DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims are directed toward an abstract idea without significantly more.
Claims 1-4 are directed toward a method, claims 5-12 are directed toward a method, and claims 13-20 are directed toward a system to implement the method of claims 5-12.
Claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) and does not include additional elements that amount significantly more than the judicial exception.
Step 1
Claims 1 is directed toward a “method”, and thus falls within a statutory category under the most recent guidelines of 35 U.S.C. 101.
Step 2A, Prong 1
Claim 1 recites the steps of “… a first machine learning model to determine first model output data collected over a period of time”; “processing the first model output data to determine first variance data corresponding to a first range of values of a first parameter of the first machine learning model, the first range of values represented in the first model output data”; “…operating, by a second device having the first characteristic, the first machine learning model to determine second model output data”; “processing the second model output data to determine second variance data corresponding to a second range of values of the first parameter, the second range of values represented in the second model output data”; “processing the first variance data and the second variance data to determine at least one value”; “applying the at least one value to the first machine learning model to determine adjusted model data”; “processing the first variance data and the second variance data to determine a first number of training steps”; “performing machine learning training by modifying the adjusted model data over the first number of training steps to determine a second machine learning model, the second machine learning model corresponding to devices having the first characteristic”; and “sending, to the first device and to the second device, the second machine learning model.” These limitations collectively recite the collection and evaluation of information, including language evaluation and processing using machine learning models. As characterized by the USPTO guidance and case law, such activities fall within the abstract-idea groupings of mental processes (e.g. observations, evaluations, and judgments that could be performed in the human mind or with pen and paper) and organizing /transmitting information. Reference can be made to latest patent eligibility guidelines. Accordingly, claim 1 recites an abstract idea.
Step 2A, Prong 2
The claim is implemented on a “computer.” The use of a generic computer components performing their well-understood, routine, and conventional functions of storing and executing instructions, receiving requests, and sending content.
The claim does not recite any specific improvement to computer functionality (e.g., a particular translation algorithm, model architecture, data structure, memory organization, caching mechanism, latency-reduction technique, or network protocol that improves the operation of the computer or network). Nor does it effect a transformation of a physical article or use the abstract idea in any other manner that imposes a meaningful limit on the claim’s scope. Therefore, the claim does not integrate the abstract idea into a practical application under Step 2A, Prong 2.
Step 2B
Beyond the abstract idea, the additional elements are the generic “computer,” “device”(s) performing their conventional functions. Implementing the abstract idea on generic computer components does not amount to significantly more. Alice, 573 U.S. at 223–24).
The ordered combination of limitations mirrors the abstract idea itself performed using routine computer operations. There is no recited unconventional hardware, no technical improvement to the functioning of the computer itself, and no nonconventional arrangement of known components etc.
Accordingly, claim 1 does not include an “inventive concept” sufficient to transform the abstract idea into a patent-eligible application.
Therefore , claim 1 is directed to an abstract idea and does not recite additional elements that integrate the exception into a practical application or amount to significantly more than the exception itself. Claim 1 is therefore rejected under 35 U.S.C. § 101. Dependent claims 2-4 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 5, is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) and does not include additional elements that amount significantly more than the judicial exception.
Step 1
Claims 5 is directed toward a “method”, and thus falls within a statutory category under the most recent guidelines of 35 U.S.C. 101.
Step 2A, Prong 1
Claim 5 recites the steps of “… receiving first model data corresponding to a first machine learning model”; “processing, by a first device corresponding to a first characteristic, first input data using the first machine learning model to determine first output data collected over a period of time”; “determining, using the first output data, a first mean value corresponding to a first parameter of the first machine learning model; determining, using at least the first output data and the first mean value, first variance data corresponding to the first parameter”; “receiving second variance data corresponding to the first parameter, the second variance data representing operation of the first machine learning model by at least one second device independent from the first characteristic”; “processing the first variance data, the second variance data, and the first model data to train a second machine learning model corresponding to devices having the first characteristic”; and “causing second model data corresponding to the second machine learning model to be stored by the first device.” These limitations collectively recite the collection and evaluation of information, including language evaluation and processing using machine learning models. As characterized by the USPTO guidance and case law, such activities fall within the abstract-idea groupings of mental processes (e.g. observations, evaluations, and judgments that could be performed in the human mind or with pen and paper) and organizing /transmitting information. Reference can be made to latest patent eligibility guidelines. Accordingly, claim 1 recites an abstract idea.
Step 2A, Prong 2
The claim is implemented on a “computer.” The use of a generic computer components performing their well-understood, routine, and conventional functions of storing and executing instructions, receiving requests, and sending content.
The claim does not recite any specific improvement to computer functionality (e.g., a particular translation algorithm, model architecture, data structure, memory organization, caching mechanism, latency-reduction technique, or network protocol that improves the operation of the computer or network). Nor does it effect a transformation of a physical article or use the abstract idea in any other manner that imposes a meaningful limit on the claim’s scope. Therefore, the claim does not integrate the abstract idea into a practical application under Step 2A, Prong 2.
Step 2B
Beyond the abstract idea, the additional elements are the generic “computer,” “device”(s) performing their conventional functions. Implementing the abstract idea on generic computer components does not amount to significantly more. Alice, 573 U.S. at 223–24).
The ordered combination of limitations mirrors the abstract idea itself performed using routine computer operations. There is no recited unconventional hardware, no technical improvement to the functioning of the computer itself, and no nonconventional arrangement of known components etc.
Accordingly, claim 5 does not include an “inventive concept” sufficient to transform the abstract idea into a patent-eligible application.
Therefore , claim 5 is directed to an abstract idea and does not recite additional elements that integrate the exception into a practical application or amount to significantly more than the exception itself. Claim 5 is therefore rejected under 35 U.S.C. § 101. Dependent claims 6-12 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claims 13-20 are directed toward a system that implements to method of claims 5-12, and are similar in scope and content, and therefore are rejected under similar rationale.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-7, 10-22 of U.S. Patent No. 12,094,451. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are similar in scope and content of the patented claims 1-7, 10-22 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 1-20 are to be found in patented claims 1-7, 10-22 (as the application claims 1-20 fully encompasses patented claims 1-7, 10-22). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1-7, 10-22 of the patent is in effect a “species” of the “generic” invention of the application claims 1-20. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 1-20 is anticipated by claims 1-7, 10-22 of the patent, it is not patentably distinct from of the patented claims.
Application No: 18/776,657
Patent No: 12,094,451
1. A computer-implemented method comprising: operating, by a first device having a first characteristic, a first machine learning model to determine first model output data collected over a period of time; processing the first model output data to determine first variance data corresponding to a first range of values of a first parameter of the first machine learning model, the first range of values represented in the first model output data; operating, by a second device having the first characteristic, the first machine learning model to determine second model output data; processing the second model output data to determine second variance data corresponding to a second range of values of the first parameter, the second range of values represented in the second model output data; processing the first variance data and the second variance data to determine at least one value; applying the at least one value to the first machine learning model to determine adjusted model data; processing the first variance data and the second variance data to determine a first number of training steps; performing machine learning training by modifying the adjusted model data over the first number of training steps to determine a second machine learning model, the second machine learning model corresponding to devices having the first characteristic; and sending, to the first device and to the second device, the second machine learning model.
1. A computer-implemented method for updating machine learning models, the method comprising: determining first model data corresponding to a first machine learning model corresponding to a first function, the first model data comprising a first value corresponding to a first parameter; sending the first model data to a first device having a first characteristic; sending the first model data to a second device having a second characteristic different from the first characteristic; receiving first variance data corresponding to the first parameter, the first variance data based at least in part on a first range of values corresponding to a first plurality of devices having the first characteristic, wherein the first plurality of devices comprises the first device; receiving second variance data corresponding to the first parameter, the second variance data based at least in part on a second range of values corresponding to a second plurality of devices having the second characteristic; based at least in part on the first variance data and the second variance data, determining third variance data corresponding to the first parameter, the third variance data corresponding to at least the first plurality of devices and the second plurality of devices; sending the third variance data to the first device; sending the third variance data to the second device; based at least in part on the first variance data, the second variance data, and the first model data, determining second model data corresponding to a second machine learning model corresponding to the first function, the second model data comprising a second value corresponding to the first parameter; sending the second model data to the first device, wherein the first device is configured to perform processing of further input data using the second machine learning model; and sending the second model data to the second device.
2. The computer-implemented method of claim 1, wherein the first model output data comprises first output data and second output data and the method further comprises, by the first device: receiving first input data; processing the first input data using the first machine learning model to determine the first output data; receiving second input data; processing the second input data using the first machine learning model to determine the second output data; based at least in part on the first output data and the second output data, determining a first mean value corresponding to the first parameter; based at least in part on the first output data, the second output data, and the first mean value, determining standard deviation data corresponding to the first parameter; and determining the first variance data using the standard deviation data.
2. The computer-implemented method of claim 1, further comprising, by the first device: receiving first input data; processing the first input data using the first machine learning model to determine first output data; receiving second input data; processing the second input data using the first machine learning model to determine second output data; based at least in part on the first output data and the second output data, determining a first mean value corresponding to the first parameter; based at least in part on the first output data, the second output data, and the first mean value, determining standard deviation data corresponding to the first parameter; and determining the first variance data using the standard deviation data.
3. The computer-implemented method of claim 1, further comprising: receiving, by at least one remote device, third variance data corresponding to a third device, the third device independent from the first characteristic; processing, by the at least one remote device, the first variance data, the second variance data, and the third variance data, to determine a third machine learning model; and sending the third machine learning model to the first device, the second device and the third device.
3. The computer-implemented method of claim 1, further comprising: after sending the third variance data to the first device, receiving third model data corresponding to a third machine learning model corresponding to the first function, the third machine learning model trained for operation by devices having the first characteristic; and after sending the third variance data to the second device, receiving fourth model data corresponding to a fourth machine learning model corresponding to the first function, the fourth machine learning model trained for operation by devices having the second characteristic, wherein determining the second model data is further based at least in part on the third model data and the fourth model data.
4. The computer-implemented method of claim 3, further comprising: receiving, by the at least one remote device, fourth variance data corresponding to a fourth device, the fourth device independent from the first characteristic; processing, by the at least one remote device, at least the third variance data and the fourth variance data to determine fifth variance data corresponding to a plurality of devices; and sending, by the at least one remote device to at least one recipient device, wherein determination of the first number of training steps is further based at least in part on the fifth variance data.
4. The computer-implemented method of claim 3, further comprising, by the first device: processing the first variance data and the second variance data to determine at least one value; applying the at least one value to the first model data to determine adjusted model data; processing the first variance data and the second variance data to determine a first number of training steps; and performing machine learning training by modifying the adjusted model data over the first number of training steps to determine the third model data.
5. A computer-implemented method comprising: receiving first model data corresponding to a first machine learning model; processing, by a first device corresponding to a first characteristic, first input data using the first machine learning model to determine first output data collected over a period of time; determining, using the first output data, a first mean value corresponding to a first parameter of the first machine learning model; determining, using at least the first output data and the first mean value, first variance data corresponding to the first parameter; receiving second variance data corresponding to the first parameter, the second variance data representing operation of the first machine learning model by at least one second device independent from the first characteristic; processing the first variance data, the second variance data, and the first model data to train a second machine learning model corresponding to devices having the first characteristic; and causing second model data corresponding to the second machine learning model to be stored by the first device.
5. A computer-implemented method for updating machine learning models, the method comprising: receiving first variance data corresponding to a first parameter of a first machine learning model, the first variance data corresponding to a first plurality of devices having a first characteristic, wherein the first plurality of devices comprises a first device; receiving second variance data corresponding to the first parameter, the second variance data corresponding to a second plurality of devices having a second characteristic; based at least in part on the first variance data and the second variance data, determining third variance data corresponding to the first parameter, the third variance data corresponding to at least the first plurality of devices and the second plurality of devices; sending the third variance data to at least one device corresponding to the first characteristic; sending the third variance data to a second device corresponding to the second characteristic; based at least in part on the first variance data, the second variance data, and the first machine learning model, determining an updated machine learning model; and sending updated model data representing the updated machine learning model to the first device and the second device, wherein the first device is configured to perform processing of further input data using the updated machine learning model.
6. The computer-implemented method of claim 5, further comprising: processing the first variance data and the second variance data to determine a first number of training steps, wherein training the second machine learning model comprises performing the first number of training steps.
6. The computer-implemented method of claim 5, further comprising: after sending the third variance data to the first device, receiving first model data corresponding to a third machine learning model trained for operation by devices having the first characteristic; and after sending the third variance data to the second device, receiving second model data corresponding to a fourth machine learning model trained for operation by devices having the second characteristic, wherein determining the updated machine learning model is further based at least in part on the first model data and the second model data.
7. The computer-implemented method of claim 5, further comprising: determining an estimated value for the first parameter corresponding to operation of the first machine learning model by at least the second device; and estimating the second variance data based at least in part on the estimated value.
7. The computer-implemented method of claim 6, further comprising, by the first device: processing the first variance data and the second variance data to determine at least one value; determining adjusted model data using the at least one value and the first machine learning model; and determining the third machine learning model from the adjusted model data.
8. The computer-implemented method of claim 5, further comprising: processing the first variance data and the second variance data to determine at least one value; determining adjusted model data using the at least one value and the first model data; and determining the second machine learning model from the adjusted model data.
7. The computer-implemented method of claim 6, further comprising, by the first device: processing the first variance data and the second variance data to determine at least one value; determining adjusted model data using the at least one value and the first machine learning model; and determining the third machine learning model from the adjusted model data.
9. The computer-implemented method of claim 5, further comprising: receiving third variance data representing operation of the first machine learning model by a third device, the third device corresponding to the first characteristic, wherein the first variance data is based at least in part on the third variance data.
10. The computer-implemented method of claim 5, further comprising: determining an estimated value for the first parameter corresponding to operation of the first machine learning model by at least a third device corresponding to a third characteristic; and determining the third variance data based at least in part on the estimated value.
10. The computer-implemented method of claim 5, further comprising: determining difference data representing at least one difference between the first machine learning model and the second machine learning model, wherein the second model data includes the difference data.
12. The computer-implemented method of claim 5, further comprising: determining difference data representing at least one difference between the first machine learning model and the updated machine learning model, wherein the updated model data includes the difference data.
11. The computer-implemented method of claim 5, further comprising: receiving, by at least one remote device, third variance data corresponding to a third device, the third device independent from the first characteristic; processing, by the at least one remote device, the first variance data, the second variance data, and the third variance data, to determine third model data corresponding to a third machine learning model; and sending the third model data to the first device, the second device, and the third device.
13. The computer-implemented method of claim 5, wherein sending the third variance data to the at least one device corresponding to the first characteristic includes sending the third variance data to the first device.
12. The computer-implemented method of claim 5, wherein the first machine learning model comprises a model configured to detect a wakeword represented in input audio data and the method further comprises: processing, by the first device, first audio data using the first machine learning model to determine first output data indicating detection of a first representation of the wakeword in the first audio data; and processing, by the first device, second audio data using the second machine learning model to determine second output data indicating detection of a second representation of the wakeword in the second audio data.
10. The computer-implemented method of claim 5, further comprising: determining an estimated value for the first parameter corresponding to operation of the first machine learning model by at least a third device corresponding to a third characteristic; and determining the third variance data based at least in part on the estimated value.
11. The computer-implemented method of claim 5, wherein the first machine learning model comprises a model configured to detect a wakeword represented in input audio data.
12. The computer-implemented method of claim 5, further comprising: determining difference data representing at least one difference between the first machine learning model and the updated machine learning model, wherein the updated model data includes the difference data.
13. A system, comprising: at least one processor; and at least one memory comprising instructions that, when executed by the at least one processor, cause the system to: receive first model data corresponding to a first machine learning model; process, by a first device corresponding to a first characteristic, input data using the first machine learning model to determine output data; determine, using the output data, first variance data corresponding to a first parameter of the first machine learning model; receive second variance data corresponding to the first parameter, the second variance data representing operation of the first machine learning model by at least one second device independent from the first characteristic; process the first variance data and the second variance data to determine a first number of training steps; use the first number of training steps and the first model data to train a second machine learning model corresponding to devices having the first characteristic; and cause second model data corresponding to the second machine learning model to be stored by the first device.
14. A system, comprising: at least one processor; and at least one memory comprising instructions that, when executed by the at least one processor, cause the system to: receive first variance data corresponding to a first parameter of a first machine learning model, the first variance data corresponding to a first plurality of devices having a first characteristic, wherein the first plurality of devices comprises a first device; receive second variance data corresponding to the first parameter, the second variance data corresponding to a second plurality of devices having a second characteristic; based at least in part on the first variance data and the second variance data, determine third variance data corresponding to the first parameter, the third variance data corresponding to at least the first plurality of devices and the second plurality of devices; send the third variance data to a first at least one device corresponding to the first characteristic; send the third variance data to a second device corresponding to the second characteristic; based at least in part on the first variance data, the second variance data, and the first machine learning model, determine an updated machine learning model; and send updated model data representing the updated machine learning model to the first device and the second device, wherein the first device is configured to perform processing of further input data using the updated machine learning model.
14. The system of claim 13, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the system to: operate, by the first device, the first machine learning model to determine model output data; and process the model output data to determine the first variance data.
15. The system of claim 14, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the system to: after sending the third variance data to the first device, receive first model data corresponding to a third machine learning model trained for operation by devices having the first characteristic; and after sending the third variance data to the second device, receive second model data corresponding to a fourth machine learning model trained for operation by devices having the second characteristic, wherein determination of the updated machine learning model is further based at least in part on the first model data and the second model data.
15. The system of claim 13, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the system to: determine an estimated value for the first parameter corresponding to operation of the first machine learning model by at least the second device; and estimate the second variance data based at least in part on the estimated value.
16. The system of claim 15, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the first device to: process the first variance data and the second variance data to determine at least one value; determine adjusted model data using the at least one value and the first machine learning model; and determine the third machine learning model from the adjusted model data.
16. The system of claim 13, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the system to: process the first variance data and the second variance data to determine at least one value; apply the at least one value to the first model data to determine adjusted model data; and perform training by modifying the adjusted model data over the first number of training steps to determine the second machine learning model.
18. The system of claim 14, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the first device to: receive first input data; process the first input data using the first machine learning model to determine first output data; receive second input data; process the second input data using the first machine learning model to determine second output data; based at least in part on the first output data and the second output data, determine a first mean value corresponding to the first parameter; based at least in part on the first output data, the second output data, and the first mean value, determine standard deviation data corresponding to the first parameter; and determine the first variance data using the standard deviation data.
17. The system of claim 13, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the system to: receive third variance data corresponding to the first parameter, the third variance data representing operation of the first machine learning model by a third device, the third device corresponding to the first characteristic, wherein the first variance data is based at least in part on the third variance data.
19. The system of claim 14, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the system to: determine an estimated value for the first parameter corresponding to operation of the first machine learning model by at least a third device corresponding to a third characteristic; and determine the third variance data based at least in part on the estimated value.
18. The system of claim 13, wherein the second model data includes difference data representing at least one difference between the first machine learning model and the second machine learning model.
17. The system of claim 15, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the first device to: processing the first variance data and the second variance data to determine a first number of training steps; and training the third machine learning model using the first number of training steps.
19. The system of claim 13, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the system to: receive, by at least one remote device, third variance data corresponding to a third device, the third device not corresponding to the first characteristic; process, by the at least one remote device, the first variance data, the second variance data, and the third variance data, to determine third model data corresponding to a third machine learning model; and send the third model data to the first device, the second device and the third device.
19. The system of claim 14, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the system to: determine an estimated value for the first parameter corresponding to operation of the first machine learning model by at least a third device corresponding to a third characteristic; and determine the third variance data based at least in part on the estimated value.
20. The system of claim 13, wherein the first machine learning model comprises a model configured to detect a wakeword represented in input audio data and wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the system to: process, by the first device, first audio data using the first machine learning model to determine first output data indicating detection of a representation of the wakeword in the first audio data; and process, by the first device, second audio data using the second machine learning model to determine second output data indicating detection of a representation of the wakeword in the second audio data.
20. The system of claim 14, wherein the first machine learning model comprises a model configured to detect a wakeword represented in input audio data.
21. The system of claim 14, wherein the at least one memory further includes instructions that, when executed by the at least one processor, further cause the system to: determine difference data representing at least one difference between the first machine learning model and the updated machine learning model, wherein the updated model data includes the difference data.
22. The system of claim 14, wherein sending the third variance data to the at least one device corresponding to the first characteristic includes sending the third variance data to the first device.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over McMahan et al., (US 2017/0109322) in view of Cho et al., (“FLAME: Federated Learning Across Multi-device Environments”, Proc. ACM Interact. Mob. Wearable Technology, vol.6, No.3, Article 107, September 2022, pages 1-29).
As per claim 1, McMahan et al., teach a computer-implemented method comprising:
operating, by a first device having a first characteristic, a first machine learning model to determine first model output data collected over a period of time (0031);
processing the first model output data to determine first variance data corresponding to a first range of values of a first parameter of the first machine learning model, the first range of values represented in the first model output data (0032);
operating, by a second device having the first characteristic, the first machine learning model to determine second model output data; processing the second model output data to determine second variance data corresponding to a second range of values of the first parameter, the second range of values represented in the second model output data (0031);
processing the first variance data and the second variance data to determine at least one value (0038);
applying the at least one value to the first machine learning model to determine adjusted model data (0038);
processing the first variance data and the second variance data to determine a first number of training steps (0021, 0038);
performing machine learning training by modifying the adjusted model data over the first number of training steps to determine a second machine learning model, the second machine learning model corresponding to devices having the first characteristic (0027)
McMahan et al., teach the method of claim 1. McMahan et al., fail to explicitly teach sending, to the first device and to the second device, the second machine learning model. However, Cho et al., teach sending, to the first device and to the second device, the second machine learning model page 3, section 2.1, paragraph 1). Therefore it would have been obvious to one of ordinary skill in the art before the filing date of the invention to have incorporated the teaching of Cho et al., into the method of McMahan et al., because, this would effectively allow for collaborative training of machine learning models while preserving user privacy (Cho et al., page 1, section 1, paragraph 2).
As per claim 2, McMahan et al., in view of Cho et al., teach the computer-implemented method of claim 1, wherein the first model output data comprises first output data and second output data and the method further comprises, by the first device: receiving first input data (McMahan et al., 0031); processing the first input data using the first machine learning model to determine the first output data (McMahan et al., 0031); receiving second input data (Cho et al., page 3, Section 2.1, paragraph 1); processing the second input data using the first machine learning model to determine the second output data (Cho et al., page 3, Section 2.1, paragraph 1); based at least in part on the first output data and the second output data, determining a first mean value corresponding to the first parameter (Cho et al., page 5, Fig.3, Section 3.1, paragraph 4); based at least in part on the first output data, the second output data, and the first mean value, determining standard deviation data corresponding to the first parameter; and determining the first variance data using the standard deviation data (Cho et al., page 19, Section 5.7, paragraph 7).
As per claim 3, McMahan et al., in view of Cho et al., teach the computer-implemented method of claim 1, further comprising: receiving, by at least one remote device, third variance data corresponding to a third device, the third device independent from the first characteristic (Cho et al., Fig.2, page 4, Section 3.1, paragraph 3); processing, by the at least one remote device, the first variance data, the second variance data (McMahan et al., 0038), and the third variance data, to determine a third machine learning model (McMahan et al., 0038); and sending the third machine learning model to the first device, the second device and the third device (McMahan et al., 0038).
As per claim 4, McMahan et al., in view of Cho et al., teach the computer-implemented method of claim 3, further comprising: receiving, by the at least one remote device, fourth variance data corresponding to a fourth device, the fourth device independent from the first characteristic (Cho et al., Fig.2, page 4, Section 3.1, paragraph 3); processing, by the at least one remote device, at least the third variance data and the fourth variance data to determine fifth variance data corresponding to a plurality of devices (McMahan et al., 0038); and sending, by the at least one remote device to at least one recipient device, wherein determination of the first number of training steps is further based at least in part on the fifth variance data (0038 – 0039).
As per claim 5, McMahan in view of Cho et al., teach a computer-implemented method comprising:
receiving first model data corresponding to a first machine learning model McMahan et al., (0031);
processing, by a first device corresponding to a first characteristic, first input data using the first machine learning model to determine first output data collected over a period of time (0032);
determining, using the first output data, a first mean value corresponding to a first parameter of the first machine learning model (0038);
determining, using at least the first output data and the first mean value, first variance data corresponding to the first parameter;
processing the first variance data, the second variance data, and the first model data to train a second machine learning model corresponding to devices having the first characteristic (0044).
McMahan et al., teach the computer-implemented method according to claims 5. McMahan et al., fail to explicitly teach receiving second variance data corresponding to the first parameter, the second variance data representing operation of the first machine learning model by at least one second device independent from the first characteristic; and causing second model data corresponding to the second machine learning model to be stored by the first device. However Cho et al., do teach receiving second variance data corresponding to the first parameter, the second variance data representing operation of the first machine learning model by at least one second device independent from the first characteristic; and causing second model data corresponding to the second machine learning model to be stored by the first device (Cho et al., page 19, Section 5.7, paragraph 7, page 3, Section 2.1, paragraphs 1).
Therefore it would have been obvious to one of ordinary skill in the art before the filing date of the invention to have incorporated the teaching of Cho et al., into the method of McMahan et al., because, this would effectively allow for collaborative training of machine learning models while preserving user privacy (Cho et al., page 1, section 1, paragraph 2). 6. The computer-implemented method of claim 5, further comprising: processing the first variance data and the second variance data to determine a first number of training steps, wherein training the second machine learning model comprises performing the first number of training steps.
As per claim 6, McMahan et al., in view of Cho et al., teach the computer-implemented method of claim 5, further comprising: processing the first variance data and the second variance data to determine a first number of training steps, wherein training the second machine learning model comprises performing the first number of training steps (McMahan et al., 0021).
As per claim 7, McMahan et al., in view of Cho et al., teach the computer-implemented method of claim 5, further comprising: determining an estimated value for the first parameter corresponding to operation of the first machine learning model by at least the second device; and estimating the second variance data based at least in part on the estimated value (0027-0029).
As per claim 8, McMahan et al., in view Cho et al., teach the computer-implemented method of claim 5, further comprising: processing the first variance data and the second variance data to determine at least one value; determining adjusted model data using the at least one value and the first model data; and determining the second machine learning model from the adjusted model data (0021, 0038, 0040).
As per claim 9, McMahan et al., in view of Cho et al., teach the computer-implemented method of claim 5, further comprising: receiving third variance data representing operation of the first machine learning model by a third device, the third device corresponding to the first characteristic, wherein the first variance data is based at least in part on the third variance data (Cho et al., Fig.2, page 4, Section 3.1, paragraph 3).
As per claim 10, McMahan et al., in view of Cho et al., teach the computer-implemented method of claim 5, further comprising: determining difference data representing at least one difference between the first machine learning model and the second machine learning model, wherein the second model data includes the difference data (Cho et al., page 21, Section 7, paragraph 4).
As per claim 11, McMahan et al., in Cho et al., teach the computer-implemented method of claim 5, further comprising: receiving, by at least one remote device, third variance data corresponding to a third device, the third device independent from the first characteristic (Cho et al., Fig.2, page 4, Section 3.1, paragraph 3); processing, by the at least one remote device, the first variance data, the second variance data, and the third variance data, to determine third model data corresponding to a third machine learning model (McMahan et al., 0038); and sending the third model data to the first device, the second device, and the third device (McMahan et al., 0039).
Claims 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over McMahan et al., (US 2017/0109322) in view of Cho et al., (“FLAME: Federated Learning Across Multi-device Environments”, Proc. ACM Interact. Mob. Wearable Technology, vol.6, No.3, Article 107, September 2022, pages 1-29), and further in view of Mandal et al., (US 2020/0349228 A1).
As per claims 12 and 20, McMahan et al., in view of Cho et al., teach the method of claims 5 and 13. However, McMahan et al., in view of Cho et al., fail to explicitly teach the claimed, wherein the first machine learning model comprises a model configured to detect a wakeword represented in input audio data and the method further comprises: processing, by the first device, first audio data using the first machine learning model to determine first output data indicating detection of a first representation of the wakeword in the first audio data; and processing, by the first device, second audio data using the second machine learning model to determine second output data indicating detection of a second representation of the wakeword in the second audio data. Mandal et al., do teach the claimed wherein the first machine learning model comprises a model configured to detect a wakeword represented in input audio data and the method further comprises: processing, by the first device, first audio data using the first machine learning model to determine first output data indicating detection of a first representation of the wakeword in the first audio data; and processing, by the first device, second audio data using the second machine learning model to determine second output data indicating detection of a second representation of the wakeword in the second audio data (0050 – 0053). Therefore, it would have been obvious to one of ordinary skill in the art before the filing date of the invention to incorporate the method/system of Mandal et al., in the method/system of McMahan et al., in view Cho et al., because, this would effectively execute specific functionality based on user’s spoken commands and improve human-computer interactions (Mandal et al., 0002 – 0003).
System claims 14-19 are similar in scope and content of method claims 6-11, and are rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
Nakayama et al., (US 2021/0406782 A1) teach a system for decentralized federated learning is provided. The system comprises agents and aggregators coupled to a communication network. Each agent comprises a data collector collecting raw data; a memory storing the collected raw data and a local machine learning model; and a processor training the local machine learning model. Each aggregator comprises a model collector collecting the local machine learning models; a memory storing the collected local machine learning models; and a processor creating a cluster machine learning model from the local machine learning models. The aggregators communicate with each other and exchange the cluster machine learning models to create a semi-global machine learning model. Each of the aggregators sends the semi-global machine learning model to the associated agents. Each of the agents updates the local machine learning model with the semi-global machine learning model.
Miao et al., (US 2015/0242760 A1) teach that machine learning may be personalized to individual users of computing devices, and can be used to increase machine learning prediction accuracy and speed, and/or reduce memory footprint. Personalizing machine learning can include hosting, by a computing device, a consensus machine learning model and collecting information, locally by the computing device, associated with an application executed by the client device. Personalizing machine learning can also include modifying the consensus machine learning model accessible by the application based, at least in part, on the information collected locally by the client device. Modifying the consensus machine learning model can generate a personalized machine learning model. Personalizing machine learning can also include transmitting the personalized machine learning model to a server that updates the consensus machine learning model.
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/VIJAY B CHAWAN/Primary Examiner, Art Unit 2658