DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the remarks filed 17 July 2026.
Claims 1-20 are pending. Claims 1, 12, and 16 are independent claims.
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 remain rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
According to Step 1 of the two Step analysis, claims 1-11 are directed toward a system (machine). Claims 12-15 are directed toward a non-transitory computer-readable medium (manufacture). Claims 16-20 are directed toward a method (process). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
The claim recites in part:
separate the data into one or more clusters, each cluster based at least one a feature from one or more models (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an observation to determine features of the one or more models and separating data into clusters based on that feature)
determine an accuracy associated with each respective feature of a subset dataset of the data of each cluster (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation to determine the accuracy associated with each feature subset dataset of each cluster)
automatically detect, based at least in part on the accuracy, that the subset dataset of the data is outside of a threshold accuracy (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation to determine that the accuracy is outside an accuracy threshold)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
at least one processor and at least one memory containing instructions that, when executed, cause the processor to:
This element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional elements:
receive a continuous pipeline of crowd sourced data
receive verification of the subset dataset from the crowd source service
in response to automatically detecting that the accuracy being outside of the threshold accuracy, automatically forward the subset to a crowd source service
These additional elements of receiving and transmitting data are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional elements:
add the verified subset dataset to at least one model of the one or more models
The adding data to at least one model of the one or more models is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
at least one processor and at least one memory containing instructions that, when executed, cause the processor to:
This element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional elements:
receive a continuous pipeline of crowd sourced data
receive verification of the subset dataset from the crowd source service
in response to automatically detecting that the accuracy being outside of the threshold accuracy, automatically forward the subset to a crowd source service
These additional elements of receiving and transmitting data are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional elements:
add the verified subset dataset to at least one model of the one or more models
The adding data to at least one model of the one or more models is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
With respect to claim 2, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
This claim is directed toward the same abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the elements:
generate a second model based on the received verification subset dataset
upon determining that an accuracy of the second model exceeds the at least one model, update the at least one model with the second model
The generating and updating the model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
generate a second model based on the received verification subset dataset
upon determining that an accuracy of the second model exceeds the at least one model, update the at least one model with the second model
The generating and updating the model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 3:
With respect to claim 3, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the elements:
determine a feature metric associated with the respective feature (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation to determine a feature metric associated with a feature)
define a centroid based on the feature metric (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation to define a centroid based on the feature metric)
determine a measured metric associated with the data in each cluster (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation to determine a measured metric associated with the data in each cluster)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements to consider under Step 2B.
Claim 4:
With respect to claim 4, the claim depends upon claim 3. The analysis of claim 3 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the elements:
select the subset dataset of each cluster based on the measured metric matching the feature metric associated with the centroid within a threshold (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing a judgement to select the subset dataset based on the measured metric matching)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements to consider under Step 2B.
Claim 5:
With respect to claim 5, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the elements:
determine the accuracy of the second model (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation to determine the accuracy of a model)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
receive a labeled subset dataset from the crowd source service
These additional elements of receiving data are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional elements:
add the labeled subset dataset to the at least one model to create a combined model dataset
generate a second model based on the combined model dataset
The adding the labeled subset dataset and generating a second model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
receive a labeled subset dataset from the crowd source service
These additional elements of receiving data are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional elements:
add the labeled subset dataset to the at least one model to create a combined model dataset
generate a second model based on the combined model dataset
The adding the labeled subset dataset and generating a second model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 6:
With respect to claim 6, the claim depends upon claim 5. The analysis of claim 5 is incorporated herein by reference.
Step 2A, Prong 1:
This claim is directed toward the same abstract idea identified with respect to claim 5.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the elements:
generate the second model on an ongoing basis
The generating of the model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
generate the second model on an ongoing basis
The generating of the model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 7:
With respect to claim 7, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the elements:
determine the feature is at least one of an accent, gender, or environmental background noise in the data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an observation to determine the feature is one of accent, gender, or environmental background noise)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements to consider under Step 2B.
Claim 8:
With respect to claim 8, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the elements:
wherein the data is comprised of multiple data types, the multiple data types including audio, visual, and text data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an observation to determine the data is one of audio, visual, or text data)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements to consider under Step 2B.
Claim 9:
With respect to claim 9, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
This claim is directed toward the same abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the elements:
wherein the data is configured to be separated into the one or more clusters based on an unsupervised machine learning technique
The use of the unsupervised machine learning technique is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
wherein the data is configured to be separated into the one or more clusters based on an unsupervised machine learning technique
The use of the unsupervised machine learning technique is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 10:
With respect to claim 10, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
This claim is directed toward the same abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the elements:
wherein automatically forward the subset dataset to the crowd source service based on a volume of the subset dataset being outside the threshold accuracy
The additional element of transmitting data to a crowd source service based on a volume is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
The additional element of transmitting data to a crowd source service based on a volume is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 11:
With respect to claim 11, the claim depends upon claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the elements:
wherein the volume of the subset dataset that initiates forwarding to the crowd source service is based on a volume heuristics model that is configured to determine an amount of data predicted to successfully update the at least one model (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation to generate a prediction of the amount of data required to successfully update the at least one model)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements to consider under Step 2B.
Claim 12:
With respect to claim 12, the claim recites the limitations substantially similar to those in claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
This claim is directed toward the same abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
a non-transitory computer-readable medium comprising instructions, when executed by at least one processor of a system, cause the system to:
This element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
a non-transitory computer-readable medium comprising instructions, when executed by at least one processor of a system, cause the system to:
This element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claims 13-15:
With respect to claims 13-15, the claims recite the limitations substantially similar to those in claims 2-3 and 5, respectively. The analysis of claims 2-3 and 5 are incorporated herein by reference.
Claims 16-20:
With respect to claims 16-20, the claims recite the limitations substantially similar to those in claims 1-2, 5-6, and 9, respectively. The analysis of claims 1-2, 5-6, and 9 are incorporated herein by reference.
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 remain rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 and 16-17 of U.S. Patent No. 11809965. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims recite an obvious variant.
Application 18490281
Patent 11809965
Claim 1: A system comprising:
Claim 1: A near real-time data pipeline system for training a model comprising (column 9, lines 29-30):
at least one processor and at least one memory containing instructions that, when executed, cause the at least one processor to:
at least one processor and at least one memory containing instructions that, when executed cause the at least one processor… to: (column 9, lines 34-36)
receive a continuous pipeline of crowd source data
A… data pipeline system (column 9, lines 29-30)
receiving the media data (column 9, line 36)
separate the data into one or more clusters, each cluster based at least one a feature from one or more models
separate the media data into one or more clusters, each cluster of the one or more clusters based on a feature from a first model (column 9, line 37-39)
determine an accuracy associated with each respective feature of a subset dataset of the data of each cluster
determine an accuracy of a subset dataset of the media data of each cluster, the accuracy associated with the feature (column 9, lines 41-43)
automatically detect, based at least in part on the accuracy, that the subset data of the data is outside of a threshold accuracy
automatically detect, based on the accuracy of the subset dataset of the media data, that the subset dataset of the media data is outside of a threshold accuracy (column 9, lines 44-47)
in response to automatically detecting that the accuracy being outside of the threshold accuracy, automatically forward the subset dataset to a crowd source service
in response to automatically detecting that the accuracy being outside of the threshold accuracy, automatically forward the subset dataset to a crowd source service (column 9, lines 48-51)
receive verification of the subset dataset from the crowd source service
receive verification of the subset dataset from the crowd source service (column 9, lines 52-53)
add the verified subset data to at least one model of the one or more models
add the verified subset dataset to the first model (column 9, line 54)
Claim 2: The system of claim 1, the at least one processor further configured to:
Claim 2: The system of claim 1, the at least one processor further configured to: (column 9, lines 55-56)
generate a second model based on the received verified subset dataset
generate a second model based on the received verified subset dataset (column 9, lines 57-58)
upon determining that an accuracy of the second model exceeds the at least one model, update the at least one model with the second model
upon determining that an accuracy of the second model exceeds the first model, update the first model with the second model (column 9, line 59-61)
Claim 3: The system of claim 1, wherein the at least one processor that determines the accuracy of the subset dataset is configured to:
Claim 3: They system of claim 1, wherein the at least one processor that determines the accuracy of the subset dataset is configured to: (column 9, lines 62-64)
determine a feature metric associated with the respective feature
determine a feature metric associated with the feature from an analysis of the first model (column 9, lines 65-66)
define a centroid based on the feature metric
define a centroid based on the feature metric (column 9, line 67)
determine a measured metric associated with the data in each cluster
determine a measured metric associated with the media data in each cluster (column 10, lines 2-3)
Claim 4: The system of claim 3, wherein the at least one processor is further configured to: select the subset dataset of each cluster based on the measured metric matching the feature metric associated with the centroid within a threshold.
Claim 4: The system of claim 3, wherein the at least one processor is further configured to: select the subset dataset of each cluster based on the measured metric matching the feature metric associated with the centroid within a threshold (column 10, lines 4-8).
Claim 5: The system of claim 1, wherein the at least one processor is further configured to:
Claim 5: The system of claim 1, wherein the at least one processor is further configured to: (column 10, lines 10-11)
receive a labelled subset dataset from the crowd source service
receive a labelled subset dataset from the crowd source service (column 10, lines 12-13)
add the labelled subset dataset to the at least one model to create a combined model dataset
add the labelled subset dataset to the first model to create a combined model dataset (column 10, lines 14-15)
generate a second model based on the combined model dataset
generate the second model based on the combined model dataset (column 10, lines 16-17)
determine the accuracy of the second model
determine the accuracy of the second model (column 10, line 18)
Claim 6: The system of claim 5, wherein the at least one processor is configured to generate the second model on an ongoing basis.
Claim 6: The system of claim 1, wherein the at least one processor is configured to generate the second model on an ongoing basis (column 10, lines 19-21).
Claim 7: The system of claim 1, wherein the at least one processor is configured to determine the feature is at least one of an accent, gender, or environmental background noise in the data.
Claim 7: The system of claim 1, wherein the at least one processor is configured to determine the feature from the first model based on at least one of an accent, gender, or environmental background noise in the media data (column 10, lines 22-25).
Claim 8: The system of claim 1, wherein the data is comprised of multiple data types, the multiple data types including audio, visual, and text data.
Claim 8: The system of claim 1, wherein the media data is comprised of multiple data types, the multiple data types including audio, visual, and text data (column 10, lines 26-28).
Claim 9: The system of claim 1, wherein the data is configured to be separated into the one or more clusters based on an unsupervised machine learning technique.
Claim 9: The system of claim 1, wherein the media data is configured to be separated into the one or more clusters based on an unsupervised machine learning technique (column 10, lines 30-32).
Claim 10: The system of claim 1, wherein the at least one processor is further configured to automatically forward the subset dataset to the crowd source service based on a volume of the subset dataset being outside the threshold accuracy.
Claim 10: The system of claim 1, wherein the at least one processor is further configured to automatically forward the subset dataset to the crowd source service based on a volume of the subset dataset being above the threshold accuracy (column 10, lines 33-36).
Claim 11: The system of claim 10, wherein the volume of the subset dataset that initiates forwarding to the crowd source service is based on a volume heuristics model that is configured to determine an amount of data predicted to successfully update the at least one model.
Claim 11: The system of claim 10, wherein the volume of the subset dataset that initiates forwarding to the crowd source service is based on a volume heuristics model that is configured to determine an amount of data predicted to successfully update the first model (column 10, lines 37-41).
Claim 12: A non-transitory computer-readable medium comprising instructions, when executed by at least one processor of a system, causes the system to:
Claim 12: A non-transitory computer-readable medium comprising instructions, when executed by a near real-time data pipeline system, the instructions cause the computing system to (column 10, lines 42-45)
receive a continuous pipeline of crowd sourced data
receive a continuous pipeline of media data, wherein the media data includes at least crowd sourced data (column 10, lines 46-47)
separate the data into one or more clusters, each cluster based at least on a feature from one or more models
… separate media data into one or more clusters, each cluster of the one or more clusters based on a feature from a first model (column 10, lines 48-50)
determine an accuracy associated with each respective feature of a subset dataset of the data of each cluster
… determine an accuracy of a subset dataset of the media data of each cluster, the accuracy associated with the feature (column 10, lines 52-54)
automatically detect, based at least in part on the accuracy, that the subset dataset of the data is outside of a threshold accuracy
automatically detect, based on the accuracy of the subset dataset of the media data, that the subset dataset of the media data is outside of a threshold accuracy (column 10, lines 55-57)
in response to automatically detecting that the accuracy being outside of the threshold accuracy, automatically forward the subset dataset to a crowd source service
in response to automatically detecting that the accuracy being outside of the threshold accuracy, automatically forward the subset dataset to a crowd source service (column 10, lines 57-60)
receive verification of the subset dataset from the crowd source service
receive verification of the subset dataset from the crowd source service (column 10, lines 61-62)
add the verified subset dataset to at least one model of the one or more models
add the verified subset dataset to the first model (column 10, lines 62-63)
Claim 13: The non-transitory computer-readable medium of claim 12, wherein the at least one processor is further configured to:
Claim 13: The non-transitory computer-readable medium of claim 12, the instructions further configured to (column 10, lines 64-65)
generate a second model based on the received verified subset dataset
generate a second model based on the received verification subset dataset (column 10, lines 66-67)
upon determining that an accuracy of the second model exceeds the at least one model, update the at least one model with the second model.
upon determining that an accuracy of the second model exceeds the first model, update the first model with a second model (column 11, lines 1-3).
Claim 14: The non-transitory computer-readable medium of claim 12, wherein the at least one processor is further configured to:
Claim 14: The non-transitory computer-readable medium of claim 12, wherein the instructions that determine the accuracy of the subset dataset are configured to: (column 11, lines 4-6)
determine a feature metric associated with the respective feature
determine a feature metric associated with the feature from an analysis of the first model (column 11, lines 7-8)
define a centroid based on the feature metric
define a centroid based on the feature metric (column 11, line 9)
determine a measured metric associated with the data in each cluster.
… determine a measured metric associated with the media data in each cluster (column 11, lines 11-12)
Claim 15: The non-transitory computer-readable medium of claim 12, wherein the at least one processor is further configured to:
Claim 5: The system of claim 1, wherein the at least one processor is further configured to: (column 10, lines 10-11)
receive a labelled subset dataset from the crowd source service
receive a labelled subset dataset from the crowd source service (column 10, lines 12-13)
add the labelled subset dataset to the at least one model to create a combination model dataset
add the labelled subset dataset to the first model to create a combined model dataset (column 10, lines 14-15)
generate a second model based on the combined model dataset
generate the second model based on the combined model dataset (column 10, lines 16-17)
determine the accuracy of the second model.
determine the accuracy of the second model (column 10, line 18)
Claim 16: A method comprising:
Claim 16: A method of training a model by a near real-time data pipeline, the method comprising: (column 11, lines 21-22)
receiving a continuous pipeline of crowd sourced data
receiving a continuous pipeline of media data, wherein the media data includes at least crowd sourced data (column 11, lines 23-24)
separating the data into one or more clusters, each cluster based at least on a feature from one or more models
… separating media data into one or more clusters, each cluster of the one or more clusters based on a feature from a first model (column 11, lines 25-27)
determining an accuracy associated with each respective feature of a subset dataset of the data of each cluster
… determining an accuracy of a subset dataset of the media data of each cluster, the accuracy associated with the feature (column 11, lines 28-30)
automatically detecting, based at least in part on the accuracy, that the subset dataset of the data is outside of a threshold accuracy
automatically detecting, based on the accuracy of the subset dataset of the media data, that the subset dataset of the media data is outside of a threshold accuracy (column 11, lines 31-33)
in response to automatically detecting that the accuracy being outside of the threshold accuracy, automatically forwarding the subset dataset to a crowd source service
in response to automatically detecting that the accuracy being outside the threshold accuracy, automatically forwarding the subset dataset to a crowd source service (column 11, line 33- column 12, line 3)
receiving verification of the subset dataset from the crowd source service
receiving verification of the subset dataset from the crowd source service (column 12, lines 4-5)
adding the verified subset dataset to at least one model of the one or more models.
adding the verified subset dataset to the first model (column 12, line 6)
Claim 17: The method of claim 16, further comprising:
Claim 17: The method of claim 16, further comprising: (column 12, line 7)
generating a second model based on the received verified subset dataset
generating a second model based on the received verified subset dataset (column 12, lines 8-9)
upon determining that an accuracy of the second model exceeds the at least one model, update the at least one model with the second model.
upon determining that an accuracy of the second model exceeds the first model, update the first model with the second model (column 12, lines 10-12)
Claim 18: The method of claim 16, further comprising:
Claim 5: The system of claim 1, wherein the at least one processor is further configured to: (column 10, lines 10-11)
receiving a labelled subset dataset from the crowd source service
receive a labelled subset dataset from the crowd source service (column 10, lines 12-13)
adding the labelled subset dataset to the at least one model to create a combined model dataset
add the labelled subset dataset to the first model to create a combined model dataset (column 10, lines 14-15)
generating a second model based on the combined model dataset
generate the second model based on the combined model dataset (column 10, lines 16-17)
determining the accuracy of the second model.
determine the accuracy of the second model (column 10, line 18)
Claim 19: The method of claim 18, wherein generating the second model is an ongoing basis.
Claim 6: The system of claim 1, wherein the at least one processor is configured to generate the second model on an ongoing basis (column 10, lines 19-21).
Claim 20: The method of claim 16, wherein the data is configured to be separated into the one or more clusters based on an unsupervised machine learning technique.
Claim 9: The system of claim 1, wherein the media data is configured to be separated into the one or more clusters based on an unsupervised machine learning technique (column 10, lines 30-32).
Claim Rejections - 35 USC § 103
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 1-2, 5-6, 8-13, and 15-20 remain rejected under 35 U.S.C. 103 as being unpatentable over Garera et al. (US 2014/0297570, published 2 October 2014, hereafter Garera) in view of Jeffery et al (US 10614373, patented 7 April 2020, hereafter Jeffery) and further in view of Acharya et al. (US 2014/0292746, published 2 October 2014, hereafter Acharya).
As per independent claim 1, Garera discloses a system comprising:
at least one processor (Figure 2, item 202) and at least one memory (Figure 2, item 204) containing instructions that, when executed, cause the at least one processor to:
separate the data into one or more clusters (paragraph 0054: Here, “all records in a record corpus may be classified (separated) using the trained model”), each cluster based at least on a separate feature from one or more models (paragraph 0054: Here, text with classification value pairings indicates the common features within each class or cluster (paragraph 0061))
determine an accuracy associated with each respective feature of a subset dataset of the data of each cluster (paragraph 0056: Here, data is sent to the crowdsourcing because the score is outside the threshold. Therefore, the classification that are not identified as high confidence reads on the subset dataset where their threshold are outside)
detect, based at least in part on the accuracy, that the subset dataset of the data is outside of a threshold accuracy (paragraph 0056: Here, data is identified as being outside the confidence threshold and is sent to crowd sourcing)
in response to detecting that the accuracy being outside of the threshold accuracy, automatically forward the subset dataset to a crowd source service (paragraph 0056: Here, data is identified as being outside the confidence threshold and is sent to crowd sourcing)
receive verification of the subset dataset from the crowd source service (paragraph 0057: Here, a validation decision may be received from the crowd sourcing forum)
add the verification subset dataset to at least one model of the one or more models (paragraph 0060: Here, the crowd sourcing validated classification is added to the training dataset)
Garera does not specifically disclose in response to automatically detecting that the accuracy being outside of the threshold accuracy, automatically forward the subset dataset to a crowd source service and receiving the media data continuously.
However, Jeffery, which is analogous to the claimed invention because it is directed toward a classification system, discloses a system that receives a classification judgment about an input data instance from a classifier. (Col. 13 lines 37-41- the system automatically determines the certainty or accuracy of the classification judgement of the input data instance)
Based on Col. 13 lines 41-45 Jeffery automatically determines whether the judgment confidence value satisfies a confidence threshold.
If the judgement confidence value does not satisfies the confidence threshold, the system sends input data sample the to an oracle for verification. (See Col. 13 lines 46- 50 automatically the system determines if the confidence threshold is below the confidence threshold then the data instance is sent to the oracle (crowdsource))
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At step 715, the system automatically detects if the judgment confidence satisfies a confidence threshold.
The system moves to step 720 if the response is no meaning the judgement (accuracy of the classification) does not satisfies the confidence threshold (below or outside the threshold)
When the system detects the accuracy of the classification is outside threshold then the data instance is sent or forwarded to the oracle (crowdsource) for verification.
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Jeffery with Garera, with a reasonable expectation of success, as it would have allowed the system to save time by automatically determining if the accuracy of the classification is within the allowed threshold.
Garera in view of Jeffery does not disclose receiving media data on a continuous basis.
However, Acharya, which is analogous to the claimed invention because it is directed toward clustering crowd sourced data, discloses receiving media data on a continuous basis. (paragraph 0096 “… Continuous image stream with image data …”)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Acharya with Garera-Jeffery, with a reasonable expectation of success, as it would have allowed for processing a continuous image stream (paragraph 0096).
As per dependent claim 2, Garera, Jeffrey, and Acharya disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Garera discloses wherein the at least one processor further configured to generate a second model based on the received verified subset dataset; (Garera: Section 0066, lines 8-10 “augmenting 506 training data which is received from an analyst workstation with high confidence data” reads on the second model) and upon determining that an accuracy of the second model exceeds the first model, update the first model with the second model. (Garera: Section 0055, lines 2-9- thus when the classification with a confidence score above a specified threshold may be added to the training set thus updated)
As per dependent claim 5, Garera, Jeffery, and Acharya disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Garera discloses wherein the at least one processor is further configured to receive a labelled subset dataset from the crowd source service; (Garera: Section 0041 lines 6-8- “substitute classification” means a new class or label has been outputted by the crowdsource forum)
(Garera: also see Section 0052, lines 3-4 “add more descriptive data to the one or more records”)
add the labelled subset dataset to the first model to create a combined model dataset; (Garera: Section 0042, lines 3-5- thus the new classification designated as valid by the crowdsource forum are added/combined to the training data (model) to create a new model)
generate the second model based on the combined model dataset; (Garera: Section 0042, lines 6-8- thus the combined training dataset reads on the new model or training dataset) and determine the accuracy of the second model. (Garera: Section 0043, lines 8-9 “evaluation of the correctness of the validation decision”)
As per dependent claim 6, Garera, Jeffery, and Acharya disclose the limitations similar to those in claim 5, and the same rejection is incorporated herein. Garera discloses wherein the at least one processor is configured to generate the second model on an ongoing basis. (Garera: Section 0047, lines 6-9- thus “the analyst generate training data when appropriate” – this means the training data is generated as an ongoing basis)
As per dependent claim 8, Garera, Jeffery, and Acharya disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Garera discloses wherein the media data is comprised of multiple data types the multiple data types including audio, visual, and text data. (Garera: Section 0051, lines 3-5 product records of a product catalog means the product data is a text or image data)
As per dependent claim 9, Garera, Jeffery, and Acharya disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Garera discloses wherein the media data is configured to be separated into the one or more clusters based on an unsupervised machine learning technique. (Garera: Section 0036, lines 5-7- “machine learning algorithm including unsupervised learning algorithm”)
As per dependent claim 10, Garera, Jeffery, and Acharya disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Garera discloses wherein the at least one processor is further configured to automatically forward the subset dataset to the crowd source service based on a volume (Garera: Section 0056, lines 1-2 “Some or all of the Classification” reads on volume or part of the data) of the subset dataset being above the threshold accuracy. (Garera: Section 0047, lines 1-9- thus “an analyst module may select classification values or categories of classification values on the basis of a percentage of classification (threshold accuracy) … that were marked as invalid to generate a prompt transmitted or displayed to analysts (the analyst offers crowdsourcing services)”)
As per dependent claim 11, Garera, Jeffery, and Acharya disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Garera discloses wherein the volume of the subset dataset that initiates forwarding to the crowd source service (Garera: Section 0056- Crowdsource services) is based on a volume heuristics model that is configured to determine an amount of data predicted to successfully update the first model. (Garera: Section 0049, lines 1-5- if the classification value percentage is above the threshold then the crowdsource is initiated to generate a training data).
With respect to independent claim 12, the claim recites the limitations substantially similar to those in claim 1. The rejection of claim 1 is incorporated herein by reference.
Additionally, Garera discloses a non-transitory computer-readable medium comprising instructions, (Section 0027, lines 8-10- thus Processor which includes various types of computer readable media) when executed by a near real time data pipeline system, (Section 0034, lines 1-3 executable program).
With respect to dependent claim 13, the claim recites the limitations substantially similar to those in claim 2. The rejection of claim 2 is incorporated herein by reference.
With respect to independent claim 15, the claim recites the limitations substantially similar to those in claim 5. The rejection of claim 5 is incorporated herein by reference.
With respect to independent claim 16, the claim recites the limitations substantially similar to those in claim 1. The rejection of claim 1 is incorporated herein by reference.
With respect to dependent claims 17-20, the claim recites the limitations substantially similar to those in claims 2, 5-6, and 9, respectively. The rejection of claims 2, 5-6, and 9 are incorporated herein by reference.
Claims 3-4 and 14 remain rejected under 35 U.S.C. 103 as being unpatentable over Garera, Jeffery, and Acharya and further in view of Pallath et al. (US 2017/0011111, published 12 January 2017, hereafter Pallath).
As per dependent claim 3, Garera, Jeffery, and Acharya disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Garera discloses wherein determining the accuracy of the subset dataset (Garera: Section 0051, lines 1-2 training data…generated by the analysts) comprises determining a feature metric associated with the feature from an analysis of the first model; (Garera: Section 0051, lines 1-5- thus Classification value represents a common character among the product records)
after separating the media data into the one or more clusters, (Classified data) determining a measured metric (confidence score (measured metric)) associated with the media data in each cluster; (Garera: Section 0038, lines 1-4- thus classification data or records with a confidence score (measured metric) above a threshold)
Garera fails to specifically disclose defining a centroid based on the feature metric.
However, Pallath, which is analogous to the claimed invention because it is directed toward advanced analytics of datasets, discloses defining a centroid based on the feature metric (Pallath: Section 0065, lines 8-10 “ a centroid of a cluster can be determined by using k-means clustering”).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Pallath with Garera-Jeffery-Acharya, with a reasonable expectation of success, as it would have allowed for using a centroid as a feature metric to further assist in interpreting a cluster.
As per dependent claim 4, Garera, Jeffrey, Acharya, and Pallath disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Garera further discloses wherein the at least one processor is further configured to select the subset dataset (Garera: Section 0051, lines 1-2 training data…generated by the analysts) of each cluster based on the measured metric (Garera: confidence score (measured metric)) matching the feature metric (Garera: Characteristic value) associated with the centroid within a threshold. (Garera: paragraph 0038, lines 8-10- thus the confidence score is in between the first and the second threshold)
With respect to dependent claim 14, the claim recites the limitations substantially similar to those in claim 3. The rejection of claim 3 is incorporated herein by reference.
Claim 7 remains rejected under 35 U.S.C. 103 as being unpatentable over Garera, Jeffery, and Acharya and further in view of Senior et al. (US 20150269931, published 24 September 2015, hereafter Senior).
As per dependent claim 7, Garera, Jeffery, and Acharya disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Garera fails to specifically disclose wherein the at least one processor is configured to determine the feature from the first model based on at least one of an accent, gender, or environmental background noise in the media data.
However, Senior, which is analogous to the claimed invention because it is directed toward processing clusters, discloses a system wherein the at least one processor is configured to determine the feature from the first model based on at least one of an accent, gender, or environmental background noise in the media data. (paragraph 0043: demographic characteristic (e.g. gender or accent)).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Senior with Garera-Jeffery-Acharya, with a reasonable expectation of success, as it would have allowed for identifying demographic characteristics (Senior: paragraph 0043).
Response to Arguments
Applicant's arguments with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
The applicant’s initial argument is based upon the assertion that the claims fail to recite a mental process because the “ordered combination is not a process that can practically be performed in the human mind or with pencil and paper (page 7).” To support this assertion, the applicant argues that “a continuous data pipeline, model-based clustering, automatic threshold detection, automated routing to a crowd source service, receipt of verification from that service, and updating of one or more models… are not merely extra-solution activity (pages 7-8).”
However, the examiner does not allege that a continuous data pipeline, model-based clustering, automatic threshold detection, automated routing to a crowd source service, receipt of verification from that service, and updating of one or more models” are merely extra-solution activity. Instead, under Step 2A, Prong One, the examiner identifies separating the data into one or more clusters, each cluster based at least one a feature from one or more models; determining an accuracy associated with each respective feature of a subset dataset of the data of each cluster; and automatically detecting, based at least in part on the accuracy, that the subset dataset of the data is outside of a threshold accuracy as mental processes.
Additionally, under Step 2A, Prong 2 and Step 2B, the examiner identifies receiving a continuous pipeline of crowd sourced data; receiving verification of the subset dataset from the crowd source service; responsive to automatically detecting that the accuracy being outside of the threshold accuracy, automatically forward the subset to a crowd source service; and adding the verified subset dataset to at least one model of the one or more models.
For these reasons, this argument is not persuasive.
The applicant further argues that the present claims recite a technological improvement (page 8). To support this position, the applicant argues that the “claimed pipeline improves the operation of a machine-learning system by automatically identifying lower-accuracy cluster/subset data, obtaining verification for only the data requiring validation, and adding the verified subset dataset to at least one model (page 8).”
However, at best, the claimed combination amounts to an improvement to the abstract idea rather than to an improvement on the functioning of a computer or to any other technology. See MPEP 2106.05(a). Specifically, automatically detecting, based at least in part on the accuracy, that the subset dataset of the data is outside of a threshold accuracy is a mental process (see: Claim 1, Step 2A, Prong One analysis). Thus, even when considering the elements in combination, the claim as a whole does not integrate the recited exception into a practical application
For this reason, this argument is not persuasive.
The applicant further argues that the rejection “overlooks the claimed crowd source service, the threshold-based forwarding operation, and the model-update operation as part of the ordered combination (page 8).”
First, it is noted that the independent claims do not recite a “model-update operation.” Instead, the claim recites “add the verified subset dataset to at least one model of the one or more models (claim 1, line 14).” Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Additionally, “receive verification of the subset dataset from the crowd source service (claim 1, line 13)” and “automatically forward the subset dataset to a crowd source service (claim 1, line 12)” are not overlooked by the examiner. Instead, these limitations are considered under Step 2B both individually and in combination. As noted by the examiner, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
For this reason, this argument is not persuasive.
Applicant's arguments filed with respect to the rejection of claims under 35 USC 103 have been fully considered but they are not persuasive.
The applicant argues that the prior art fails to teach “at least one receiver for receiving a continuous pipeline of media data, wherein the media data includes at least crowd sourced data (page 10).”
It is noted that the claims of the present application do not recite “at least one receiver for receiving a continuous pipeline of media data, wherein the media data includes at least crowd sourced data.” Instead, the present application merely recites “receive a continuous pipeline of crowd sourced data (claim 1, line 4). Although the applicant argues that the “limitation is not a meaningful broadening away from the parent” claims, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
For this reason, this argument is not persuasive.
The applicant further argues that neither Garera, Acharya, nor Jeffery disclose at least one receiver for receiving a continuous pipeline of media data, wherein the media data includes at least crowd sourced data (pages 11-12).
However, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Further, the examiner acknowledges that the prior art of record fails to specifically disclose receiving a continuous pipeline of crowd sourced data. However, Acharya, which is analogous to the claimed invention because it is directed toward clustering crowd sourced data, discloses receiving media data on a continuous basis (paragraph 0096). This is in combination with Gerera’s teaching of crowd sourced data (paragraph 0056). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Acharya with Garera-Jeffery, with a reasonable expectation of success, as it would have allowed for processing a continuous image stream (paragraph 0096).
For these reasons, this argument is not persuasive.
The applicant further argues that the rejection “relies upon several unsupported equivalences: a classification category is treated as a cluster; a confidence score is treated as feature-specific accuracy; an individual low-confidence classification is treated as a subset dataset of each cluster; and adding a classification to training data is treated as adding the verified subset dataset to a model (pages 11-12).”
The applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
“During patent examination, the pending claims must be "given their broadest reasonable interpretation consistent with the specification." The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005) expressly recognized that the USPTO employs the "broadest reasonable interpretation" standard (MPEP 2111).”
The examiner has applied the broadest reasonable interpretation standard to the claims and this argument is not persuasive.
The applicant further argues that a person of ordinary skill would not “have modified Gerera’s product-record classification/crowdsourced-validation system with Acharya’s line-of-sight video-stream clustering system to arrive at the claimed continuous crowd-sourced-data training pipeline (page 12).”
However, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
In this instance, Acharya is being relied upon for disclosing discloses receiving media data on a continuous basis (paragraph 0096). As noted by the examiner, it would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Acharya with Garera-Jeffery, with a reasonable expectation of success, as it would have allowed for processing a continuous image stream (paragraph 0096). This would have allowed for processing a continuous image stream (Acharya: paragraph 0096) using Gerera’s method of clustering data, determining accuracy, detecting accuracy outside a threshold, forwarding data to a crowd source service, receiving verification, and adding data to the model.
For this reason, this argument is not persuasive.
The examiner notes the applicant’s remarks with respect to the rejection of claims under Nonstatutory Double Patenting. The applicant does not provide any arguments, but indicates that a terminal disclaimer will be filed to obviate the rejection (page 13).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Nielsen et al. (US 10516911): Discloses receiving streams of crowd sourced media content (column 1, line 64- column 2, line 23)
Saraf (US 10489284): Discloses human evaluation outputs from crowd sourcing computed from real-time streams (column 4, line 30- column 5, line 8)
Gupta et al. (US 2019/0251707): Discloses crowd-sourced training tasks corresponding to video streams (paragraph 0037 and 0072)
Hampton et al. (US 10201307): Discloses continuous providing the user with crowd sourced information (claim 1)
Santiago (US 2018/0357317): Discloses stream processing in-cloud leveraging crowd sourced data (paragraph 0037)
Crabtree et al. (US 2018/0276508): Discloses one or more streams from a plurality of sources including crowd sourcing campaigns (paragraph 0085)
Koren et al. (US 10073923): Discloses a machine learner using artificial intelligence to provide an evolving and continuously improving interaction with a user based on output of the crowd source analyzer (claim 22)
Smith et al. (US 2018/0192158): Discloses using crowd source analytics in a transport stream (paragraph 0020)
Nishi et al. (US 2018/0090016): Discloses continuously aggregating weather data into a stream for processing from crowd-sourced weather data (paragraph 0023)
Garner et al. (US 9621989): Discloses receiving a data stream comprising audio content and crowd sourced data from a data source over a communication link (column 7, lines 23-37)
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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