system:
receiving an input object from a requestor device;
storing the input object in a memory;
(A) receiving and accessing contextual data associated with the input object to contextualize the input object;
(B) associating the contextual data with the input object;
(C) processing the input object and contextual data using a plurality of supervised learning processing stages distributed between at least a first order supervised learning processor subsystem and a second order supervised learning processor subsystem having an equal or higher, predictive output quality value than the first order supervised learning processor subsystem, wherein:
(i) each supervised learning processing stage comprises one or more supervised learning processor models,
(ii) each model of the first order supervised learning processor subsystem:
(a) processes at least the input object and contextual data to determine a predictive response to the input object; and
(b) determines a quality value of the predictive response; and
(iii) the second order supervised learning processing subsystem determines an output of each supervised learning processing stage for each predictive response whose quality value is below a pre-determined threshold; and
(D) training at least one model of the first order supervised learning processor subsystem with the determined output from the second order supervised learning processor subsystem;
(E) determining a response to the input object using at least a combined output from each supervised learning processing stage, wherein enhancement of the network of supervised learning processor subsystems includes concurrently distributing training and cooperatively operating the supervised learning processor subsystems to determine the response to the input object; and
(F) providing the response to the user of the requestor device.
2. The method of claim 1 wherein the input object is a natural language statement.
3. The method of claim 1 wherein the models include domain classifier, intent classifier, entities extraction, and entities normalization models.
4. The method of claim 3 wherein the models include analytics that are tuned to respective problems and solutions.
5. The method of claim 3 wherein at least one of the domain classifier, intent classifier, entities extraction, and entities normalization models comprise multiple sub-models.
6. The method of claim 1 wherein the input object comprises at least one of (i) a query and (ii) a command.
7. The method of claim 1 wherein the requestor device is associated with a user of the requestor device, and the contextual data includes data associated with the user.
8. An apparatus comprising a cooperatively operated network of supervised learning processor subsystems to concurrently distribute supervised learning processor training and provide predictive responses to input objects, the apparatus comprising:
one or more data processors;
a memory, coupled to the data processors, having code stored therein to cause the one or more data processors to:
receive an input object from a requestor device;
store the input object in the memory;
(A) receive and access contextual data associated with the input object to contextualize the input object;
(B) associate the contextual data with the input object;
(C) process the input object and contextual data using a plurality of supervised learning processing stages distributed between at least a first order supervised learning processor subsystem and a second order supervised learning processor subsystem having an equal or higher, predictive output quality value than the first order supervised learning processor subsystem, wherein:
(i) each supervised learning processing stage comprises one or more supervised learning processor models,
(ii) each model of the first order supervised learning processor subsystem:
(a) processes at least the input object and contextual data to determine a predictive response to the input object; and
(b) determines a quality value of the predictive response; and
(iii) the second order supervised learning processing subsystem determines an output of each supervised learning processing stage for each predictive response whose quality value is below a pre-determined threshold;
(D) train at least one model of the first order supervised learning processor subsystem with the determined output from the second order supervised learning processor subsystem;
(E) determine a response to the input object using at least a combined output from each supervised learning processing stage, wherein enhancement of the network of supervised learning processor subsystems includes concurrently distributing training and cooperatively operating the supervised learning processor subsystems to determine the response to the input object; and
(F) provide the response to the user of the requestor device.
9. The apparatus of claim 8 wherein the input object is a natural language statement.
10. The apparatus of claim 8 wherein the models include domain classifier, intent classifier, entities extraction, and entities normalization models.
11. The apparatus of claim 10 wherein the models include analytics that are tuned to respective problems and solutions.
12. The apparatus of claim 10 wherein at least one of the domain classifier, intent classifier, entities extraction, and entities normalization models comprise multiple sub-models.
13. The apparatus of claim 8 wherein the input object comprises at least one of (i) a query and (ii) a command.
14. The apparatus of claim 8 wherein the requestor device is associated with a user of the requestor device, and the contextual data includes data associated with the user.
15. A non-transitory, computer program product comprising code stored therein and executable by a cooperatively operated a network of supervised learning processor subsystems to concurrently distribute supervised learning processor training and provide predictive responses to input objects by:
receiving an input object from a requestor device;
storing the input object in a memory;
(A) receiving and accessing contextual data associated with the input object to contextualize the input object;
(B) associating the contextual data with the input object;
(C) processing the input object and contextual data using a plurality of supervised learning processing stages distributed between at least a first order supervised learning processor subsystem and a second order supervised learning processor subsystem having an equal or higher, predictive output quality value than the first order supervised learning processor subsystem, wherein:
(i) each supervised learning processing stage comprises one or more supervised learning processor models,
(ii) each model of the first order supervised learning processor subsystem:
(a) processes at least the input object and contextual data to determine a predictive response to the input object; and
(b) determines a quality value of the predictive response; and
(iii) the second order supervised learning processing subsystem determines an output of each supervised learning processing stage for each predictive response whose quality value is below a pre-determined threshold; and
(D) training at least one model of the first order supervised learning processor subsystem with the determined output from the second order supervised learning processor subsystem;
(E) determining a response to the input object using at least a combined output from each supervised learning processing stage, wherein enhancement of the network of supervised learning processor subsystems includes concurrently distributing training and cooperatively operating the supervised learning processor subsystems to determine the response to the input object; and
(F) providing the response to the user of the requestor device.
16. The non-transitory, computer program product of claim 15 wherein the input object is a natural language statement.
17. The non-transitory, computer program product of claim 15 wherein the models include domain classifier, intent classifier, entities extraction, and entities normalization models.
18. The non-transitory, computer program product of claim 17 wherein the models include analytics that are tuned to respective problems and solutions.
19. The non-transitory, computer program product of claim 17 wherein at least one of the domain classifier, intent classifier, entities extraction, and entities normalization models comprise multiple sub-models.
20. The non-transitory, computer program product of claim 15 wherein the input object comprises at least one of (i) a query and (ii) a command.
21. The non-transitory, computer program product of claim 15 wherein the requestor device is associated with a user of the requestor device, and the contextual data includes data associated with the user.