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 .
The filing date of the present invention is 04/22/2022.
This action is in response to amendment and/or remarks filed on 01/30/2026. In the current amendments, claims1, 3-4, 7-8, 14-15 and 21-23 have been amended and claims 18-20 has been cancelled. Claims 1-12 and 14-21 are currently pending and have been examined.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-12 and 14-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-17 and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 1
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“A method for processing data, the method comprising: ….(d) performing a first inference on the server by applying the sensor data to the inference model to generate a first inference result;”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]). The server limitation is recited at a high level of generality which merely uses a computer as a tool to perform the concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “(a) training an inference model; …server… (b) deploying the inference model to a server and a first edge device;”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. In addition, the claim limitation “(c) receiving sensor data from a second edge device at the server and at the first edge device… (e) sending the first inference result to the first edge device;… (f) performing a second ML inference on the sensor data on the first edge device in response to a wait time exceeding a predetermined delay threshold without the first edge device receiving the first ML inference result generated by the sever, wherein performing the second ML inference includes applying the sensor data to the trained ML inference model on the first edge device, and wherein performing the second ML inference minimizes further delays” as explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. See MPEP 2106.05(g). 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.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The only remaining limitation of the claim “(c) receiving sensor data from a second edge device at the server and at the first edge device… (e) sending the first inference result to the first edge device;” constitute storing and retrieving information in memory, which the courts have found to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). In addition, Ali teaches (f) performing a second ML inference on the sensor data on the first edge device in response to a wait time exceeding a predetermined delay threshold without the first edge device receiving the first ML inference result generated by the sever, wherein performing the second ML inference includes applying the sensor data to the trained ML inference model on the first edge device, and wherein performing the second ML inference minimizes further delays (“pg. 4 left col By overlapping the deep learning pipeline stages over edge, in-transit and cloud resources, the deep learning pipeline can be executed in parallel to improve the performance of the overall system. The numbers of nodes in each tier may vary depending on the commodity hardware availability and the application requirements. Total computational nodes on the network will be the sum of the edge, cloudlet and cloud resources. By using infrastructure in Fig.3, we can transform the low-value density of video data into high-value density data before feeding this data to the cloud resulting in improved response times, reduced bandwidth and storage requirements on the cloud. For object recognition, we define the value of the data as high if the video frame contains an object or low otherwise.”). 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.
Regarding claim 2
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
“wherein the sensor data is audio data.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “sensor”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 3
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
“wherein the first ML inference and the second inference comprises inferring a text translation based on the audio data.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 4
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the first inference and the second inference comprises inferring a stress level based on the sensor data.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “sensor… ML”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 5
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the sensor data is image data or video data.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “sensor”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 6
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“further comprising aborting the second inference if the …first inference result is received by the first edge …prior to the second inference being completed”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “device… ML”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. 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.
Regarding claim 7
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“…(b) performing a first inference on the first set of data to generate a first result using a trained inference model in response to the queue being shorter than a predetermined threshold; and (c) in response to the queue being longer than the predetermined threshold:…”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “server”…(ii) instructing the second device to perform a second inference on the first set of data to generate a second result.”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. In addition, the claim limitation “the method comprising:(a) receiving a first set of data from a first device in a queue for processing on a server…(i) sending the first set of data to a second device,… “performing a second ML inference on the first set of data on the second edge device using the trained ML inference model to generate a second ML inference result, wherein performing the second ML inference in response to the queue being longer than the predetermined threshold minimizes delays.” as explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. See MPEP 2106.05(g). 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.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The only remaining limitation of the claim “(d) receiving a first set of data from a first device in the first queue” constitute storing and retrieving information in memory, which the courts have found to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). 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. In addition, Ali teaches “performing a second ML inference on the first set of data on the second edge device using the trained ML inference model to generate a second ML inference result, wherein performing the second ML inference in response to the queue being longer than the predetermined threshold minimizes delays.” (“pg. 4 left col By overlapping the deep learning pipeline stages over edge, in-transit and cloud resources, the deep learning pipeline can be executed in parallel to improve the performance of the overall system. The numbers of nodes in each tier may vary depending on the commodity hardware availability and the application requirements. Total computational nodes on the network will be the sum of the edge, cloudlet and cloud resources. By using infrastructure in Fig.3, we can transform the low-value density of video data into high-value density data before feeding this data to the cloud resulting in improved response times, reduced bandwidth and storage requirements on the cloud. For object recognition, we define the value of the data as high if the video frame contains an object or low otherwise.”). 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.
Regarding claim 8
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “wherein (c) further comprises deploying the trained ML inference model to the second device”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 9
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “wherein the second edge device is a mobile phone.”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. 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.
Regarding claim 10
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the first set of data is audio data, image data, or video data.”
This limitation just places restrictions on the type of model include and doesn’t change the fact that the underlying data manipulation is mental.
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 11
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the first inference and the second ML inference comprise inferring a stress level based on the first set of data.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 12
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “wherein the first ML inference and the second ML inference comprises inferring a text translation based on the first set of data.”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 13
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “further comprising caching the first set of data on the second edge device.”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 14
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“A method for processing data, the method comprising: …(e) predicting a wait time for the first queue; …and (g) in response to the predicted wait time for the first queue being greater than a predetermined threshold: … and (iii) directing at least a portion of subsequent sets of data to the second queue in lieu of the first queue.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “(a) training an inference model;(b) deploying the inference model to a first computer; (c) creating a first queue on the first computer…(f) deploying at least a version of the trained ML inference model to a second edge device”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. In addition, the claim limitation “(d) receiving a first set of data from a first device in the first queue… and sending results to a second device… (ii) creating a second queue on the second device;” as explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. See MPEP 2106.05(g). 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.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The only remaining limitation of the claim “(d) receiving a first set of data from a first device in the first queue… (ii) creating a second queue on the second device;” constitute storing and retrieving information in memory, which the courts have found to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). 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.
Regarding claim 15
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “further comprising:(h) generating a first stream of ML inference results on the first computer; (i) forwarding the first stream of ML inference results to the second edge device;(j) generating a second stream of ML inference results on the second edge device; and (k) ordering the first stream of ML inference results and the second stream of ML inference results on the second edge device”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 16
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the first set of data includes audio data, and wherein the trained ML inference model infers a text translation based on the audio data.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 17
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the data is image data or video data.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 21
Step 1: The claim recites a system; therefore, it falls into the statutory category of machine.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“…perform a first inference on the first set of data to generate a first result using a …in response to the queue being shorter than a predetermined threshold; and in response to the queue being longer than the predetermined threshold: …”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “A system having at least one processor… trained inference model” as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. In addition, the claim limitation “A system having at least one processor, the system configured to: receive a first set of data from a first edge device in a queue on the server for processing on a server… send the first set of data to a second device, instruct the second device to perform a second inference on the first set of data to generate a second result” as explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. See MPEP 2106.05(g). 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.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The only remaining limitation of the claim “A system having at least one processor, the system configured to: receive a first set of data from a first device in a queue for processing on a server… send the first set of data to a second device, perform a second inference on the first set of data to generate a second result” constitute storing and retrieving information in memory, which the courts have found to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). 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.
Regarding claim 22
Step 1: The claim recites a system; therefore, it falls into the statutory category of machine.
Step 2A Prong 1:
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “configured to deploy the trained ML inference model to the second device in response to the queue being longer than the predetermined threshold.” as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 23
Step 1: The claim recites a system; therefore, it falls into the statutory category of machine.
Step 2A Prong 1:
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “wherein at least one of: the second edge device is or a mobile phone; and the first set of data is audio data, image data, or video data” as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 5-6, 7-8, 12-15 and 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 11301762 B1) in view of Ali et al. (“Edge Enhanced Deep Learning System for Large-scale Video Stream Analytics”).
Regarding claim 1 (Amended)
Chen teaches a method for processing data, the method comprising:(a) training an inference model; (col 3 lines 9-17 “Further, some embodiments can provide a flywheel for ML on connected devices, allowing users to deploy models across potentially millions of devices and continually improve them. Data can be collected in a secure manner from devices and used to train new models, which can be re-deployed to devices. This in turn generates new data for additional cycles of re-training and re-deployment, where each cycle can increase availability of the system and improve device experience” also see col 10 lines 63-67 “The operating environment includes end user devices 602 (e.g., client device 120 and/or edge device(s) 122), a model training system 620, a model hosting system 640, a training data store 660, a training metrics data store 665, a container data store 670, a training model data store 675, and a model prediction data store 680”)
(b) deploying the inference model to a server and a first edge device; (col 4 lines 56-65 “For example, an edge device 122A may include firmware or other pre-installed software including the UIF client module 124. Alternatively, a user may cause the UIF client module 124 to be deployed to the one or more edge devices 122A-122N. For example, the user 118 may use client device 120 to “log in” to an edge device 122A (e.g., via SSH, telnet, web application, etc.) and issue commands to the device 122A to install software (including the UIF client module 124), etc., at circle (A1). As part of installing this software, the UIF client module 124 may contact an edge device management service 110 and provide (or acquire, as assigned by the UIF server module 112) an identifier of itself, an identifier of the user 118 (or user/customer account), etc.”)
(c) receiving sensor data from a second edge device at the server and at the first edge device; (col 7 lines 56-67 “The one or more edge devices 122A-122N[correspond to plurality of devices first, second third etc] may then operate as intended, e.g., by an application 127 capturing/creating input data via one or more sensors 128 (e.g., optical sensors, audio sensors, temperature sensors, humidity sensors, air pressure sensors, gas sensors, moisture sensors, water flow sensors, weight sensors, motion sensors, global positioning system (GPS) sensors, rotation/acceleration sensors, radio sensors, biological sensors (e.g., pulse sensors), fingerprint sensors, and the like. This input data, at circle (5), is provided to the inference engine 132 which at circle (6) can perform inference using the optimized model 130 and optionally logic of an inference library 134.”)
(d) performing a first inference on the server by applying the sensor data to the inference model to generate a first inference result; (“The ML model evaluator 628 can obtain the model data for a machine learning model being trained and evaluation data from the training data store 660. The evaluation data is separate from the data used to train a machine learning model and includes both input data and expected outputs (e.g., known results), and thus the ML model evaluator 628 can define a machine learning model using the model data and execute the machine learning model by providing the input data as inputs to the machine learning model.” see col 3 lines 43-54 “FIG. 1 is a diagram illustrating an exemplary environment including a unified inference framework (“UIF”) server module 112 according to some embodiments. The UIF server module 112, in some embodiments, is a portion of software allowing users 118 to deploy and manage high performance machine learning models 130 running on connected devices 122 in production. Users 118 (e.g., individuals, organizations, even OEMs) can import to—or train machine learning models in—a provider network 100 (“the cloud”), and reliably deploy these models to large numbers of devices 122 at the edge.”)
(e) sending the first inference result to the first edge device; (col 10 lines 47-58 “At block 520, the operations 500 also include, causing the optimized second one or more files to be provided to an inference engine of each of the one or more electronic devices. Block 520 may be performed by the UIF server module 112 or edge device management service 110, where the optimized second one or more files are transmitted directly to the one or more electronic devices or to a client device (to be installed upon the one or more electronic devices by a user or application), or where the optimized second one or more files are placed in a storage location (e.g., of a storage service) where the one or more electronic devices will obtain these files.”)
Chen does not teach (f) performing a second ML inference on the sensor data on the first edge device in response to a wait time exceeding a predetermined delay threshold without the first edge device receiving the first ML inference result generated by the sever, wherein performing the second ML inference includes applying the sensor data to the trained ML inference model on the first edge device, and wherein performing the second ML inference minimizes further delays.
Ali teaches (f) performing a second ML inference on the sensor data on the first edge device in response to a wait time exceeding a predetermined delay threshold without the first edge device receiving the first ML inference result generated by the sever, wherein performing the second ML inference includes applying the sensor data to the trained ML inference model on the first edge device, and wherein performing the second ML inference minimizes further delays (“pg. 4 left col By overlapping the deep learning pipeline stages over edge, in-transit and cloud resources, the deep learning pipeline can be executed in parallel to improve the performance of the overall system. The numbers of nodes in each tier may vary depending on the commodity hardware availability and the application requirements. Total computational nodes on the network will be the sum of the edge, cloudlet and cloud resources. By using infrastructure in Fig.3, we can transform the low-value density of video data into high-value density data before feeding this data to the cloud resulting in improved response times, reduced bandwidth and storage requirements on the cloud. For object recognition, we define the value of the data as high if the video frame contains an object or low otherwise.”)
Chen and Lin are analogous art because they are both directed to Machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined high performance machine learning inference framework for edge devices disclosed by Chen to include federated learning of Lin in order to incorporate a “FedDelAvg(federated delayed averaging) is developed, which generalizes the standard federated averaging algorithm to incorporate a weighting between the current local model and the delayed global model received at each device during the synchronization step” as disclosed by Lin (abstract “A technique called FedDelAvg (federated delayed averaging) is developed, which generalizes the standard federated averaging algorithm to incorporate a weighting between the current local model and the delayed global model received at each device during the synchronization step. Through theoretical analysis, an upper bound is derived on the global model loss achieved by FedDelAvg, which reveals a strong dependency of learning performance on the values of the weighting and learning rate. Experimental results on a popular ML task indicate significant improvements in terms of convergence speed when optimizing the weighting scheme to account for delays.”).
Regarding claim 2 (Original)
Chen in view of Ali teaches the method of claim 1.
Chen further teaches wherein the sensor data is audio data. (Para [0044] “The devices 122 may use different sensors such as cameras, light detection and ranging (LIDAR), radar, ultra sonic, or other sensors. Different types of data may be collected by a device 122, for example, image data, weather data”)
Regarding claim 5 (Original)
Chen in view of Ali teaches the method of claim 1.
Chen further teaches wherein the sensor data is image data or video data. (Para [0044] “The devices 122 may use different sensors such as cameras, light detection and ranging (LIDAR), radar, ultra sonic, or other sensors. Different types of data may be collected by a device 122, for example, image data, weather data”)
Regarding claim 6 (Currently Amended)
Chen in view of Ali teaches the method of claim 1.
Chen further teaches the method further comprising aborting the second inference if the first inference result is received by the first edge device prior to the second inference being completed. (Col 10 lines 8-12 “At block 510, the operations 500 also include, translating a first one or more files of the ML model in the first format into a second one or more files of a second format. Block 510 may be performed, for example, by a model optimizer 114A of a UIF server module 112 of the other figures.”)
Regarding claim 14 (Currently Amended)
Claim 14 recites analogous limitations to claim 1 and therefore is rejected on the same ground as claim 1.
Regarding claim 17 (Original)
Chen in view of Lin teaches the method of claim 14.
Chen further teaches wherein the data is image data or video data. (Para [0044] “The devices 122 may use different sensors such as cameras, light detection and ranging (LIDAR), radar, ultra sonic, or other sensors. Different types of data may be collected by a device 122, for example, image data, weather data”)
Claim(s) 3-4 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 11301762 B1) in view of Ali et al. and further in view of Bose et al. (“A Hands Free Browser Using EEG and Voice Inputs”).
Regarding claim 3 (Currently Amended)
Chen in view of Ali teaches the method of claim 2.
Chen in view of Ali does not teach wherein the first ML inference and the second inference comprises inferring a text translation based on the audio data.
Bose teaches wherein the first ML inference and the second inference comprises inferring a text translation based on the audio data. (Pg. 3 section B “The captured audio stream is then sent to the server for Speech to Text translation. We used the Google Speech API [12-14] available with Google's Chrome browser and sent the voice to the Google server for conversion to text.”)
Chen, Ali and Bose are analogous art because they are all directed to neural network.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined high performance machine learning inference framework for edge devices disclosed by Chen in view of Ali to include hands free mobile browser using EEG sensor of Bose in order to provide “maintaining of the concentration level higher than a threshold enables the user to cycle through a list of items” as disclosed by Bose (abstract “In this paper we describe the design of a hands free browser. It uses EEG inputs using a NeuroSky EEG sensor, as well as voice inputs. Various functions in the browser including add to favorites, scroll up or down, next and previous tab, open an URL that is part of the favorites, are mapped to varying attention and blink levels recognized by the EEG sensor. Maintaining of the concentration level higher than a threshold enables the user to cycle through a list of items, which could be menu items or history items or most visited sites in the browser. A deliberate double blink enables the user to open the currently selected URL or menu item”).
Regarding claim 4 (Currently Amended)
Chen in view of Ali teaches the method of claim 1.
Chen in view of Ali does not teach wherein the first inference and the second inference comprises inferring a stress level based on the sensor data.
Bose teaches wherein the first inference and the second inference comprises inferring a stress level based on the sensor data. (FIG. 2. Flowchart illustrating various preprocessing steps for the EEG input on the web browser, where the recorded EEG output is compared to a pre-existing standard in order to determine the user's emotion or stress level.” See abstract “In this paper we describe the design of a hands free browser. It uses EEG inputs using a NeuroSky EEG sensor, as well as voice inputs.”)
Chen, Ali and Bose are analogous art because they are all directed to neural network.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined high performance machine learning inference framework for edge devices disclosed by Chen in view of Ali to include hands free mobile browser using EEG sensor of Bose in order to provide “maintaining of the concentration level higher than a threshold enables the user to cycle through a list of items” as disclosed by Bose (abstract “In this paper we describe the design of a hands free browser. It uses EEG inputs using a NeuroSky EEG sensor, as well as voice inputs. Various functions in the browser including add to favorites, scroll up or down, next and previous tab, open an URL that is part of the favorites, are mapped to varying attention and blink levels recognized by the EEG sensor. Maintaining of the concentration level higher than a threshold enables the user to cycle through a list of items, which could be menu items or history items or most visited sites in the browser. A deliberate double blink enables the user to open the currently selected URL or menu item”).
Regarding claim 15 (Original)
Chen in view of Ali teaches the method of claim 14.
Chen further teaches further comprising:(h) generating a first stream of ML inference results on the first computer; (i) forwarding the first stream of inference results to the second device; (col 8 lines 9-16 “As another example, the input data or inferences could be sent to a machine learning service, sent to a data monitoring/logging service, stored in a database, sent to a serverless code execution service to be processed, etc., allowing users to take “local” inference results generated by edge devices and integrate these results into an overall application 127 in nearly any manner desired by the users.”)
(j) generating a second stream of inference results on the second device; (col 21 lines 17-42 “The second ML scoring container 650 further includes a model data file stored therein, which is read by the executable instructions in the second code 656 to determine values for the characteristics defining the machine learning model. Execution of the second code 656 results in a second output. The virtual machine instance 642 that initialized the second ML scoring container 650 can then transmit the second output to the model prediction data store 680 and/or the user device 602 via the frontend 649 (e.g., if no more trained machine learning models are needed to generate an output) or transmit the second output to a third ML scoring container 650 initialized in the same or different virtual machine instance 642 (e.g., if outputs from one or more additional trained machine learning models are needed), and the above-referenced process can be repeated with respect to the third ML scoring container 650.”)
Chen in view of Ali does not teach and (k) ordering the first stream of inference results and the second stream of inference results on the second device.
Lin teaches and (k) ordering the first stream of inference results and the second stream of inference results on the second device. (Section A “The federated learning (FL) system architecture consists of a single edge server and N edge devices indexed by i = 1, 2, ..., N, as shown in Fig.1. The edge devices collect data and perform local updates to optimize a loss function F(·) corresponding to a machine learning task (described next). The edge server (the cloud) plays the role of an aggregator, collecting the locally trained parameters wi and the evaluations of the local loss functions Fi(wi) from the edge devices to perform a global update. Local updates are taken to be gradient descent steps on the local loss functions Fi(w), while global updates are constituted of aggregation followed by synchronization.”)
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 11301762 B1) in view of Ali et al. and further in view of Bose et al. (“A Hands Free Browser Using EEG and Voice Inputs”).
Regarding claim 16 (Original)
Chen in view of Ali teaches the method of claim 14.
Chen further teaches wherein the first set of data includes audio data, (col 7 lines 56-60 “The one or more edge devices 122A-122N may then operate as intended, e.g., by an application 127 capturing/creating input data via one or more sensors 128 (e.g., optical sensors, audio sensors, temperature sensors,”)
Chen in view of Ali does not teach and wherein the inference model infers a text translation based on the audio data.
Bose teaches and wherein the inference model infers a text translation based on the audio data. (Pg. 3 section B “The captured audio stream is then sent to the server for Speech to Text translation. We used the Google Speech API [12-14] available with Google's Chrome browser and sent the voice to the Google server for conversion to text.”)
Chen, Lin and Bose are analogous art because they are all directed to neural network.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined high performance machine learning inference framework for edge devices disclosed by Chen in view of Lin to include hands free mobile browser using EEG sensor of Bose in order to provide “maintaining of the concentration level higher than a threshold enables the user to cycle through a list of items” as disclosed by Bose (abstract “In this paper we describe the design of a hands free browser. It uses EEG inputs using a NeuroSky EEG sensor, as well as voice inputs. Various functions in the browser including add to favorites, scroll up or down, next and previous tab, open an URL that is part of the favorites, are mapped to varying attention and blink levels recognized by the EEG sensor. Maintaining of the concentration level higher than a threshold enables the user to cycle through a list of items, which could be menu items or history items or most visited sites in the browser. A deliberate double blink enables the user to open the currently selected URL or menu item”).
Claim(s) 7-10, 13, 21 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Capota et al. (US 2020/0410288 A1) in view of Lin et al. (“Low Latency Deep Learning Inference Model for Distributed Intelligent IoT Edge Clusters”).
Regarding claim 7 (Original)
Capota teaches a method for processing data, the method comprising: (a) receiving a first set of data from a first device in a queue for processing on a server; (para [0052] “The devices 122 are configured to communicate with the campaign server. The devices 122 receive at least instructions, updates, the model, and model parameters from the campaign server. The model may be either prepackaged and available on device or dynamically downloaded as a campaign starts and device pools are assigned roles. The devices 122 are configured to initialize”)
(b) performing a first inference on the first set of data to generate a first result using a trained inference model in response to the queue being shorter than a predetermined threshold; (para [0056] “The parameter server 137 may also be configured to regulate the frequency / number of transmissions from the devices 122 by setting a threshold number of data points for the devices 122 to process prior to sending an update. The threshold may be set at the start of the process and / or may updated as the training process proceeds. The parameter server 137 communicates with each device 122 of the plurality of devices 122 that are assigned to the parameter server 137. The parameter servers 137 may be configured to aggregate parameters from”)
Capota does not teach and (c) in response to the queue being longer than the predetermined threshold: (i) sending the first set of data to a second device, (ii) instructing the second device to perform a second inference on the first set of data to generate a second result.
Lin teaches and (c) in response to the queue being longer than the predetermined threshold: (i) sending the first set of data to a second device, (ii) instructing the second device to perform a second inference on the first set of data to generate a second result. (Section C “In FedDelAvg, i.e., Federated Delayed Averaging, the effect of communication delay between edge and cloud on learning performance is incorporated into the design of the FL system. We divide the learning process into discrete time intervals t ∈ {1, 2, ..., T}, where the duration between two consecutive aggregations is denoted as τ . The communication delay between the time when edge devices send their updates to the cloud and the resulting synchronization is denoted ∆, where τ ≥ ∆ ≥ 0. In Fig.1, we assume a symmetric delay of τ /2 upstream and downstream.”)
Capota and Lin are analogous art because they are both directed to Machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined high performance machine learning inference framework for edge devices disclosed by Chen to include federated learning of Lin in order to incorporate a “FedDelAvg(federated delayed averaging) is developed, which generalizes the standard federated averaging algorithm to incorporate a weighting between the current local model and the delayed global model received at each device during the synchronization step” as disclosed by Lin (abstract “A technique called FedDelAvg (federated delayed averaging) is developed, which generalizes the standard federated averaging algorithm to incorporate a weighting between the current local model and the delayed global model received at each device during the synchronization step. Through theoretical analysis, an upper bound is derived on the global model loss achieved by FedDelAvg, which reveals a strong dependency of learning performance on the values of the weighting and learning rate. Experimental results on a popular ML task indicate significant improvements in terms of convergence speed when optimizing the weighting scheme to account for delays.”).
Regarding claim 8 (Original)
Capota in view of Lin teaches the method of claim 7.
Lin further teaches wherein (c) further comprises deploying the trained inference model to the second device. (Section C “In FedDelAvg, i.e., Federated Delayed Averaging, the effect of communication delay between edge and cloud on learning performance is incorporated into the design of the FL system. We divide the learning process into discrete time intervals t ∈ {1, 2, ..., T}, where the duration between two consecutive aggregations is denoted as τ . The communication delay between the time when edge devices send their updates to the cloud and the resulting synchronization is denoted ∆, where τ ≥ ∆ ≥ 0. In Fig.1, we assume a symmetric delay of τ /2 upstream and downstream.”)
Regarding claim 9 (Original)
Capota in view of Lin teaches the method of claim 7.
Capota further teaches wherein the second device is an edge device or a mobile phone. (Para [0042] “The system includes devices 122 (also referred to as edge devices or worker devices 122). The devices may include probe devices, probe sensors, or other devices 122 such as personal navigation devices 122, location aware devices, smart phones mounted”)
Regarding claim 10 (Original)
Capota in view of Lin teaches the method of claim 7.
Capota further teaches wherein the first set of data is audio data, image data, or video data. (Para [0028] “The annotations / labels may be provided by a user or inferred by a user action (e.g. stopping at a stop light). Annotations / labels may also be derived from other sensor data (e.g. LIDAR sensor data used to label image data). The images are input into a large centralized neural network that is trained until the neural network reliably recognizes the relevant elements of the images and is able to accurately classify the objects. A”)
Regarding claim 13 (Original)
Capota in view of Lin teaches the method of claim 7.
Capota further teaches the method further comprising caching the first set of data on the second device. (Para [0143] “The term “computer-readable medium” includes a single medium or multiple media, such as a centralized or distributed database”)
Regarding claim 21 (New)
Claim 21 recites analogous limitations to independent claim 7 and therefore is rejected on the same ground as independent claim 7.
Regarding claim 22 (New)
Capota in view of Lin teaches the system of claim 7.
Lin further teaches the system further configured to deploy the trained inference model to the second device in response to the queue being longer than the predetermined threshold. (Section C “In FedDelAvg, i.e., Federated Delayed Averaging, the effect of communication delay between edge and cloud on learning performance is incorporated into the design of the FL system. We divide the learning process into discrete time intervals t ∈ {1, 2, ..., T}, where the duration between two consecutive aggregations is denoted as τ . The communication delay between the time when edge devices send their updates to the cloud and the resulting synchronization is denoted ∆, where τ ≥ ∆ ≥ 0. In Fig.1, we assume a symmetric delay of τ /2 upstream and downstream.”)
Regarding claim 23 (New)
Capota in view of Lin teaches the method of claim 21.
Capota further teaches wherein at least one of: the second device is an edge device or a mobile phone; (Para [0042] “The system includes devices 122 (also referred to as edge devices or worker devices 122). The devices may include probe devices, probe sensors, or other devices 122 such as personal navigation devices 122, location aware devices, smart phones mounted”)
and the first set of data is audio data, image data, or video data. (Para [0028] “The annotations / labels may be provided by a user or inferred by a user action (e.g. stopping at a stop light). Annotations / labels may also be derived from other sensor data (e.g. LIDAR sensor data used to label image data). The images are input into a large centralized neural network that is trained until the neural network reliably recognizes the relevant elements of the images and is able to accurately classify the objects.”)
Claim(s) 11 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Capota et al. in view of Lin et al. and further in view of Bose et al. (“A Hands Free Browser Using EEG and Voice Inputs”).
Regarding claim 11 (Original)
Capota in view of Lin teaches the method of claim 10.
Capota in view of Lin does not teach wherein the first inference and the second inference comprise inferring a stress level based on the first set of data.
Bose teaches wherein the first inference and the second inference comprises inferring a stress level based on the sensor data. (FIG. 2. Flowchart illustrating various preprocessing steps for the EEG input on the web browser, where the recorded EEG output is compared to a pre-existing standard in order to determine the user's emotion or stress level.” See abstract “In this paper we describe the design of a hands free browser. It uses EEG inputs using a NeuroSky EEG sensor, as well as voice inputs.”)
Chen, Lin and Bose are analogous art because they are all directed to neural network.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined high performance machine learning inference framework for edge devices disclosed by Chen in view of Lin to include hands free mobile browser using EEG sensor of Bose in order to provide “maintaining of the concentration level higher than a threshold enables the user to cycle through a list of items” as disclosed by Bose (abstract “In this paper we describe the design of a hands free browser. It uses EEG inputs using a NeuroSky EEG sensor, as well as voice inputs. Various functions in the browser including add to favorites, scroll up or down, next and previous tab, open an URL that is part of the favorites, are mapped to varying attention and blink levels recognized by the EEG sensor. Maintaining of the concentration level higher than a threshold enables the user to cycle through a list of items, which could be menu items or history items or most visited sites in the browser. A deliberate double blink enables the user to open the currently selected URL or menu item”).
Regarding claim 12 (Original)
Capota in view of Lin teaches the method of claim 7.
Capota in view of Lin does not teach wherein the first inference and the second inference comprises inferring a text translation based on the first set of data.
Bose teaches and wherein the inference model infers a text translation based on the audio data. (Pg. 3 section B “The captured audio stream is then sent to the server for Speech to Text translation. We used the Google Speech API [12-14] available with Google's Chrome browser and sent the voice to the Google server for conversion to text.”)
Chen, Lin and Bose are analogous art because they are all directed to neural network.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined high performance machine learning inference framework for edge devices disclosed by Chen in view of Lin to include hands free mobile browser using EEG sensor of Bose in order to provide “maintaining of the concentration level higher than a threshold enables the user to cycle through a list of items” as disclosed by Bose (abstract “In this paper we describe the design of a hands free browser. It uses EEG inputs using a NeuroSky EEG sensor, as well as voice inputs. Various functions in the browser including add to favorites, scroll up or down, next and previous tab, open an URL that is part of the favorites, are mapped to varying attention and blink levels recognized by the EEG sensor. Maintaining of the concentration level higher than a threshold enables the user to cycle through a list of items, which could be menu items or history items or most visited sites in the browser. A deliberate double blink enables the user to open the currently selected URL or menu item”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/VAN C MANG/Primary Examiner, Art Unit 2126
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