DETAILED ACTION
This action is in response to the Applicant Response filed 18 November 2025 for application 17/336,640 filed 02 June 2021.
Claim(s) 1, 6, 13-14, 17-19, 24, 26 is/are currently amended.
Claim(s) 4, 8-9, 21-22 is/are cancelled.
Claim(s) 1-3, 5-7, 10-20, 23-27 is/are pending.
Claim(s) 1-3, 5-7, 10-20, 23-27 is/are rejected.
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 .
Priority
This application makes reference to or appears to claim subject matter disclosed in Application No. 62/704,976, filed 05 June 2020 and Application No. 17/143,796 filed 07 January 2021. If applicant desires to claim the benefit of a prior-filed application under 35 U.S.C. 119(e), 120, 121, 365(c) or 386(c), the instant application must contain, or be amended to contain, a specific reference to the prior-filed application in compliance with 37 CFR 1.78. If the application was filed before September 16, 2012, the specific reference must be included in the first sentence(s) of the specification following the title or in an application data sheet (ADS) in compliance with pre-AIA 37 CFR 1.76; if the application was filed on or after September 16, 2012, the specific reference must be included in an ADS in compliance with 37 CFR 1.76. For benefit claims under 35 U.S.C. 120, 121, 365(c), or 386(c), the reference must include the relationship (i.e., continuation, divisional, or continuation-in-part) of the applications.
If the instant application is a utility or plant application filed under 35 U.S.C. 111(a), the specific reference must be submitted during the pendency of the application and within the later of four months from the actual filing date of the application or sixteen months from the filing date of the prior application. If the application is a national stage application under 35 U.S.C. 371, the specific reference must be submitted during the pendency of the application and within the later of four months from the date on which the national stage commenced under 35 U.S.C. 371(b) or (f), four months from the date of the initial submission under 35 U.S.C. 371 to enter the national stage, or sixteen months from the filing date of the prior application. See 37 CFR 1.78(a)(4) for benefit claims under 35 U.S.C. 119(e) and 37 CFR 1.78(d)(3) for benefit claims under 35 U.S.C. 120, 121, 365(c), or 386(c). This time period is not extendable and a failure to submit the reference required by 35 U.S.C. 119(e) and/or 120, where applicable, within this time period is considered a waiver of any benefit of such prior application(s) under 35 U.S.C. 119(e), 120, 121, 365(c), and 386(c). A benefit claim filed after the required time period may be accepted if it is accompanied by a grantable petition to accept an unintentionally delayed benefit claim under 35 U.S.C. 119(e) (see 37 CFR 1.78(c)) or under 35 U.S.C. 120, 121, 365(c), or 386(c) (see 37 CFR 1.78(e)). The petition must be accompanied by (1) the reference required by 35 U.S.C. 120 or 119(e) and by 37 CFR 1.78 to the prior application (unless previously submitted), (2) the applicable petition fee under 37 CFR 1.17(m)(1) or (2), and (3) a statement that the entire delay between the date the benefit claim was due under 37 CFR 1.78 and the date the claim was filed was unintentional. The presentation of a benefit claim may result in an additional fee under 37 CFR 1.17(w)(1) or (2) being required, if the earliest filing date for which benefit is claimed under 35 U.S.C. 120, 121, 365(c), or 386(c) and 1.78(d) in the application is more than six years before the actual filing date of the application. The Director may require additional information where there is a question whether the delay was unintentional. The petition should be addressed to: Mail Stop Petition, Commissioner for Patents, P.O. Box 1450, Alexandria, Virginia 22313-1450.
If the reference to the prior application was previously submitted within the time period set forth in 37 CFR 1.78 but was not included in the location in the application required by the rule (e.g., if the reference was submitted in an oath or declaration or the application transmittal letter), and the information concerning the benefit claim was recognized by the Office as shown by its inclusion on the first filing receipt, the petition under 37 CFR 1.78 and the petition fee under 37 CFR 1.17(m)(1) or (2) are not required. Applicant is still required to submit the reference in compliance with 37 CFR 1.78 by filing an ADS in compliance with 37 CFR 1.76 with the reference (or, if the application was filed before September 16, 2012, by filing either an amendment to the first sentence(s) of the specification or an ADS in compliance with pre-AIA 37 CFR 1.76). See MPEP § 211.02.
Response to Arguments
Applicant’s arguments regarding the incorporation by reference have been fully considered but are not persuasive, as discussed below.
Applicant's arguments regarding the objections to the claims have been fully considered and, in light of the amendments to the claims, are persuasive. However, in light of the amendments to the claims, new claim objections have arisen, as noted below.
Applicant's arguments regarding the 35 U.S.C. 112(b) rejection(s) of claim(s) 24, 26 have been fully considered and, in light of the amendments to the claims, are persuasive. The 35 U.S.C. 112(b) rejection(s) of claim(s) 24, 26 has/have been withdrawn. However, in light of the amendments to the claims, new 35 U.S.C. 112(b) rejections have arisen, as noted below.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of claims 1-3, 5-7, 10-20, 23-27 have been fully considered but are not persuasive. Applicant argues that in light of the August 4, 2025 memo (the “memo”), that the claims should be eligible as they recite neural networks. Examiner respectfully disagrees, as this is a misinterpretation of the memo. The memo was release to clarify the steps of an eligibility analysis as recited in the MPEP, not to change the analysis. Further, the memo does create automatic eligibility for any given technological area. As detailed below, the analysis was performed on the claims and the claims were found to be ineligible. Examiner also notes that the Office release several AI-related SME examples (Examples 47-49) to assist with the analysis of AI-related technologies. Therefore, the 35 U.S.C. 101 rejection of claims 1-3, 5-7, 10-20, 23-27 is maintained.
Applicant’s arguments regarding the 35 U.S.C. 103 rejections of claims 1-3, 5-7, 10-20, 23-27 have been fully considered and are not persuasive. Applicant argues that the cited references do not teach “neural networks,” but instead sensor networks and/or graphs, and therefore do not teach the recited claim limitations. Examiner respectfully disagrees. There is no requirement that the references use the same labels as used in the claim language. All that is required in that the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious to a person having ordinary skill in the art. While the references may use the terms sensor nodes and/or graph nodes, instead of neural network neurons, the references teach the scope of the subject matter if the invention as recited in the claims, as detailed below. Therefore, the 35 U.S.C. 103 rejections of claims 1-3, 5-7, 10-20, 23-27 is maintained.
Specification/Drawings
The amendment to add inadvertently omitted material pursuant to 37 CFR 1.57(b) filed 17 October 2024 is not in compliance with 37 CFR 1.57(b) because (1) there is no evidence that the inadvertently omitted portion is not completely contained in the prior-filed application; (2) a copy of the prior-filed application (except where the prior-filed application is an application filed under 35 U.S.C. 111) was not submitted; and (3) applicant did not identify where the inadvertently omitted portion of the specification or drawings can be found in the prior-filed application.
The amendment filed 17 October 2024 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows: [0099]-[0133] of the Specification and Figures 13-27 filed 17 October 2024.
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Objections
Claim(s) 1-3, 5-7, 10-20, 23-27 is/are objected to because of the following informalities:
Claim 1, lines 16-17, the respective heterogeneous neuron should read “the respective heterogeneous test neuron”
Claim 13, line 18, each heterogeneous neurons should read “the respective heterogeneous test neuron”
Claim 14, line 2, the test neuron value should read “the modeled heterogeneous test neuron values”
Claim 19, lines 19-20, the respective heterogeneous neuron should read “the respective heterogeneous test neuron”
Claim 20, line 2, the corresponding actual values should read “the corresponding neuron values”
Claim 27, line 2, the heterogeneous test neurons should read “the at least some heterogeneous test neurons”
Claims 2-3, 5-7, 10-12, 14-18, 20, 23-27 are objected to due to their dependence, either directly or indirectly, on claims 1, 13-14, 19-20, 27
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.
Claims 15-18 are 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.
Claim 15 recites at least one corresponding actual value while failing to provide a proper antecedent basis. In light of the amendments to claim 13, it is now unclear as to what the term refers. Correction or clarification is required.
Examiner’s Note: For the purposes of examination, the term will be interpreted as “at least one of the known sensor values.”
Claims 16-18 are rejected under 35 U.S.C. 112(b) due to their dependence, either directly or indirectly, on claim 15.
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.
Claim(s) 1-3, 5-7, 10-20, 23-27 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy.
The limitation of ... produce modeled heterogenous test neuron values and a modeled value of the heterogenous target neuron, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of comparing modeled heterogenous test neuron values to known sensor values, to determine quality of the modeled heterogenous test neuron values, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of calculating connection strengths of each modeled heterogeneous test neuron value relative to the heterogenous target neuron, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of calculating accuracy of the heterogenous target neuron using: quality of the modeled heterogenous test neuron values, and connection strengths between the heterogenous target neuron and the heterogenous test neurons, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – one or more computers. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – heterogenous neural network, heterogenous test neurons, heterogenous target neuron, cost function, activation functions. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites running a heterogenous neural network with heterogenous test neurons and a heterogenous target neuron using known sensor values at heterogenous test neurons for a cost function ... which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
The claim recites wherein the heterogenous test neurons comprise activation functions, at least some of the activation functions comprising multiple equations within the respective heterogenous neuron which is simply additional information regarding the neurons, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of:
one or more computers amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
heterogenous neural network, heterogenous test neurons, heterogenous target neuron, cost function, activation functions amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the neurons do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 2 carries out the method of claim 1 but for the recitation of additional element(s) of wherein running the heterogenous neural network comprises using state time series values as input into the heterogenous neural network for a running period.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein running the heterogenous neural network comprises using state time series values as input into the heterogenous neural network for a running period which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy. The Step 2A Prong One Analysis for claim 2 is applicable here since claim 3 carries out the method of claim 2 but for the recitation of additional element(s) of wherein the state time series values are weather values affecting a controlled space.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the state time series values and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the state time series values do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy.
The limitation of wherein calculating connection strength comprises using automatic differentiated vector gradients, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy.
The limitation of wherein calculating accuracy of the heterogenous target neuron comprises matrix multiplying the quality of the modeled heterogenous test neuron values by connection strengths between the heterogenous target neuron and the heterogenous test neurons, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses matrix multiplication.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy.
The limitation of ... determine connection strengths between the heterogenous target neuron and the heterogenous test neurons comprises using automatic differentiation to backpropagate from the heterogenous target neuron to the heterogenous test neurons, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses backpropagation.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – machine learning techniques. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein running the heterogenous neural network comprises using machine learning techniques ... which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
machine learning techniques amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy.
The limitation of ... warming up the heterogenous neural network using at least a portion of an initial state time series values to modify the internal values, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the heterogenous neural network has internal values ... which is simply additional information regarding the heterogenous neural network, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of:
additional information regarding the heterogenous neural network do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy.
The limitation of warming up the heterogenous neural network ..., as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites ... pre-running the heterogenous neural network using successively larger portions of an input wave form until a goal state is reached which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 12 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the heterogenous neural network models a controlled system, and wherein the controlled system comprises a controlled building system, a process control system, an HVAC system, an energy system, or an irrigation system.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the heterogenous neural network models a controlled system, and wherein the controlled system comprises a controlled building system, a process control system, an HVAC system, an energy system, or an irrigation system which is simply additional information regarding the heterogenous neural network, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – controlled building system, process control system, HVAC system, energy system, irrigation system. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of:
controlled building system, process control system, HVAC system, energy system, irrigation system amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
additional information regarding the heterogenous neural network do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for computing neuron accuracy.
The limitation of ... produce modeled heterogenous test neuron values and a modeled value of the heterogenous target neuron, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of compare modeled heterogenous test neuron values to known sensor values, to determine quality of the modeled heterogenous test neuron values, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of calculate connection strengths of each modeled heterogenous test neuron value relative to the heterogenous target neuron, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of calculate accuracy of the heterogenous target neuron using: quality of the modeled heterogenous test neuron values, and connection strengths between the heterogenous target neuron and the heterogenous test neurons, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – system, processor, memory. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – heterogenous neural network, heterogenous test neurons, heterogenous target neuron, cost function, activation functions. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites run a heterogenous neural network with heterogenous test neurons and a heterogenous target neuron using known sensor values at heterogenous test neurons for a cost function ... which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
The claim recites wherein the heterogenous test neurons comprise activation functions, at least some of the activation functions comprising multiple equations within each heterogenous neuron which is simply additional information regarding the neurons, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of:
system, processor, memory amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
heterogenous neural network, heterogenous test neurons, heterogenous target neuron, cost function, activation functions amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the neurons do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for computing neuron accuracy.
The limitation of matrix multiplying the quality of test neuron values by connection strengths between the at least one heterogenous target neuron and the heterogenous test neurons, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses matrix multiplication.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for computing neuron accuracy. The Step 2A Prong One Analysis for claim 13 is applicable here since claim 15 carries out the system of claim 13 but for the recitation of additional element(s) of wherein at least one corresponding actual value comprises a sensor state value.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for computing neuron accuracy. The Step 2A Prong One Analysis for claim 15 is applicable here since claim 16 carries out the system of claim 15 but for the recitation of additional element(s) of wherein the sensor state value is derived from a sensor in a controlled space.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the sensor state value is derived from a sensor in a controlled space which is simply additional information regarding the data, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – sensor. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of:
sensor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 17, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for computing neuron accuracy. The Step 2A Prong One Analysis for claim 16 is applicable here since claim 17 carries out the system of claim 16 but for the recitation of additional element(s) of using state time series values as input into the heterogenous neural network for a running period.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites using state time series values as input into the heterogenous neural network for a running period which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 18, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for computing neuron accuracy. The Step 2A Prong One Analysis for claim 17 is applicable here since claim 18 carries out the system of claim 17 but for the recitation of additional element(s) of wherein calculating connection strengths uses automatic differentiation.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the connection strengths and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the connection strengths do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 19, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a(n) computer-readable storage medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable storage medium … which … perform a method for computing neuron accuracy.
The limitation of initializing values for at least some heterogenous test neurons in a heterogenous neural network, the at least some heterogenous test neurons representing corresponding actual values, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of specifying a heterogenous target neuron in the heterogenous neural network, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of optimizing the heterogenous neural network using the actual values producing a solved heterogenous neural network with a target neuron value and test neuron values, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of ... determine connection strengths between the heterogenous target neuron and the at least some heterogenous test neurons, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of determining quality of the test neuron values by comparing test neuron values in the solved heterogenous neural network to corresponding neuron values, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of calculating accuracy of the heterogenous target neuron using: quality of the test neuron values, and connection strengths between the heterogenous target neuron and at least some heterogenous test neuron, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer-readable storage medium, instructions, processor. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – heterogenous test neurons, heterogenous neural network, heterogenous target neuron, machine learning techniques, activation functions. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites using machine learning techniques ... which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
The claim recites wherein the at least some heterogenous test neurons comprise activation functions, at least some of the activation functions comprising multiple equations within the respective heterogenous neuron which is simply additional information regarding the neurons, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of:
computer-readable storage medium, instructions, processor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
heterogenous test neurons, heterogenous neural network, heterogenous target neuron, machine learning techniques, activation functions amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the neurons do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 20, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a(n) computer-readable storage medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable storage medium … which … perform a method for computing neuron accuracy. The Step 2A Prong One Analysis for claim 19 is applicable here since claim 20 carries out the computer-readable storage medium of claim 19 but for the recitation of additional element(s) of wherein the corresponding actual values are sensor values that correspond to heterogenous test neuron locations.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 23, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 23 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 23 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the heterogenous test neurons are connected by edges with values, and wherein at least one of the multiple equations take as input at least two values.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the neurons and the equations and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the neurons and the equations do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 24, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 24 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for computing neuron accuracy. The Step 2A Prong One Analysis for claim 23 is applicable here since claim 24 carries out the method of claim 23 but for the recitation of additional element(s) of wherein at least one value of the edges with values comprises a resistance value or a capacitance value.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the edges and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the edges do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 25, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 25 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for computing neuron accuracy. The Step 2A Prong One Analysis for claim 13 is applicable here since claim 25 carries out the system of claim 13 but for the recitation of additional element(s) of wherein the heterogenous test neurons are connected by edges with values, and wherein at least one of the multiple equations take as input at least two values.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the neurons and the equations and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the neurons and the equations do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 26, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 26 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for computing neuron accuracy. The Step 2A Prong One Analysis for claim 25 is applicable here since claim 26 carries out the system of claim 25 but for the recitation of additional element(s) of wherein at least one value of the edges with values comprises a resistance value or a capacitance value.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the edges and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the edges do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 27, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 27 is directed to a(n) computer-readable storage medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable storage medium … which … perform a method for computing neuron accuracy. The Step 2A Prong One Analysis for claim 19 is applicable here since claim 27 carries out the computer-readable storage medium of claim 19 but for the recitation of additional element(s) of wherein the heterogenous test neurons are connected by edges with values, and wherein at least one of the multiple equations take as input at least two values.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the neurons and the equations and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the neurons and the equations do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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) 1-3, 10-13, 15-17, 19-20, 23, 25, 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deshpande et al. (Model-Driven Data Acquisition in Sensor Networks, hereinafter referred to as “Deshpande”) in view of Tulone et al. (PAQ: Time Series Forecasting for Approximate Query Answering in Sensor Networks, hereinafter referred to as “Tulone”).
Regarding claim 1 (Currently Amended), Deshpande teaches a method for computing neuron accuracy (Deshpande, section 2 – teaches solving neuron query given an confidence [accuracy] requirement) implemented by one or more computers (Deshpande, section 2 – teaches operating code on a query processor) comprising:
running a heterogenous neural network with heterogenous test neurons and a heterogenous target neuron using known sensor values at heterogenous test neurons for a cost function to produce modeled heterogenous test neuron values and a modeled value of the heterogenous target neuron (Deshpande, section 2 – teaches a sensor network with sensor nodes [neurons] and determining a queried neuron value [modeled target neuron] using known neuron values [modeled test neurons]; Deshpande, section 4 – teaches a cost function to determine the modeled values; see also Deshpande, section 2- teaches sensor network of differing sensor value types such as temperature and voltage [heterogeneous neural network]);
comparing modeled heterogenous test neuron values to known sensor values, to determine quality of the modeled heterogenous test neuron values (Deshpande, section 3 – teaches comparing calculated values to known past values to determine the quality of a chosen value within a given confidence);
calculating connection strengths of each modeled heterogeneous test neuron value relative to the heterogenous target neuron (Deshpande, section 4 – teaches determining edge values between sensor nodes);
calculating accuracy of the heterogenous target neuron using:
quality of the modeled heterogenous test neuron values (Deshpande, section 3 – teaches comparing calculated values to known past values to determine the quality of a chosen sensor within a given confidence), and
connection strengths between the heterogenous target neuron and the heterogenous test neurons (Deshpande, section 4 – teaches determining the accuracy of the chosen sensor within a given confidence based on the edge values).
However, Deshpande does not explicitly teach wherein the heterogenous test neurons comprise activation functions, at least some of the activation functions comprising multiple equations within each heterogenous neuron.
Tulone teaches wherein the heterogenous test neurons comprise activation functions, at least some of the activation functions comprising multiple equations within the respective heterogenous neuron (Tulone, section 4.1 – teaches for each sensor device in the sensor network, creating either a multivariate model or multiple univariate models).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Deshpande with the teachings of Tulone in order to improve efficiency in the field of querying sensor networks (Tulone, section 4.1 – “... For these reasons, it is more efficient for the sensor to compute m univariate AR models, although the multivariate model provides additional predictive power as it is able to capture correlations between measurements...”).
Regarding claim 2 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the method of claim 1 as noted above. Deshpande further teaches wherein running the heterogenous neural network comprises using state time series values as input into the heterogenous neural network for a running period (Deshpande, section 3.2 – teaches using time series data as input to the model).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Deshpande and Tulone for the same reasons as disclosed in claim 1 above.
Regarding claim 3 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the method of claim 2 as noted above. Deshpande further teaches wherein the state time series values are weather values affecting a controlled space (Deshpande, section 2 – teaches temperature values in a controlled space; see also Deshpande, section 5).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Deshpande and Tulone for the same reasons as disclosed in claim 2 above.
Regarding claim 10 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the method of claim 1 as noted above. Deshpande further teaches wherein the heterogenous neural network has internal values (Deshpande, section 2 – teaches the model includes attributes), and further comprising warming up the heterogenous neural network using at least a portion of an initial state time series values to modify the internal values (Deshpande, section 2.2, 2.4, 3.2 - teaches initializing the model with initial attributes at time t=0).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Deshpande and Tulone for the same reasons as disclosed in claim 1 above.
Regarding claim 11 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the method of claim 10 as noted above. Deshpande further teaches warming up the heterogenous neural network by pre-running the heterogenous neural network using successively larger portions of an input wave form until a goal state is reached (Deshpande, section 3.2 – teaches initializing the model using more data until time t is reached to condition the model).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Deshpande and Tulone for the same reasons as disclosed in claim 10 above.
Regarding claim 12 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the method of claim 1 as noted above. Deshpande further teaches wherein the heterogenous neural network models a controlled system, and wherein the controlled system comprises a controlled building system, a process control system, an HVAC system, an energy system, or an irrigation system (Deshpande, section 2 – teaches sensors in a controlled system of a lab [building] to measure temperature and voltage [energy system]; see also Deshpande, section 5).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Deshpande and Tulone for the same reasons as disclosed in claim 1 above.
Regarding claim 13, it is the system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Deshpande further teaches a system for computing neuron accuracy (Deshpande, section 2 – teaches solving neuron query given an confidence [accuracy] requirement) comprising:
a processor (Deshpande, section 2 – teaches operating code on a query processor);
a memory in operational communication with the processor (Deshpande, section 2 – teaches operating code [requiring memory] on a query processor), wherein the processor is configured to …
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Deshpande and Tulone for the same reasons as disclosed in claim 1 above.
Regarding claim 15 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the system of claim 13 as noted above.
Deshpande further teaches wherein at least one corresponding actual value comprises a sensor state value (Deshpande, section 3.2 – teaches using time series data as input to the model).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Deshpande and Tulone for the same reasons as disclosed in claim 13 above.
Regarding claim 16 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the system of claim 15 as noted above.
Deshpande further teaches wherein the sensor state value is derived from a sensor in a controlled space (Deshpande, section 2 – teaches temperature values in a controlled space; see also Deshpande, section 5).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Deshpande and Tulone for the same reasons as disclosed in claim 15 above.
Regarding claim 17 (Currently Amended), Deshpande in view of Tulone teaches all of the limitations of the system of claim 16 as noted above. Deshpande further teaches further comprising using state time series values as input into the heterogenous neural network for a running period (Deshpande, section 3.2 – teaches using time series data as input to the model).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Deshpande in view of Tulone for the same reasons as disclosed in claim 16 above.
Regarding claim 19 (Currently Amended), Deshpande teaches a non-transitory computer-readable storage medium configured with data and instructions which upon execution by a processor (Deshpande, section 2 – teaches operating code [requiring memory] on a query processor) perform a method for computing neuron accuracy (Deshpande, section 2 – teaches solving neuron query given an confidence [accuracy] requirement), the method comprising:
initializing values (Deshpande, section 2.2, 2.4, 3.2 - teaches initializing the model with initial attributes at time t=0) for at least some heterogenous test neurons in a heterogenous neural network, the at least some heterogenous test neurons representing corresponding actual values (Deshpande, section 2 – teaches a sensor network with sensor nodes [neurons] and determining an queried neuron value [target neuron] using known neuron values [test neurons]; see also Deshpande, section 2- teaches sensor network of differing sensor value types such as temperature and voltage [heterogeneous neural network]);
specifying a heterogenous target neuron in the heterogenous neural network (Deshpande, section 2 – teaches a sensor network with sensor nodes [neurons] and determining a queried neuron value [target neuron] using known neuron values [test neurons]);
optimizing the heterogenous neural network using the actual values producing a solved heterogenous neural network with a target neuron value and test neuron values (Deshpande, section 2 – teaches a sensor network with sensor nodes [neurons] and determining an queried neuron value [modeled target neuron] using known neuron values [modeled test neurons]; Deshpande, section 4 – teaches a cost function to determine the modeled values; see also Deshpande, section 2- teaches sensor network of differing sensor value types such as temperature and voltage [heterogeneous neural network]);
using machine learning techniques to determine connection strengths between the heterogenous target neuron and the at least some heterogenous test neurons (Deshpande, section 4 – teaches determining edge values between sensor nodes);
determining quality of the test neuron values by comparing test neuron values in the solved heterogenous neural network to corresponding neuron values (Deshpande, section 3 – teaches comparing calculated values to known past values to determine the quality of a chosen value within a given confidence);
calculating accuracy of the heterogenous target neuron using:
quality of the test neuron values (Deshpande, section 3 – teaches comparing calculated values to known past values to determine the quality of a chosen sensor within a given confidence), and
connection strengths between the heterogenous target neuron and at least some heterogenous test neuron (Deshpande, section 4 – teaches determining the accuracy of the chosen sensor within a given confidence based on the edge values).
However, Deshpande does not explicitly teach wherein the heterogenous test neurons comprise activation functions, at least some of the activation functions comprising multiple equations within each heterogenous neuron.
Tulone teaches wherein the at least some heterogenous test neurons comprise activation functions, at least some of the activation functions comprising multiple equations within the respective heterogenous neuron (Tulone, section 4.1 – teaches for each sensor device in the sensor network, creating either a multivariate model or multiple univariate models).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Deshpande with the teachings of Tulone in order to improve efficiency in the field of querying sensor networks (Tulone, section 4.1 – “... For these reasons, it is more efficient for the sensor to compute m univariate AR models, although the multivariate model provides additional predictive power as it is able to capture correlations between measurements...”).
Regarding claim 20 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the computer-readable storage medium of claim 19 as noted above. Deshpande further teaches wherein the corresponding actual values are sensor values that correspond to heterogenous test neuron locations (Deshpande, section 2 – teaches sensor values corresponding to sensor locations; see also Deshpande, section 5).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Deshpande and Tulone for the same reasons as disclosed in claim 19 above.
Regarding claim 23 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the method of claim 1 as noted above.
Deshpande further teaches wherein the heterogenous test neurons are connected by edges with values (Deshpande, section 4 – teaches determining edge values between sensor nodes).
Tulone further teaches wherein at least one of the multiple equations take as input at least two values (Tulone, section 4.1 – teaches for each sensor device in the sensor network, creating either a multivariate model or multiple univariate models [It would be obvious that both could be creates for a sensor device]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Deshpande and Tulone in order to use multiple equations with multiple inputs to improve efficiency and capture data correlations (Tulone, section 4.1).
Regarding claim 25 (Previously Presented), the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Deshpande in view of Tulone for the reasons set forth in the rejection of claim 23.
Regarding claim 27 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the computer-readable storage medium of claim 19 as noted above.
Deshpande further teaches wherein the heterogenous test neurons are connected by edges with values (Deshpande, section 4 – teaches determining edge values between sensor nodes).
Tulone further teaches wherein at least one of the multiple equations take as input at least two values (Tulone, section 4.1 – teaches for each sensor device in the sensor network, creating either a multivariate model or multiple univariate models [It would be obvious that both could be creates for a sensor device]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Deshpande and Tulone in order to use multiple equations with multiple inputs to improve efficiency and capture data correlations (Tulone, section 4.1).
Claim(s) 5-7, 14, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deshpande in view of Tulone and further in view of Zhang et al. (Modeling IoT Equipment with Graph Neural Networks, hereinafter referred to as “Zhang”).
Regarding claim 5 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the method of claim 1 as noted above. However, Deshpande in view of Tulone does not explicitly teach wherein calculating connection strength comprises using automatic differentiated vector gradients.
Zhang teaches wherein calculating connection strength comprises using automatic differentiated vector gradients (Zhang, section III.D – teaches determining connection strengths using gradient backpropagation).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Deshpande in view of Tulone with the teachings of Zhang in order to consider temporal and inner logic relations of data in the field of querying sensor networks (Zhang, Abstract – “Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different dimensions of data collected from embedded sensors. This paper proposes a graph neural network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both temporal and inner logic relations of data, in which vertices denote sensor data and edges denote relationships between vertices. The GNNM-IoT model's relationships between sensors with neural networks to produce nonlinear complex relationships. We have evaluated the GNNM-IoT using air-conditioner data from a world leading IoT company, which demonstrates that it is effective and outperforms ARIMA and LSTM methods.”).
Regarding claim 6 (Currently Amended), Deshpande in view of Tulone in view of Zhang teaches all of the limitations of the method of claim 5 as noted above.
Zhang further teaches wherein calculating accuracy of the heterogenous target neuron comprises matrix multiplying the quality of the modeled heterogenous test neuron values by connection strengths between the heterogenous target neuron and the heterogenous test neurons (Zhang, section III.D – teaches multiplying the connection strengths and the quality of values).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Deshpande, Tulone and Zhang in order to calculate target neuron accuracy to consider temporal and inner logic relations of data (Zhang, Abstract).
Regarding claim 7 (Previously Presented), Deshpande in view of Tulone teaches all of the limitations of the method of claim 1 as noted above. However, Deshpande in view of Tulone does not explicitly teach wherein running the heterogenous neural network comprises using machine learning techniques to determine connection strengths between the heterogenous target neuron and the heterogenous test neurons comprises using automatic differentiation to backpropagate from the heterogenous target neuron to the heterogenous test neurons.
Zhang teaches wherein running the heterogenous neural network comprises using machine learning techniques to determine connection strengths between the heterogenous target neuron and the heterogenous test neurons comprises using automatic differentiation to backpropagate from the heterogenous target neuron to the heterogenous test neurons (Zhang, section III.D – teaches determining connection strengths using gradient backpropagation).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Deshpande in view of Tulone with the teachings of Zhang in order to consider temporal and inner logic relations of data in the field of querying sensor networks (Zhang, Abstract – “Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different dimensions of data collected from embedded sensors. This paper proposes a graph neural network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both temporal and inner logic relations of data, in which vertices denote sensor data and edges denote relationships between vertices. The GNNM-IoT model's relationships between sensors with neural networks to produce nonlinear complex relationships. We have evaluated the GNNM-IoT using air-conditioner data from a world leading IoT company, which demonstrates that it is effective and outperforms ARIMA and LSTM methods.”).
Regarding claim 14 (Currently Amended), Deshpande in view of Tulone teaches all of the limitations of the system of claim 13 as noted above. However, Deshpande in view of Tulone does not explicitly teach wherein the function calculator comprises matrix multiplying the quality of test neuron values by connection strengths between the at least one heterogenous target neuron and the heterogenous test neurons.
Zhang teaches matrix multiplying the quality of test neuron values by connection strengths between the at least one heterogenous target neuron and the heterogenous test neurons (Zhang, section III.D – teaches multiplying the connection strengths and the quality of values).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Deshpande in view of Tulone with the teachings of Zhang in order to consider temporal and inner logic relations of data in the field of querying sensor networks (Zhang, Abstract – “Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different dimensions of data collected from embedded sensors. This paper proposes a graph neural network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both temporal and inner logic relations of data, in which vertices denote sensor data and edges denote relationships between vertices. The GNNM-IoT model's relationships between sensors with neural networks to produce nonlinear complex relationships. We have evaluated the GNNM-IoT using air-conditioner data from a world leading IoT company, which demonstrates that it is effective and outperforms ARIMA and LSTM methods.”).
Regarding claim 18 (Currently Amended), Deshpande in view of Tulone teaches all of the limitations of the system of claim 17 as noted above. However, Deshpande in view of Tulone does not explicitly teach wherein at least one of the machine learning techniques uses automatic differentiation to calculate connection strengths.
Zhang teaches wherein calculating connection strengths uses automatic differentiation (Zhang, section III.D – teaches determining connection strengths using gradient backpropagation).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Deshpande in view of Tulone with the teachings of Zhang in order to consider temporal and inner logic relations of data in the field of querying sensor networks (Zhang, Abstract – “Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different dimensions of data collected from embedded sensors. This paper proposes a graph neural network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both temporal and inner logic relations of data, in which vertices denote sensor data and edges denote relationships between vertices. The GNNM-IoT model's relationships between sensors with neural networks to produce nonlinear complex relationships. We have evaluated the GNNM-IoT using air-conditioner data from a world leading IoT company, which demonstrates that it is effective and outperforms ARIMA and LSTM methods.”).
Claim(s) 24, 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deshpande in view of Tulone and further in view of Fazenda et al. (Context-Based Thermodynamic Modeling of Building Spaces, hereinafter referred to as “Fazenda”).
Regarding claim 24 (Currently Amended), Deshpande in view of Tulone teaches all of the limitations of the method of claim 23 as noted above. However, Deshpande in view of Tulone does not explicitly teach wherein at least one value comprises a resistance value or a capacitance value.
Fazenda teaches wherein at least one value comprises a resistance value or a capacitance value (Fazenda, section 3 – teaches using resistance and capacitance values in modeling).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Deshpande in view of Tulone with the teachings of Fazenda in order to improve modeling using contexts in the field of querying sensor networks (Fazenda, Abstract – “Thermodynamic models are frequently used for modeling the thermal behavior of building spaces. How-ever, the occurrence of events such as, for example, doors, windows and blinds being opened or closed, can drastically affect the underlying processes that govern the dynamics of temperature evolution of building spaces, rendering current thermodynamic models less effective for control and prediction. This article presents a framework for appropriate model structure and parameter selection that accounts for such discrete disturbances based on the notion of context. Contexts are modeled as discrete configurations, capable of representing different thermodynamic behavior models for a building space. Depending on how context changes, our thermodynamic model transitions through a set of different linear time-invariant sub-models. Each sub-model is effective in representing the thermal behavior of the space under a given context and the result is a hybrid automaton that effectively adjusts to the discrete and continuous dynamics of the building environment. We present an application example and use the out-puts of EnergyPlus as reference for model performance evaluation. We show, through different context changes, how a context-based model can be used to represent, with reasonable accuracy, the evolution of temperatures in a simulated thermal zone.”).
Regarding claim 26 (Currently Amended), the rejection of claim 25 is incorporated herein. Further, the limitations in this claim are taught by Deshpande in view of Tulone and further in view of Fazenda for the reasons set forth in the rejection of claim 24.
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.
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125