DETAILED ACTION
This action is in response to the submission filed 03 January 2024 for application 18/403,542. Currently claims 1-20 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant's claim for domestic priority based on the provisional application 62/704,976 filed on 05 June 2020.
Information Disclosure Statement
Information disclosure statements (IDS) were submitted on 29 February 2024, 28 March 2024, 12 April 2024, 05 September 2024, 26 November 2024, 08 January 2025, 15 January 2025 and 03 June 2026. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because in Fig. 7 the reference character “720” has been used to designate both “Zone 1” and “Wall 3”. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 19 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 19 recites the limitation "… the first heterogeneous neural network…" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards abstract ideas without significantly more
Regarding claims 1-8:
According to the first step (Step 1) of the 101 analysis, claims 1-8 are directed to a method for training a neural network performed by a computer-based machine (process) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding claim 1:
In the next step (Step 2A, prong 1) of the analysis, the limitation of:
calculating a cost function using the neural network output and the first desired output
Under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components because one can calculate a cost function using outputs. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the same step (Step 2A, prong 1) of the analysis, the limitation of:
determining a next input in an iterative manner with the next input used for a next iteration, until a goal state is met
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component because one can determine what the next input could be in an iterative manner until a goal is met. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitations of:
proceeding, starting with the first desired output and a first input, to run a heterogeneous neural network,
and using a further input of the trained neural network as a second desired output for a heterogeneous neural network.
are considered to be additional elements and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a method proceeding, starting with the first desired output and a first input, to run a heterogeneous neural network, and using a further input of the trained neural network as a second desired output for a heterogeneous neural network) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the same step (Step 2A, prong 2) of the analysis, the limitation of:
receiving a first desired output;
is considered to be an additional element and as recited represent insignificant extra-solution activity because it is mere data gathering. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity.
In the same step (Step 2A, prong 2) of the analysis, the limitations of:
outputting a neural network output;
producing a trained neural network;
are considered to be additional elements and as recited represents insignificant extra-solution activity that is data output, because it is a mere nominal or tangential addition to the claim and is therefore not indicative of integration into a practical application. See MPEP 2106.05(g).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method proceeding, starting with the first desired output and a first input, to run a heterogeneous neural network, and using a further input of the trained neural network as a second desired output for a heterogeneous neural network is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
In the same step (Step 2B) of the analysis, as discussed above the additional element of receiving a first desired output, which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more.
In the same step (Step 2B), the recitation of, outputting a neural network output; producing a trained neural network; limitation amounts to insignificant extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the heterogenous neural network models multiple activation functions of neurons in the neural network as physics equations.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a method wherein the heterogenous neural network models multiple activation functions of neurons in the neural network as physics equations) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein the heterogenous neural network models multiple activation functions of neurons in the neural network as physics equations is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 3:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the heterogenous neural network comprises at least some neurons arranged topologically.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a method wherein the heterogenous neural network comprises at least some neurons arranged topologically) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein the heterogenous neural network comprises at least some neurons arranged topologically is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 4:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein at least some neurons have inputs associated with state values.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a method wherein at least some neurons have inputs associated with state values) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein at least some neurons have inputs associated with state values, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 5:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein at least one input is associated with temperature.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a method wherein at least one input is associated with temperature) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein at least one input is associated with temperature, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 6:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the heterogenous neural network comprises at least one neuron with multiple variables.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a method wherein the heterogenous neural network comprises at least one neuron with multiple variables) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein the heterogenous neural network comprises at least one neuron with multiple variables, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 7:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the multiple variables describe state moving through a location.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a method wherein the multiple variables describe state moving through a location) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein the multiple variables describe state moving through a location, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 8:
In the next step (Step 2A, prong 1) of the analysis, the limitation of:
wherein at least one of the multiple variables comprises a value meaningful outside of the heterogenous neural network.
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component because one has to evaluate the multiple variables and decide if it comprises a value that is meaningful outside of the heterogeneous neural network. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible.
Regarding claims 9-16:
According to the first step (Step 1) of the 101 analysis, claims 9-16 are directed to a non-transitory machine-readable medium encoded with instructions for execution by a processor for training a heterogeneous neural network, the non-transitory machine-readable medium comprising: instructions (manufacture) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding claim 9:
In step (Step 2A, prong 2) of the analysis, the limitation of:
a non-transitory machine-readable medium encoded with instructions for execution by a processor for training a heterogeneous neural network, the non-transitory machine-readable medium comprising: instructions:
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is a non-transitory machine-readable medium encoded with instructions for execution by a processor for training a heterogeneous neural network, the non-transitory machine-readable medium comprising: instructions to perform something) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, a non-transitory machine-readable medium encoded with instructions for execution by a processor for training a heterogeneous neural network, the non-transitory machine-readable medium comprising: instructions to perform something, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
The rest of the limitations of claim 9 are substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1 as explained above. The claim is not patent eligible.
Regarding claim 10:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein at least several neurons in the heterogenous neural network represent devices.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a non-transitory machine-readable medium wherein at least several neurons in the heterogenous neural network represent devices) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the non-transitory machine-readable medium wherein at least several neurons in the heterogenous neural network represent devices, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 11:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the at least several neurons representing devices are arranged in the heterogenous neural network with connections between the at least several neurons representing connections between the devices.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a non-transitory machine-readable medium wherein the at least several neurons representing devices are arranged in the heterogenous neural network with connections between the at least several neurons representing connections between the devices) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the non-transitory machine-readable medium wherein the at least several neurons representing devices are arranged in the heterogenous neural network with connections between the at least several neurons representing connections between the devices, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 12:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
where connections between neurons pass variable values.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a non-transitory machine-readable medium where connections between neurons pass variable values) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the non-transitory machine-readable medium where connections between neurons pass variable values, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 13:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein activation functions within at least some of the neurons comprise at least one equation.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a non-transitory machine-readable medium wherein activation functions within at least some of the neurons comprise at least one equation) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the non-transitory machine-readable medium wherein activation functions within at least some of the neurons comprise at least one equation, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 14:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the at least one equation uses the variable values of incoming connections to determine values of outgoing connections.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a non-transitory machine-readable medium wherein the at least one equation uses the variable values of incoming connections to determine values of outgoing connections) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the non-transitory machine-readable medium wherein the at least one equation uses the variable values of incoming connections to determine values of outgoing connections, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 15:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein at least two neurons have different equations as activation functions.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a non-transitory machine-readable medium wherein at least two neurons have different equations as activation functions) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the non-transitory machine-readable medium wherein at least two neurons have different equations as activation functions, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 16:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the different equations represent device behavior.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a non-transitory machine-readable medium wherein the different equations represent device behavior) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the non-transitory machine-readable medium wherein the different equations represent device behavior, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claims 17-20:
According to the first step (Step 1) of the 101 analysis, claims 17-20 are directed to an apparatus for training a neural network, the apparatus comprising: a memory and processor, the processor being configured to perform something (manufacture) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding claim 17:
In step (Step 2A, prong 2) of the analysis, the limitation of:
apparatus for training a neural network, the apparatus comprising: a memory and processor, the processor being configured to:
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is an apparatus for training a neural network, the apparatus comprising: a memory and processor, the processor being configured to perform something) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, an apparatus for training a neural network, the apparatus comprising: a memory and processor, the processor being configured to perform something, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
The rest of the limitations of claim 17 are substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1 as explained above. The claim is not patent eligible.
Regarding claim 18:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the second heterogeneous neural network models equipment.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is an apparatus wherein the second heterogeneous neural network models equipment) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the apparatus wherein the second heterogeneous neural network models equipment, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 19:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the first heterogeneous neural network models space.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is an apparatus wherein the first heterogeneous neural network models space) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the apparatus wherein the first heterogeneous neural network models space, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 20:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the first input is a state input curve.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is an apparatus wherein the first input is a state input curve) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the apparatus wherein the first input is a state input curve, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-11, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation, 2019) in view of Yang et al (US 20170076195 A1).
Regarding claim 1:
Zhang teaches: A method for training a neural network performed by a computer-based machine, the method comprising: receiving a first desired output ([Page 290, Column 2, Paragraph 3] output the next observation o(t + 1) as shown in Figure 2);
proceeding, starting with the first desired output and a first input, to run a ... neural network, outputting a neural network output, calculating a cost function using the neural network output and the first desired output, and determining a next input in an iterative manner with the next input used for a next iteration, until a goal state is met, producing a trained neural network ([Page 289, Column 2, Section 3.2 Partial Observable Markov Decision Process, Section 3.2.1] s(t ): state vector at time step t , which isTo,Mo,Qo,T2,T3,W3 in Section 3.1.. c(t ) is the cost function. [Page 291, Column 1, Last Paragraph] The detailed procedure is shown in Algorithm 2. [Page 291, Column 2, Last Paragraph] Algorithm 2: Model-based Reinforcement Learning for HVAC Control with Neural Network Policy. Note: Algorithm 2 shows input, output. The for loop shows iteration);
and using a further input of the trained neural network as a second desired output for a neural network ([Page 291, Column 2, Last Paragraph] Algorithm 2. Note: Algorithm 2 has a for loop which is an iteration showing the use a further input of the trained neural network as a second desired output).
However, Zhang does not explicitly disclose: heterogenous neural network; a second heterogeneous neural network.
Yang teaches, in an analogous system: heterogenous neural network ([0030] heterogeneous neural network);
a second heterogeneous neural network ([0030] heterogeneous neural network [0117] a second neural network to generate a second neural network output label based on the one or more second feature maps).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang to incorporate the teachings of Yang to use heterogenous neural network and a second heterogeneous neural network. One would have been motivated to do this modification because doing so would give the benefit of outputting a label based on the one or more second feature maps as taught by Yang [0117].
Regarding claim 3:
The system of Zhang and Yang teaches: The method of claim 2 (as shown above).
Zhang further teaches: wherein the heterogenous neural network comprises at least some neurons arranged topologically ([Page 291, Figure 2] Note: Figure 2 shows the arrangement of neurons in an organized manner corresponding to at least some neurons arranged topologically).
Regarding claim 4:
The system of Zhang and Yang teaches: The method of claim 3 (as shown above).
Zhang further teaches: wherein at least some neurons have inputs associated with state values ([Page 289, Column 2, Paragraph 1] T2, T3 andW3 are sensible states, Qo and Mo are latent/hidden states).
Regarding claim 5:
The system of Zhang and Yang teaches: The method of claim 4 (as shown above).
Zhang further teaches: wherein at least one input is associated with temperature ([Page 289, Column 2, Paragraph 1] T2 is the temperature of supply air, T3 is the temperature of thermal space).
Regarding claim 6:
The system of Zhang and Yang teaches: The method of claim 1 (as shown above).
Zhang further teaches: wherein the heterogenous neural network comprises at least one neuron with multiple variables ([Page 289, Column 1, Section 3.1, Paragraph 3] The control variables in this system are 1) the speed of the variableair-volume (VAV) fan as shown in position 2. 2) the water flow rate from the chiller to the heat exchanger. [Page 289, Column 2, Paragraph 1] is the volumetric flow rate of air (ft /min) and gpm is the flow rate of chilled water (gal/min). Among them, f and gpm are control variables).
Regarding claim 7:
The system of Zhang and Yang teaches: The method of claim 6 (as shown above).
Zhang further teaches: wherein the multiple variables describe state moving through a location ([Page 289, Column 2, Paragraph 1] T2, T3 andW3 are sensible states, Qo and Mo are latent/hidden states. Note: Also see Figure 1 showing some states moving through a location).
Regarding claim 8:
The system of Zhang and Yang teaches: The method of claim 6 (as shown above).
Zhang further teaches: wherein at least one of the multiple variables comprises a value meaningful outside of the heterogenous neural network ([Page 289, Column 1, Section 3.1, Paragraph 3] The control variables in this system are 1) the speed of the variableair-volume (VAV) fan as shown in position 2. 2) the water flow rate from the chiller to the heat exchanger. [Page 289, Column 2, Paragraph 1] is the volumetric flow rate of air (ft /min) and gpm is the flow rate of chilled water (gal/min). Among them, f and gpm are control variables. Note: Teh values of these variables are meaningful to the HVAC which is outside of the heterogenous neural network).
Regarding claim 9:
Claim 9 is substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1 as explained above.
Regarding claim 10:
The system of Zhang and Yang teaches: The non-transitory machine-readable medium of claim 9 (as shown above).
However, Zhang does not explicitly disclose: wherein at least several neurons in the heterogenous neural network represent devices.
Yang further teaches: wherein at least several neurons in the heterogenous neural network represent devices ([0041] As shown, in some examples, distributed neural network framework 200 may include a cloud computing resource 203. However, distributed neural network framework 200 may include any computing device, system, or the like capable of implementing to generate object label 253 having any suitable form factor such as a desktop computer, a laptop computer, a mobile computing device, or the like. As discussed, in some examples, distributed neural network framework 200 may provide a shared lower level format and specialized fully connected portion. In some examples, such a shared lower level format may be implemented to provide a scalable end-to-end heterogeneous distributed neural network framework).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang to incorporate the teachings of Yang wherein at least several neurons in the heterogenous neural network represent devices. One would have been motivated to do this modification because doing so would give the benefit of providing a scalable end-to-end heterogeneous distributed neural network framework as taught by Yang [0041].
Regarding claim 11:
The system of Zhang and Yang teaches: The non-transitory machine-readable medium of claim 10 (as shown above).
However, Zhang does not explicitly disclose: wherein the at least several neurons representing devices are arranged in the heterogenous neural network with connections between the at least several neurons representing connections between the devices.
Yang further teaches: wherein the at least several neurons representing devices are arranged in the heterogenous neural network with connections between the at least several neurons representing connections between the devices ([0021] The distributed device such as a camera or the like may transmit the feature maps to a gateway or a cloud computing resource or the like. As discussed herein, a distributed device or devices may attain sensor data for processing via a distributed neural network. Example embodiments are provided herein with details associated with cameras and image data for the sake of clarity of presentation. However, any sensor data attained or generated via any suitable distributed device or devices may be processed using the techniques, systems, devices, computing platforms, and articles discussed herein. Note: Transmitting corresponds to connections between the devices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang to incorporate the teachings of Yang wherein the at least several neurons representing devices are arranged in the heterogenous neural network with connections between the at least several neurons representing connections between the devices. One would have been motivated to do this modification because doing so would give the benefit of providing a scalable end-to-end heterogeneous distributed neural network framework as taught by Yang [0041].
Regarding claim 17:
Claim 17 is substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1 as explained above.
Regarding claim 18:
The system of Zhang and Yang teaches: The apparatus of claim 17 (as shown above).
However, Zhang does not explicitly disclose: wherein the second heterogeneous neural network models equipment.
Yang further teaches: wherein the second heterogeneous neural network models equipment ([0030] heterogeneous neural network [0117] a second neural network to generate a second neural network output label based on the one or more second feature maps. Note: Figures 2, 3, 4, 5, and 9 shows equipment).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang to incorporate the teachings of Yang wherein the second heterogeneous neural network models equipment. One would have been motivated to do this modification because doing so would give the benefit of providing a scalable end-to-end heterogeneous distributed neural network framework as taught by Yang [0041].
Regarding claim 19:
The system of Zhang and Yang teaches: The apparatus of claim 17 (as shown above).
Zhang further teaches: wherein the first neural network models space ([Page 289, Column 2, Paragraph 1] T3 is the temperature of thermal space, Vs is the volume of thermal space [Page 291, Column 2, Last Paragraph] Algorithm 2: Model-based Reinforcement Learning for HVAC Control with Neural Network Policy).
However, Zhang does not explicitly disclose: heterogeneous neural network.
Yang further teaches: heterogeneous neural network ([0030] heterogeneous neural network).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang to incorporate the teachings of Yang to use heterogeneous neural network. One would have been motivated to do this modification because doing so would give the benefit of providing a scalable end-to-end heterogeneous distributed neural network framework as taught by Yang [0041].
Regarding claim 20:
The system of Zhang and Yang teaches: The apparatus of claim 17 (as shown above).
Zhang further teaches: wherein the first input is a state input curve ([Page 295] Figure 9: One month test temperature curve of various algorithms).
Claims 2 and 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation, 2019) in view of Yang et al (US 20170076195 A1) and further in view of Wu et al (An IoT Tree Health Indexing Method Using Heterogeneous Neural Network, 2019).
Regarding claim 2:
The system of Zhang and Yang teaches: The method of claim 1 (as shown above).
However, the system of Zhang and Yang does not explicitly disclose: wherein the heterogenous neural network models multiple activation functions of neurons in the neural network as physics equations.
Wu further teaches: wherein the heterogenous neural network models multiple activation functions of neurons in the neural network as physics equations ([Page 66180, Column 2] FIGURE 2. The structure of the designed heterogenous neural network (HNN). [Page 66181, Column 1, Paragraph 3] is the sigmoid activation function and tanh is the hyperbolic tangent activation function. Note: Neural network are made up of neurons. Also see equations (8), (9), and (10) corresponding to physics equations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang and Yang to incorporate the teachings of Wu wherein the heterogenous neural network models multiple activation functions of neurons in the neural network as physics equations. One would have been motivated to do this modification because doing so would give the benefit of providing details of the function as taught by Wu [Page 66181, Column 1, Paragraph 3].
Regarding claim 12:
The system of Zhang and Yang teaches: The non-transitory machine-readable medium of claim 11 (as shown above).
However, the system of Zhang and Yang does not explicitly disclose: where connections between neurons pass variable values.
Wu further teaches: where connections between neurons pass variable values ([Page 66181, Column 1, Paragraph 3] is the sigmoid activation function and tanh is the hyperbolic tangent activation function. Note: Neural network are made up of neurons. Also see equations (8), (9), and (10) showing variables).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang and Yang to incorporate the teachings of Wu where connections between neurons pass variable values. One would have been motivated to do this modification because doing so would give the benefit of providing details of the function as taught by Wu [Page 66181, Column 1, Paragraph 3].
Regarding claim 13:
The system of Zhang, Yang, and Wu teaches: The non-transitory machine-readable medium of claim 12 (as shown above).
However, the system of Zhang, Yang, and Wu does not explicitly disclose: wherein activation functions within at least some of the neurons comprise at least one equation.
Wu further teaches: wherein activation functions within at least some of the neurons comprise at least one equation ([Page 66180, Column 2] FIGURE 2. The structure of the designed heterogenous neural network (HNN). [Page 66181, Column 1, Paragraph 3] is the sigmoid activation function and tanh is the hyperbolic tangent activation function. Note: Neural network are made up of neurons. Also see equations (8), (9), and (10) corresponding to at least one equation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang and Yang to incorporate the teachings of Wu wherein activation functions within at least some of the neurons comprise at least one equation. One would have been motivated to do this modification because doing so would give the benefit of providing details of the function as taught by Wu [Page 66181, Column 1, Paragraph 3].
Regarding claim 14:
The system of Zhang, Yang, and Wu teaches: The non-transitory machine-readable medium of claim 13 (as shown above).
However, the system of Zhang, Yang, and Wu does not explicitly disclose: wherein the at least one equation uses the variable values of incoming connections to determine values of outgoing connections.
Wu further teaches: wherein the at least one equation uses the variable values of incoming connections to determine values of outgoing connections ([Page 66181, Column 1, Paragraph 4] The update gate determines the amount of previous states to move forward to future states. The output of GRU layer will be fed into fusion layer and fuse with the output of hidden layer).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang and Yang to incorporate the teachings of Wu wherein the at least one equation uses the variable values of incoming connections to determine values of outgoing connections. One would have been motivated to do this modification because doing so would give the benefit of providing details of the function as taught by Wu [Page 66181, Column 1, Paragraph 3].
Regarding claim 15:
The system of Zhang and Yang teaches: The non-transitory machine-readable medium of claim 10 (as shown above).
However, the system of Zhang and Yang does not explicitly disclose: wherein at least two neurons have different equations as activation functions.
Wu further teaches: wherein at least two neurons have different equations as activation functions ([Page 66180, Column 2] FIGURE 2. The structure of the designed heterogenous neural network (HNN). [Page 66181, Column 1, Paragraph 3] is the sigmoid activation function and tanh is the hyperbolic tangent activation function. Note: Neural network are made up of neurons. The sigmoid and hyperbolic tangent are two different activation functions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang and Yang to incorporate the teachings of Wu wherein at least two neurons have different equations as activation functions. One would have been motivated to do this modification because doing so would give the benefit of providing details of the function as taught by Wu [Page 66181, Column 1, Paragraph 3].
Regarding claim 16:
The system of Zhang and Yang teaches: The non-transitory machine-readable medium of claim 10 (as shown above).
However, the system of Zhang and Yang does not explicitly disclose: wherein the different equations represent device behavior.
Wu further teaches: wherein the different equations represent device behavior ([Page 66180, Column 2] FIGURE 2. The structure of the designed heterogenous neural network (HNN). [Page 66181, Column 1, Paragraph 3] is the sigmoid activation function and tanh is the hyperbolic tangent activation function. Note: Neural network are made up of neurons. The sigmoid and hyperbolic tangent are two different activation functions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zhang and Yang to incorporate the teachings of Wu wherein the different equations represent device behavior. One would have been motivated to do this modification because doing so would give the benefit of providing details of the function as taught by Wu [Page 66181, Column 1, Paragraph 3].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Matsuoka et al (US 20140312128 A1) discloses ystems, apparatus, methods, and related computer program products for controlling heating, ventilation, and air conditioning (HVAC) systems. More particularly, this patent specification relates to techniques for generating and implementing thermodynamic models of a structure in the management of HVAC systems that control a thermal environment of that structure.
Wei et al (Deep Reinforcement Learning for Building HVAC Control, 2017) discloses data-driven approach that leverages the deep reinforcement learning (DRL) technique, to intelligently learn the effective strategy for operating the building HVAC systems. We evaluate the performance of our DRL algorithm through simulations using the widely-adopted EnergyPlus tool. Experiments demonstrate that our DRL-based algorithm is more effective in energy cost reduction compared with the traditional rulebased approach, while maintaining the room temperature within desired range.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAITANYA RAMESH JAYAKUMAR whose telephone number is (571)272-3369. The examiner can normally be reached Mon-Fri 9am-1pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at (571)272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C.R.J./ Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
