DETAILED ACTION
This action is in response to the application filed 05/26/2026. Claims 1-7 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1 and 2 are objected to because of the following informalities:
Regarding claim 1, the Examiner respectfully notes that claim 1 is a method claim and the limitation of “executing relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value” is a contingent limitation and therefore under the broadest reasonable interpretation these limitation may not be performed (“The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” MPEP 2111.04(II)). Accordingly the Examiner recommends the Applicant positively recite determining an offload rate is increased to avoid a contingent interpretation of these limitations.
Regarding claim 2, the Examiner respectfully notes that claim 2 is a method claim that depends on the contingent limitation of claim 1. The limitation of “wherein the relearning of at least one of the first model or the second model is executed…” is a contingent limitation and therefore under the broadest reasonable interpretation these limitation may not be performed (“The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” MPEP 2111.04(II)). Accordingly the Examiner recommends the Applicant positively recite an offload rate is increased to avoid a contingent interpretation of these limitations.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “data having a larger contribution, compared to other data” in claim 2 is a relative term which renders the claim indefinite. The term “larger contribution” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear as to what a contribution to the variation in load or the decrease in inference accuracy entails and by that matter, it is thus unclear as to what a larger contribution comprises. For purposes of examination, Examiner has interpreted “data having a larger contribution, compared to other data” to be data that was used for training and resulted in the variation in load or the decrease in inference accuracy.
Claim 2 recites the limitation "the decrease in inference accuracy" in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. For purposes of examination, Examiner has interpreted this decrease in inference accuracy to be the first instance of a decrease in inference accuracy.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
determining whether or not an offload rate is increased from a set value in at least one of the edge device or the server device; (This limitation is a mental process as it encompasses a human mentally determining whether or not a an offload rate is increased.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
A processing method executed by a processing system that performs first inference in an edge device and performs second inference in a server device, the processing method comprising: (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
executing relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
disposing a model for which the relearning is performed in the edge device or the server device (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A processing method executed by a processing system that performs first inference in an edge device and performs second inference in a server device, the processing method comprising uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
executing relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
disposing a model for which the relearning is performed in the edge device or the server device is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites the same abstract ideas as claim 1. Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of
the relearning of at least one of the first model or the second model is executed by using data having a larger contribution, compared to other data, to a variation in load or the decrease in inference accuracy in a target data group. (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the relearning of at least one of the first model or the second model is executed by using data having a larger contribution, compared to other data, to a variation in load or the decrease in inference accuracy in a target data group uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites
target data on which the second inference is executed and an inference result in the second inference of the target data in a target data group are set as learning data, (This limitation is a mental process as it encompasses a human mentally setting learning data.)
Therefore, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites additional elements of
the relearning of the first model is executed (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 3 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the relearning of the first model is executed uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
target data on which the second inference is executed and a corrected inference result obtained by correcting an inference result in the second inference of the target data in a target data group are set as learning data, (This limitation is a mental process as it encompasses a human mentally setting learning data.)
Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional elements of
the relearning of the second model is executed (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 4 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the relearning of the second model is executed uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
Claim 5 recites a system and is thus an apparatus, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
determine whether or not an offload rate is increased from a set value in at least one of the edge device or the server device; (This limitation is a mental process as it encompasses a human mentally determining whether or not an offload rate is increased.)
Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of
A processing system that performs first inference in an edge device and performs second inference in a server device, the processing system comprising: processing circuitry configured to: (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
execute relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
dispose a model for which the relearning is performed in the edge device or the server device (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A processing method executed by a processing system that performs first inference in an edge device and performs second inference in a server device, the processing method comprising uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
execute relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
dispose a model for which the relearning is performed in the edge device or the server device is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 1:
Claim 6 recites a non-transitory computer-readable recording medium and is thus an article of manufacture, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
determining whether or not an offload rate is increased from a set value in at least one of the edge device or the server device; (This limitation is a mental process as it encompasses a human mentally determining whether or not an offload rate is increased.)
Therefore, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 further recites additional elements of
A non-transitory computer-readable recording medium storing computer executable instructions which, when executed by a computer, cause the computer to execute a process of first inference in an edge device and performs second inference in a server device, the process comprising: (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
executing relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
disposing a model for which the relearning is performed in the edge device or the server device (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 6 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A non-transitory computer-readable recording medium storing computer executable instructions which, when executed by a computer, cause the computer to execute a process of first inference in an edge device and performs second inference in a server device, the process comprising uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
executing relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
disposing a model for which the relearning is performed in the edge device or the server device is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 6 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites
correcting an inference result (This limitation is a mental process as it encompasses a human mentally correcting a result.)
determining, with respect to the first model, whether or not a tendency of a target data group is changed based on the offload rate (This limitation is a mental process as it encompasses a human mentally determining whether or not a tendency is changed based on offload rate.)
and determining, with respect to the second model, whether or not the tendency of the target data group is changed based on a correction rate (This limitation is a mental process as it encompasses a human mentally determining whether or not a tendency is changed based on correction rate.)
Therefore, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 further recites additional elements of
by the second model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because
by the second model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 7 is subject-matter ineligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2019/0079898 A1) (hereafter referred to as Xiong) in view of Dods et al. (US 10,862,805 B1).
Regarding claim 1, Xiong teaches
A processing method executed by a processing system that performs first inference in an edge device and performs second inference in a server device, the processing method comprising (Xiong, page 13, paragraph 0034, “In accordance with one aspect of the configuration disclosed in FIG. 5, edge device 3 and/or fog node 2 may run inferencing locally, thus distributing computation to the lower level. By running inferencing locally, network bandwidth may be conserved and latency of the system may be reduced. Alternatively, edge device 3 and/or fog node 2 may request that cloud server 4 provide an inference if edge device 3 and/or fog node 2 is not confidence in the local inference or otherwise questions the accuracy of the local inference.” Examiner notes that the first inference is the inference running locally and the second inference is the cloud server providing an inference.):
executing relearning of at least one of a first model that performs the first inference or a second model that performs the second inference (Xiong, page 14, paragraph 0041, “At decision 35 the lower level devices may consider whether this suggested media content is acceptable by monitoring data distribution, monitoring the confidence level of inferences, and/or testing with unused historical data. If deemed acceptable, at step 36 the suggested media content may be shared with the user using the user device. After sharing the suggested media content with the user at step 36, or if the suggested media content is determined to not be acceptable at decision 35, the lower level devices may collect any useful data regarding the correct action taken, or the unacceptable suggested media content, and send this data to the cloud. At step 38, the cloud service may retrain the model based on the new data received and the process may start over at step 33.” Examiner notes that the lower level devices are the edge device and the cloud is the server device. Examiner further notes that retraining the model is relearning the first model.).
disposing a model for which the relearning is performed in the edge device or the server device (Xiong, page 14, paragraph 0041, “At decision 35 the lower level devices may consider whether this suggested media content is acceptable by monitoring data distribution, monitoring the confidence level of inferences, and/or testing with unused historical data. If deemed acceptable, at step 36 the suggested media content may be shared with the user using the user device. After sharing the suggested media content with the user at step 36, or if the suggested media content is determined to not be acceptable at decision 35, the lower level devices may collect any useful data regarding the correct action taken, or the unacceptable suggested media content, and send this data to the cloud. At step 38, the cloud service may retrain the model based on the new data received and the process may start over at step 33.” Examiner notes that the lower level devices are the edge device and the cloud is the server device. Examiner further notes that disposing a model for which the relearning is performed in the server device is the cloud service retraining the model.).
Xiong does not explicitly disclose an offload rate, but Dods does disclose
determining whether or not an offload rate is increased from a set value in at least one of the edge device or the server device (Dods, page 17, column 9, lines 26-38, “In this case, if a particular memory utilization rate of a line card satisfies a threshold memory utilization rate (e.g., exceeds 95%, exceeds 97%, drops below 5%, drops below 3%, etc.), then the network device may, as shown by reference number 165, perform an action associated with load balancing the traffic flow of the session or may perform an action associated with improving accuracy of a technique or model used to determine whether to offload the traffic flow. For example, the network device may modify a threshold overall value used to analyze the set of offloading indicators, retrain the data model, route packets to load balance the traffic flow, and/or the like, as each described.” Examiner notes that the offload rate is the utilization rate, the set value is 95%, and the server device is the network device.)
executing relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value (Dods, page 17, column 9, lines 26-38, “In this case, if a particular memory utilization rate of a line card satisfies a threshold memory utilization rate (e.g., exceeds 95%, exceeds 97%, drops below 5%, drops below 3%, etc.), then the network device may, as shown by reference number 165, perform an action associated with load balancing the traffic flow of the session or may perform an action associated with improving accuracy of a technique or model used to determine whether to offload the traffic flow. For example, the network device may modify a threshold overall value used to analyze the set of offloading indicators, retrain the data model, route packets to load balance the traffic flow, and/or the like, as each described” where “some implementations described herein provide a network device to offload traffic flow of sessions by intelligently identifying sessions for offloading using a set of offloading indicators and/or machine learning techniques. For example, the network device may monitor traffic flow associated with a group of sessions. In this case, the network device may intelligently determine to offload traffic flow of particular sessions by using a set of offloading indicators an/or machine learning techniques to identify optimal sessions for offloading” (Dods, page 14, column 3, lines 34-43). Examiner notes that the offload rate is the utilization rate, the set value is 95%, and the server device is the network device. Examiner further notes that the first inference on the first model is the identification of sessions to be offloaded by the network device.);
Xiong and Dods are considered analogous to the claimed invention because they both retrain server and edge devices. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Xiong to retrain based on offloading rate like in Dods. Doing so is advantageous because “this causes the network device to efficiently and effectively utilize resources by periodically verifying that sessions are still optimal sessions for offloading” (Dods, page 16, column 8, line 65 – page 17, column 9, line 2).
Regarding claim 2, Xiong in view of Dods teaches the processing method according to claim 1. Xiong in view of Dods further teaches
wherein the relearning of at least one of the first model or the second model is executed by using data having a larger contribution, compared to other data, to a variation in load or the decrease in inference accuracy in a target data group (Xiong, page 14, paragraph 0041, “At decision 35 the lower level devices may consider whether this suggested media content is acceptable by monitoring data distribution, monitoring the confidence level of inferences, and/or testing with unused historical data. If deemed acceptable, at step 36 the suggested media content may be shared with the user using the user device. After sharing the suggested media content with the user at step 36, or if the suggested media content is determined to not be acceptable at decision 35, the lower level devices may collect any useful data regarding the correct action taken, or the unacceptable suggested media content, and send this data to the cloud. At step 38, the cloud service may retrain the model based on the new data received and the process may start over at step 33.” Examiner notes that monitoring the confidence level of inferences and deeming it unacceptable is a basis of a decrease in inference accuracy. Examiner notes that the lower level devices are the edge device, the cloud is the server device, and new data received is the data having a larger contribution to the decrease in inference accuracy. Examiner additionally notes that the model is the first model, and the retrained model is the second model.).
Regarding claim 3, Xiong in view of Dods teaches the processing method according to claim 1. Xiong in view of Dods further teaches
wherein target data on which the second inference is executed and an inference result in the second inference of the target data in a target data group are set as learning data, and the relearning of the first model is executed (Xiong, page 13, paragraph 0033-0034, “Also, data may be sent from edge device 3 to fog node 2 and from fog node 2 to cloud server 4. Data received from fog node 2 may be used by cloud server 4 for learning purposes. Specifically, at cloud server 4 computers may be trained and retrained using the data received from fog node 2. Learning algorithms may run over the data ultimately resulting in new or updated models 27 that may be shared with fog node 2 and edge device 3 and may be used for inferencing. [0034] In accordance with one aspect of the configuration disclosed in FIG. 5, edge device 3 and/or fog node 2 may run inferencing locally, thus distributing computation to the lower level. By running inferencing locally, network bandwidth may be conserved and latency of the system may be reduced. Alternatively, edge device 3 and/or fog node 2 may request that cloud server 4 provide an inference if edge device 3 and/or fog node 2 is not confident in the local inference or otherwise questions the accuracy of the local inference.” Examiner notes that the first inference is the inference running locally and the second inference is the cloud server providing an inference. Examiner further notes that the first model is the new model and the retrained model is the second model. Examiner notes that the cloud server has the inference result and the target data. Examiner further notes that the inference and the target data from the cloud service is sent to the edge device in order for the new model to be retrained.).
Regarding claim 4, Xiong in view of Dods teaches the processing method according to claim 1. Xiong in view of Dods further teaches
The processing method according to claim 1, wherein target data on which the second inference is executed and a corrected inference result obtained by correcting an inference result in the second inference of the target data in a target data group are set as learning data, and the relearning of the second model is executed (Xiong, page 13, paragraph 0033-0034, “Also, data may be sent from edge device 3 to fog node 2 and from fog node 2 to cloud server 4. Data received from fog node 2 may be used by cloud server 4 for learning purposes. Specifically, at cloud server 4 computers may be trained and retrained using the data received from fog node 2. Learning algorithms may run over the data ultimately resulting in new or updated models 27 that may be shared with fog node 2 and edge device 3 and may be used for inferencing. [0034] In accordance with one aspect of the configuration disclosed in FIG. 5, edge device 3 and/or fog node 2 may run inferencing locally, thus distributing computation to the lower level. By running inferencing locally, network bandwidth may be conserved and latency of the system may be reduced. Alternatively, edge device 3 and/or fog node 2 may request that cloud server 4 provide an inference if edge device 3 and/or fog node 2 is not confident in the local inference or otherwise questions the accuracy of the local inference.” Examiner notes that the first inference is the inference running locally and the second inference is the cloud server providing an inference. Examiner further notes that the first model is the new model and the retrained model is the second model. Examiner notes that the cloud server obtains the local inference, or inference result, and the target data from the edge device. Examiner further notes that the cloud server creates a corrected inference and proceeds to use the corrected inference and the target data to restart the cycle of training and retraining the edge device and cloud server.).
Regarding claim 5, Xiong teaches
A processing system that performs first inference in an edge device and performs second inference in a server device, the processing system comprising: processing circuitry configured to (Xiong, page 13, paragraph 0034, “In accordance with one aspect of the configuration disclosed in FIG. 5, edge device 3 and/or fog node 2 may run inferencing locally, thus distributing computation to the lower level. By running inferencing locally, network bandwidth may be conserved and latency of the system may be reduced. Alternatively, edge device 3 and/or fog node 2 may request that cloud server 4 provide an inference if edge device 3 and/or fog node 2 is not confidence in the local inference or otherwise questions the accuracy of the local inference” where “Software 15 may be non-transitory computer readable medium run on processor 8” (Xiong, page 12, paragraph 0022). Examiner notes that the first inference is the inference running locally and the second inference is the cloud server providing an inference.):
execute relearning of at least one of a first model that performs the first inference or a second model that performs the second inference (Xiong, page 14, paragraph 0041, “At decision 35 the lower level devices may consider whether this suggested media content is acceptable by monitoring data distribution, monitoring the confidence level of inferences, and/or testing with unused historical data. If deemed acceptable, at step 36 the suggested media content may be shared with the user using the user device. After sharing the suggested media content with the user at step 36, or if the suggested media content is determined to not be acceptable at decision 35, the lower level devices may collect any useful data regarding the correct action taken, or the unacceptable suggested media content, and send this data to the cloud. At step 38, the cloud service may retrain the model based on the new data received and the process may start over at step 33.” Examiner notes that the lower level devices are the edge device and the cloud is the server device. Examiner further notes that retraining the model is relearning the first model.).
dispose a model for which the relearning is performed in the edge device or the server device (Xiong, page 14, paragraph 0041, “At decision 35 the lower level devices may consider whether this suggested media content is acceptable by monitoring data distribution, monitoring the confidence level of inferences, and/or testing with unused historical data. If deemed acceptable, at step 36 the suggested media content may be shared with the user using the user device. After sharing the suggested media content with the user at step 36, or if the suggested media content is determined to not be acceptable at decision 35, the lower level devices may collect any useful data regarding the correct action taken, or the unacceptable suggested media content, and send this data to the cloud. At step 38, the cloud service may retrain the model based on the new data received and the process may start over at step 33.” Examiner notes that the lower level devices are the edge device and the cloud is the server device. Examiner further notes that disposing a model for which the relearning is performed in the server device is the cloud service retraining the model.).
Xiong does not explicitly disclose an offload rate, but Dods does disclose
determine whether or not an offload rate is increased from a set value in at least one of the edge device or the server device (Dods, page 17, column 9, lines 26-38, “In this case, if a particular memory utilization rate of a line card satisfies a threshold memory utilization rate (e.g., exceeds 95%, exceeds 97%, drops below 5%, drops below 3%, etc.), then the network device may, as shown by reference number 165, perform an action associated with load balancing the traffic flow of the session or may perform an action associated with improving accuracy of a technique or model used to determine whether to offload the traffic flow. For example, the network device may modify a threshold overall value used to analyze the set of offloading indicators, retrain the data model, route packets to load balance the traffic flow, and/or the like, as each described.” Examiner notes that the offload rate is the utilization rate, the set value is 95%, and the server device is the network device.)
execute relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value (Dods, page 17, column 9, lines 26-38, “In this case, if a particular memory utilization rate of a line card satisfies a threshold memory utilization rate (e.g., exceeds 95%, exceeds 97%, drops below 5%, drops below 3%, etc.), then the network device may, as shown by reference number 165, perform an action associated with load balancing the traffic flow of the session or may perform an action associated with improving accuracy of a technique or model used to determine whether to offload the traffic flow. For example, the network device may modify a threshold overall value used to analyze the set of offloading indicators, retrain the data model, route packets to load balance the traffic flow, and/or the like, as each described” where “some implementations described herein provide a network device to offload traffic flow of sessions by intelligently identifying sessions for offloading using a set of offloading indicators and/or machine learning techniques. For example, the network device may monitor traffic flow associated with a group of sessions. In this case, the network device may intelligently determine to offload traffic flow of particular sessions by using a set of offloading indicators an/or machine learning techniques to identify optimal sessions for offloading” (Dods, page 14, column 3, lines 34-43). Examiner notes that the offload rate is the utilization rate, the set value is 95%, and the server device is the network device. Examiner further notes that the first inference on the first model is the identification of sessions to be offloaded by the network device.);
Xiong and Dods are considered analogous to the claimed invention because they both retrain server and edge devices. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Xiong to retrain based on offloading rate like in Dods. Doing so is advantageous because “this causes the network device to efficiently and effectively utilize resources by periodically verifying that sessions are still optimal sessions for offloading” (Dods, page 16, column 8, line 65 – page 17, column 9, line 2).
Regarding claim 6, Xiong teaches
A non-transitory computer-readable recording medium storing computer executable instructions which, when executed by a computer, cause the computer to execute a process of first inference in an edge device and performs second inference in a server device, the process comprising (Xiong, page 12, paragraph 0022, “Referring now to FIG. 2, exemplary functional blocks of fog node 2 are illustrated. In particular, fog node 2 may include processor 8 coupled to memory 9, such as flash memory, electrically erasable programmable read only memory, and/or volatile memory. Processor 8 may be suitable for machine learning computation….Software 15 may be non-transitory computer readable medium run on processor 8” where “In accordance with one aspect of the configuration disclosed in FIG. 5, edge device 3 and/or fog node 2 may run inferencing locally, thus distributing computation to the lower level. By running inferencing locally, network bandwidth may be conserved and latency of the system may be reduced. Alternatively, edge device 3 and/or fog node 2 may request that cloud server 4 provide an inference if edge device 3 and/or fog node 2 is not confidence in the local inference or otherwise questions the accuracy of the local inference” (Xiong, page 13, paragraph 0034). Examiner notes that the first inference is the inference running locally and the second inference is the cloud server providing an inference. Examiner further notes that the executable instruction are the programmable read only memory.):
executing relearning of at least one of a first model that performs the first inference or a second model that performs the second inference (Xiong, page 14, paragraph 0041, “At decision 35 the lower level devices may consider whether this suggested media content is acceptable by monitoring data distribution, monitoring the confidence level of inferences, and/or testing with unused historical data. If deemed acceptable, at step 36 the suggested media content may be shared with the user using the user device. After sharing the suggested media content with the user at step 36, or if the suggested media content is determined to not be acceptable at decision 35, the lower level devices may collect any useful data regarding the correct action taken, or the unacceptable suggested media content, and send this data to the cloud. At step 38, the cloud service may retrain the model based on the new data received and the process may start over at step 33.” Examiner notes that the lower level devices are the edge device and the cloud is the server device. Examiner further notes that retraining the model is relearning the first model.).
disposing a model for which the relearning is performed in the edge device or the server device (Xiong, page 14, paragraph 0041, “At decision 35 the lower level devices may consider whether this suggested media content is acceptable by monitoring data distribution, monitoring the confidence level of inferences, and/or testing with unused historical data. If deemed acceptable, at step 36 the suggested media content may be shared with the user using the user device. After sharing the suggested media content with the user at step 36, or if the suggested media content is determined to not be acceptable at decision 35, the lower level devices may collect any useful data regarding the correct action taken, or the unacceptable suggested media content, and send this data to the cloud. At step 38, the cloud service may retrain the model based on the new data received and the process may start over at step 33.” Examiner notes that the lower level devices are the edge device and the cloud is the server device. Examiner further notes that disposing a model for which the relearning is performed in the server device is the cloud service retraining the model.).
Xiong does not explicitly disclose an offload rate, but Dods does disclose
determining whether or not an offload rate is increased from a set value in at least one of the edge device or the server device (Dods, page 17, column 9, lines 26-38, “In this case, if a particular memory utilization rate of a line card satisfies a threshold memory utilization rate (e.g., exceeds 95%, exceeds 97%, drops below 5%, drops below 3%, etc.), then the network device may, as shown by reference number 165, perform an action associated with load balancing the traffic flow of the session or may perform an action associated with improving accuracy of a technique or model used to determine whether to offload the traffic flow. For example, the network device may modify a threshold overall value used to analyze the set of offloading indicators, retrain the data model, route packets to load balance the traffic flow, and/or the like, as each described.” Examiner notes that the offload rate is the utilization rate, the set value is 95%, and the server device is the network device.)
executing relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value (Dods, page 17, column 9, lines 26-38, “In this case, if a particular memory utilization rate of a line card satisfies a threshold memory utilization rate (e.g., exceeds 95%, exceeds 97%, drops below 5%, drops below 3%, etc.), then the network device may, as shown by reference number 165, perform an action associated with load balancing the traffic flow of the session or may perform an action associated with improving accuracy of a technique or model used to determine whether to offload the traffic flow. For example, the network device may modify a threshold overall value used to analyze the set of offloading indicators, retrain the data model, route packets to load balance the traffic flow, and/or the like, as each described” where “some implementations described herein provide a network device to offload traffic flow of sessions by intelligently identifying sessions for offloading using a set of offloading indicators and/or machine learning techniques. For example, the network device may monitor traffic flow associated with a group of sessions. In this case, the network device may intelligently determine to offload traffic flow of particular sessions by using a set of offloading indicators an/or machine learning techniques to identify optimal sessions for offloading” (Dods, page 14, column 3, lines 34-43). Examiner notes that the offload rate is the utilization rate, the set value is 95%, and the server device is the network device. Examiner further notes that the first inference on the first model is the identification of sessions to be offloaded by the network device.);
Xiong and Dods are considered analogous to the claimed invention because they both retrain server and edge devices. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Xiong to retrain based on offloading rate like in Dods. Doing so is advantageous because “this causes the network device to efficiently and effectively utilize resources by periodically verifying that sessions are still optimal sessions for offloading” (Dods, page 16, column 8, line 65 – page 17, column 9, line 2).
Regarding claim 7, Xiong in view of Dods teaches the processing method of claim 1. Xiong in view of Dods further teaches
correcting an inference result by the second model (Xiong, page 13, paragraph 0033-0034, “Also, data may be sent from edge device 3 to fog node 2 and from fog node 2 to cloud server 4. Data received from fog node 2 may be used by cloud server 4 for learning purposes. Specifically, at cloud server 4 computers may be trained and retrained using the data received from fog node 2. Learning algorithms may run over the data ultimately resulting in new or updated models 27 that may be shared with fog node 2 and edge device 3 and may be used for inferencing. [0034] In accordance with one aspect of the configuration disclosed in FIG. 5, edge device 3 and/or fog node 2 may run inferencing locally, thus distributing computation to the lower level. By running inferencing locally, network bandwidth may be conserved and latency of the system may be reduced. Alternatively, edge device 3 and/or fog node 2 may request that cloud server 4 provide an inference if edge device 3 and/or fog node 2 is not confident in the local inference or otherwise questions the accuracy of the local inference.” Examiner notes that the first inference is the inference running locally and the second inference is the cloud server providing an inference. Examiner further notes that the first model is the new model and the retrained model is the second model. Examiner notes that the cloud server obtains the local inference, or inference result, and the target data from the edge device. Examiner further notes that the cloud server creates a corrected inference and proceeds to use the corrected inference and the target data to restart the cycle of training and retraining the edge device and cloud server.)
Xiong does not explicitly teach, but Dods does teach
determining, with respect to the first model, whether or not a tendency of a target data group is changed based on the offload rate (Dods, page 17, column 9, line 57- column 10, line 14, “In this example, the network device might analyze the set of offloading indicators to determine an overall indicator value (e.g., which may be a value between a 1 and a 10), and may compare the overall indicator value to the threshold overall indicator value of 7. Further assume the network device determines that a particular line card has a memory utilization rate of 96%, and that the memory utilization rat satisfies a threshold memory utilization rate of 95%. In this case, the network device may modify the overall threshold indicator value (e.g., by increasing the value from 7-9), which may decrease a number of sessions selected for offloading, and reduce chances of the line cards memory reaching a memory utilization rate of 100%. Additionally, or alternatively, the network device may retrain the data model. For example, if a particular memory utilization rate of a line card satisfies the threshold memory utilization rate, the network device may retrain the data model by modifying one or more values used to analyze the set of offloading indicators. Modifying the one or more values may allow the data model to process offloading indicators such that there is a decrease in a number of sessions selected for offloading (or an increase in a number of sessions selected for offloading).” Examiner notes that the first model is the data model prior to retraining and the second model is the retrained model. Examiner further notes that the offload rate is the utilization rate and the tendency of the target data group is the number of sessions selected for offloading.);
and determining, with respect to the second model, whether or not the tendency of the target data group is changed based on a correction rate (Dods, page 17, column 9, line 57- column 10, line 14, “In this example, the network device might analyze the set of offloading indicators to determine an overall indicator value (e.g., which may be a value between a 1 and a 10), and may compare the overall indicator value to the threshold overall indicator value of 7. Further assume the network device determines that a particular line card has a memory utilization rate of 96%, and that the memory utilization rat satisfies a threshold memory utilization rate of 95%. In this case, the network device may modify the overall threshold indicator value (e.g., by increasing the value from 7-9), which may decrease a number of sessions selected for offloading, and reduce chances of the line cards memory reaching a memory utilization rate of 100%. Additionally, or alternatively, the network device may retrain the data model. For example, if a particular memory utilization rate of a line card satisfies the threshold memory utilization rate, the network device may retrain the data model by modifying one or more values used to analyze the set of offloading indicators. Modifying the one or more values may allow the data model to process offloading indicators such that there is a decrease in a number of sessions selected for offloading (or an increase in a number of sessions selected for offloading).” Examiner notes that the first model is the data model prior to retraining and the second model is the retrained model. Examiner further notes that the correction rate is the utilization rate and the tendency of the target data group is the number of sessions selected for offloading. Additionally, Examiner notes that after retraining the model, the model proceeds to use the target data to restart the cycle of training and retraining.);
Xiong and Dods are considered analogous to the claimed invention because they both retrain server and edge devices. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Xiong to retrain based on offloading rate like in Dods. Doing so is advantageous because “this causes the network device to efficiently and effectively utilize resources by periodically verifying that sessions are still optimal sessions for offloading” (Dods, page 16, column 8, line 65 – page 17, column 9, line 2).
Response to Arguments
Examiner notes that the objection to the title has been withdrawn in light of the instant amendments. Examiner further notes that claim objections have been made in light of the instant amendments.
Examiner notes that the 112(b) rejections previously made on claims 3 and 4 have been overcome in light of the instant amendments. Examiner further notes that the 112(b) rejection previously made on claim 2 has been maintained due to a lack of clarity of the term “larger contribution.” Examiner also notes that additional 112(b) rejections have been made in light of the instant amendments.
On pages 7-8, Applicant argues:
Claims 1, 5, and 6, as amended, are directed to a particular technique for maintaining model accuracy across a distributed edge-server processing architecture. Specifically, the claims recite triggering relearning of at least one of the first model (used for first inference in the edge device) or the second model (used for second inference in the server device) based on whether an offload rate is increased from a set value, and then disposing the relearned model in the edge device or the server device, so that the disposed model performs subsequent inference. The technical effect is described at paragraph [0009] of the publication: "it is possible to appropriately execute relearning of the models respectively disposed in the edge and the cloud, and maintain the accuracy of the models." This is a specific improvement in distributed machine learning systems, not a generic application of an abstract idea "on a computer."
Regarding the Applicant’s argument that these elements provide an improvement, Examiner respectfully disagrees. Specifically, Examiner notes that the Applicant provides a bare assertion of an improvement without the detail necessary to be apparent to one of ordinary skill in the art and, thus, cannot provide an improvement (MPEP 2106.04(d)(1)).
On page 8, Applicant argues:
Although the Office Action characterized the "determining" limitation as a mental process, that limitation, as amended, expressly recites determining whether or not an offload rate is increased from a set value in at least one of the edge device or the server device. As described at paragraph [0050] of the publication, the offload rate reflects "an amount of transmission transmitted from the DNNl to the DNN2, that is, the edge device 30 to the server device 20." The offload rate is therefore a machine-measured characteristic of the distributed system's actual operation - not a quantity that a person can practically observe or reason about in the human mind.
Regarding the Applicant’s argument that “determining” is not a mental process, Examiner respectfully disagrees. Specifically, Examiner notes that claim 1 recites “determining whether or not an offload rate is increased from a set value in at least one of the edge device or the server device.” Under broadest reasonable interpretation, Examiner notes that this encompasses a human mentally observing an offload rate, or number value, and judging whether the rate is greater than a set value. The edge device or server device under broadest reasonable interpretation is the location on which the rate can be observed. Thus this limitation is a judgement. Examiner further notes that a claim that requires a computer may still recite a mental process (MPEP 2106.04(a)(2)(III)(C)).
On pages 8-9, Applicant argues:
Even assuming that any limitation could be characterized as an abstract idea, the additional elements integrate the alleged exception into a practical application. As the Federal Circuit explained in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016), "[s]oftware can make non-abstract improvements to computer technology just as hardware improvements can." In McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314 (Fed. Cir. 2016), the court held that claims using "limited rules in a process specifically designed to achieve an improved technological result" are not directed to an abstract idea. Here, Claims 1, 5, and 6 recite a specific process - detecting an increase in the offload rate from a set value, executing relearning of the first or second model on that basis, and disposing the relearned model in the edge device or the server device - that maintains the accuracy of the models respectively disposed in the edge and the cloud (see paragraph [0009]). This is precisely the type of "specific asserted improvement in computer capabilities" that Enfish identifies as patent-eligible. 822 F.3d at 1336.
Regarding the Applicant’s argument that these elements provide an improvement, Examiner respectfully disagrees. Specifically, Examiner notes that the Applicant provides a bare assertion of an improvement without the detail necessary to be apparent to one of ordinary skill in the art and, thus, cannot provide an improvement (MPEP 2106.04(d)(1)).
On page 9, Applicant argues:
Even if the additional elements were considered individually conventional, their ordered combination supplies the requisite inventive concept. In BASCOM Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016), the Federal Circuit held that "an inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces." The claimed arrangement - offload-rate-triggered relearning of at least one of the first model or the second model, followed by disposing the relearned model in the edge device or the server device - is not conventional in the field. As described at paragraph [0006] of the publication, prior approaches required "an administrator of a system to perform complicated processing of confirming all the data acquired during operation, determining which data is used and at which timing to execute relearning." The recited combination automates this determination on the basis of the offload rate and disposes the relearned model in the appropriate device of the distributed architecture, providing significantly more than the alleged abstract idea.
Regarding the Applicant’s argument that the claims provide significantly more, Examiner respectfully disagrees. Specifically, Examiner notes that the judicial exception alone is not eligible subject matter (MPEP 2106.04(d)(III)). However, the additional elements have been evaluated to not provide significantly more than the judicial exception since “A processing method executed by a processing system that performs first inference in an edge device and performs second inference in a server device, the processing method comprising” and “executing relearning of at least one of a first model that performs the first inference or a second model that performs the second inference in a case where it is determined that the offload rate is increased from the set value” uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)), and “disposing a model for which the relearning is performed in the edge device or the server device” is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
On page 10, Applicant argues:
For at least the foregoing reasons, Claims 1, 5, and 6, as amended, are not directed to an abstract idea, integrate any alleged abstract idea into a practical application, and recite significantly more than any alleged abstract idea. Dependent Claims 2-4 and 7 depend from independent Claim 1 and are therefore patent-eligible for at least the reasons given for Claim 1. Withdrawal of the§ 101 rejection is respectfully requested.
Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above.
On pages 10-11, Applicant argues:
The Office Action relies principally on paragraph [0041] of Xiong, which describes that "the lower level devices may consider whether this suggested media content is acceptable by monitoring data distribution, monitoring the confidence level of inferences, and/or testing with unused historical data" and that, when content is deemed unacceptable, "the cloud service may retrain the model based on the new data received" (see Xiong, [0041]). Xiong's decision criterion is therefore acceptability of "suggested media content" at the lower-level device, evaluated by monitoring data distribution and inference confidence - not whether an offload rate has increased from a set value. Xiong is silent regarding an offload rate, much less detection of an increase of the offload rate from a set value as the trigger for relearning.
Applicant’s arguments with respect to claim(s) 1 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.
On page 11, Applicant argues:
Furthermore, Xiong's paragraph [0041] describes only that, after the cloud service retrains the model, "the process may start over at step 33" (see Xiong, [0041]); Xiong does not describe disposing the relearned model in the edge device or the server device as a step of the claimed method.
Regarding the Applicant’s argument that Xiong does not disclose disposing a model for which the relearning is performed in the edge device or the server device, Examiner respectfully disagrees. Specifically, Examiner notes that Xiong does teach this limitation in paragraph 0041, “At decision 35 the lower level devices may consider whether this suggested media content is acceptable by monitoring data distribution, monitoring the confidence level of inferences, and/or testing with unused historical data. If deemed acceptable, at step 36 the suggested media content may be shared with the user using the user device. After sharing the suggested media content with the user at step 36, or if the suggested media content is determined to not be acceptable at decision 35, the lower level devices may collect any useful data regarding the correct action taken, or the unacceptable suggested media content, and send this data to the cloud. At step 38, the cloud service may retrain the model based on the new data received and the process may start over at step 33” (Xiong, page 14, paragraph 0041). Examiner notes that the lower level devices are the edge device and the cloud is the server device. Examiner further notes that disposing a model for which the relearning is performed in the server device is the cloud service retraining the model since under BRI, disposing a model encompasses using a model.
On page 11, Applicant argues:
Accordingly, Xiong does not describe or reasonably suggest each and every feature recited in Claim 1, and Xiong does not describe or reasonably suggest each and every feature recited in Claims 2-6 and new Claim 7 for substantially similar reasons. Withdrawal of the rejection is respectfully requested.
Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Dunne et al. (US 2020/0272899 A1) also describes edge and server devices that update or relearn neural networks. Khan et al. (US 2020/0027009 A1) also discusses edge and server devices that update local and global models.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN R LAU whose telephone number is (571)272-1429. The examiner can normally be reached Monday - Thursday: 8:00 am - 6:00 pm EST.
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/K.R.L./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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