Hybrid client/server architecture for parallel processing

• US 10,749,989 B2
• Filed: 09/08/2014
• Issued: 08/18/2020
• Est. Priority Date: 04/01/2014
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Le, Christopher Dac, Calcagno, Michael, Hamaker, Jon, Chambers, Rob, Plumpe, Michael, Wilson, Travis
• Primary Examiner(s): Nano, Sargon N
• Assistant Examiner(s): Robinson, Christopher B
• Application Number:
  US14/480,422
• Publication Number:
  US 20150281401A1
• Time in Patent Office: 2,171 Days
• Field of Search: None
• CPC Class Codes: G06F 40/20 Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06F 9/547 Remote procedure calls [RPC]; Web services
• CPC Class Codes: H04L 67/01 Protocols
• CPC Class Codes: H04L 67/2866 Architectures; Arrangements

Abstract

The present disclosure describes an exemplary hybrid client/server architecture that may be utilized leverage the unique capabilities of both remote and local services. Data may be processed in parallel by remote and local processes. Results generated during the parallel processing may be exchanged between remote and local services and used to update results generated by the separate services. The hybrid client/server architecture may be utilized to generate enhanced inferences, hybrid subscriptions base upon local and remote subscriptions, and enhance natural language expression evaluation services.

Claims

1. A method for parallel processing of data using a client computing device, the method comprising:

receiving, at the client computing device, a signal comprising the data;

sending, from the client computing device to at least one remote device, at least a subset of the data;

processing, at the client computing device, the data using a local service of the client computing device, wherein processing the data comprises;

loading a local knowledge base;

loading a third party knowledge base; and

processing the data using the local knowledge base and the third party knowledge base;

sending an initial local service result to a remote service of the at least one remote device for analysis, wherein the initial local service result comprises a first partial determination generated during local processing;

receiving, at the client computing device and from the remote service, an initial remote service result while processing the data using the local service, wherein the initial remote service comprises a second partial determination; and

generating using the local service, an intermediate local service result, wherein generating the intermediate local service result comprises updating the initial local service result based upon the initial remote service result.

2. The method of claim 1, wherein sending the subset of data further comprises determining that the subset of data does not contain private data.

3. The method of claim 1, wherein the local service is an inference service, and wherein processing the data using the local service comprises analyzing private data to generate local inference as the initial local service result.

4. The method of claim 3, wherein sending the initial local service result further comprises:

analyzing the local inference to determine whether the local inference can be provided to the remote service; and

when the local inference can be provided to the remote service, sending the local inference to the remote service.

5. The method of claim 4, further comprising:

when the local inference cannot be provided to the remote service, abstracting the local inference to remove private data; and

sending the abstracted local inference to the remote service.

6. The method of claim 4, wherein receiving an initial remote service result further comprises receiving an updated inference from the remote service, wherein the updated inference comprises additional information about the local inference.

7. The method of claim 6, further comprising generating a user experience based upon the updated inference.

8. The method of claim 1, wherein the data comprises a natural language expression, and wherein the local service comprises a local natural language expression (NLE) evaluation process.

9. The method of claim 8, further comprising identifying a unique term in the natural language expression, wherein the unique term is identified using the local knowledge base.

10. The method of claim 8, further comprising:

identifying a prefix in the natural language expression, wherein the prefix identifies a third party application; and

comparing the natural language expression against a third party grammar associated with the third party application.

11. The method of claim 8, further comprising:

identifying at least one hint from the third party knowledge base; and

wherein sending an initial local service result to the remote service for analysis comprises sending the at least one hint from the third party grammar.

12. The method of claim 1, further comprising:

receiving an intermediate remote service result;

comparing the intermediate local service result and the intermediate remote service result; and

based upon the comparison, generating a final result.

13. A hybrid client/server system, the system comprising:

a client device comprising;

at least one client processor; and

client computer storage media encoding computer executable instructions perform a client method comprising;

receiving a natural language expression;

sending the natural language expression to a remote speech recognition process;

processing the natural language expression to generate an initial local interpretation of the natural language expression, wherein the initial local interpretation comprises a first partial determination, and wherein processing the natural language expression comprises;

loading a local knowledge base;

loading a third party knowledge base; and

processing the natural language expression using the local knowledge base and the third party knowledge base;

receiving an initial remote interpretation of the natural language expression from a distributed network; and

the distributed network comprising at least one server, the server comprising;

at least one server processor; and

server computer storage media encoding computer executable instructions that, when executed by the at least one server processor, perform a server method comprising;

receiving the natural language expression from the client device;

processing the natural language expression in parallel with the client device to generate the initial remote interpretation of the natural language expression, wherein the initial remote interpretation comprises a second partial determination generated during local processing;

sending the initial remote interpretation of the language expression to the client device;

receiving an the initial local interpretation of the natural language expression;

updating the initial remote interpretation based upon the initial local interpretation to generate an intermediate remote interpretation; and

sending the intermediate remote interpretation to the client device.

14. The system of claim 13, wherein the initial local interpretation comprises at least one third party hint.

15. A system comprising:

at least one processor; and

memory encoding computer executable instructions that, when executed by the at least one processor, cause the system to perform operations comprising;

receive, a signal comprising the data;

send, to at least one remote device, at least a subset of the data;

process, the data using a local service, wherein processing the data comprises;

loading a local knowledge base;

loading a third party knowledge base; and

processing the data using the local knowledge base and the third party knowledge base;

send an initial local service result to a remote service of the at least one remote device for analysis, wherein the initial local service result comprises a first partial determination generated during local processing;

receive, from the remote service, an initial remote service result while processing the data using the local service, wherein the initial remote service comprises a second partial determination; and

generate using the local service, an intermediate local service result, wherein generating the intermediate local service result comprises updating the initial local service result based upon the initial remote service result.

16. The system of claim 15, wherein the data comprises a natural language expression, and wherein the local service comprises a local natural language expression (NLE) evaluation process.

17. The system of claim 16, further comprising instructions that, when executed by the at least one processor, cause the system identify a unique term in the natural language expression, wherein the unique term is identified using the local knowledge base.

18. The system of claim 16, further comprising instructions that, when executed by the at least one processor, cause the system to perform operations comprising:

identify a prefix in the natural language expression, wherein the prefix identifies a third party application; and

compare the natural language expression against a third party grammar associated with the third party application.

19. The system of claim 16, further comprising instructions that, when executed by the at least one processor, cause the system to perform operations comprising:

identifying at least one hint from the third party knowledge base; and

wherein sending an initial local service result to the remote service for analysis comprises sending the at least one hint from the third party grammar.

20. The system of claim 15, further comprising instructions that, when executed by the at least one processor, cause the system to perform operations comprising:

receiving an intermediate remote service result;

comparing the intermediate local service result and the intermediate remote service result; and

based upon the comparison, generating a final result.