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 .
Note on Prior Art
There is no prior art rejection for claim 4.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show any details of the claimed invention as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). 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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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 § 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 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea of a mathematical relationship without significantly more. The claims recite converting a routine into a tensorized neural network (NN); converting the NN layers to tensors; converting non-linearities in the layers to non-linearities applicable to each tensor; producing an inference; and adding a gauge optimization. This judicial exception is not integrated into a practical application because the additional elements of:
A quantum processor, and various industrial applications of neural networks (e.g. claims 8-11), is merely linking to a field of use;
Training the neural network is mere instructions to use computers as a tool to perform an existing process. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of memory and classical processors are claims to generic computer parts.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The claims are so broad so as to include all machines with a classical and quantum processor that accomplish the claimed method. The nature of quantum processors is at the cutting edge of science and speculative at this point, this is true for the state of art also where real quantum processors have not demonstrated a quantum advantage in general let alone a quantum advantage in tensorizing neural networks in industrial applications. Error correction and mitigation make predictability in the art very low. Applicant provides no direction or proof working examples in their specification to show that quantum processors are useful for being used in industrial applications. The quantity of experimentation for the claimed application would be enormous. Therefore, the claims require undue experimentation.
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.
Claims 3 and 4 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 3 recites the limitation "the converted neural network". There is insufficient antecedent basis for this limitation in the claim. There is a tensorized neural network, but no converted neural network.
Claim 4 recites the limitation "the respectively converted one or more first non-linearities". There is insufficient antecedent basis for this limitation in the claim.
The term “more similar” in claim 4 is a relative term which renders the claim indefinite. The term “more similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5-8, 10-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nonlinear tensor train format for deep neural network compression by Wang et al, US 20170300808 A1 to Ronagh et al and US20220026879A1 to Kale.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Nonlinear tensor train format for deep neural network compression by Wang et al, US 20170300808 A1 to Ronagh et al, US20220026879A1 to Kale and Tensor-based framework for training flexible neural networks by Zniyed et al.
Claims 18-20 don’t include a quantum processor, so Ronagh is not used in those rejections, but the rest of the rejection is the same.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Nonlinear tensor train format for deep neural network compression by Wang et al, US 20170300808 A1 to Ronagh et al, US20220026879A1 to Kale and Gauging tensor networks with belief propagation by Tindall et al.
Claims 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Nonlinear tensor train format for deep neural network compression by Wang et al, US 20170300808 A1 to Ronagh et al, US20220026879A1 to Kale and US 20210065010 A1 to Pfeil.
Wang teaches claims 1, 12 and 18. An apparatus comprising at least one classical processor or at least one
convert, into a tensorized neural network, a predetermined machine learning routine (Wang abs “DNN”) in the form of a neural network and associated with a target machine or system or process by: (The system is the system with the DNN in it.)
converting one or more layers of a plurality of layers of the neural network into respective one or more tensor networks; and (Wang sec. 3.1 “compressing a typical FC layer in DNNs y =f(xW) in tensorizing way…” compressing in a tensorizing way is converting a layer to a tensor network.)
converting one or more first non-linearities applicable to the converted one or more layers of the neural network into one or more second non-linearities applicable to each tensor of the respective one or more tensor networks; and (Wang sec. 3.1 To obtain the corresponding mapping way from Eq. (9), all the Gk should be separated as new independent weights based on Eq. (7). Naturally, inserting extra nonlinear activation functions after contracting every Gk could reach this requirement, then we have… which we term it nonlinear TT or NTT [non-linear tensor train].” Wang abs. “a novel nonlinear tensor train (NTT) format, which contains extra nonlinear activation functions embedded in sequenced contractions and convolutions on the top of the normal TT decomposition and the proposed TT format connected by convolutions, to compensate the accuracy loss that normal TT cannot give.”)
Wang doesn’t teach a quantum processor.
However, Ronagh teaches a quantum processor. (Ronagh para 1 “a quantum processor and its use for implementing a neural network.”)
Ronagh, Wang and the claims all operate neural network. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use a quantum processor to implement a neural network because “an advantage of the quantum processor disclosed herein is that it is comprised only of a system of quantum circuits with degree two interactions.” Ronagh para 231.
Wang doesn’t make predictions.
However, Kale teaches how to produce at least one output about the target machine or system or process, the at least one output being inferred by the converted predetermined machine learning routine upon inputting a data set therein. (Kale para 34 “The system further includes a computing device (e.g., an SSD that includes the memory and a processing device) to predict a maintenance service for one or more of the machines based on an output from an artificial neural network (ANN).”)
Kale, Wang and the claims all use a neural network. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use an ANN to do machine control/maintenance because “the prediction allows the service to be scheduled at a convenient time, for which it is more technically and/or cost efficient to implement the service.” Kale para 29.
Wang teaches claim 2. The apparatus of claim 1, wherein the predetermined machine learning routine is a (Wang uses pre-trained weights W, Wang abs, “tensorizing neural weights into higher-order tensors for better decomposition, and directly mapping efficient tensor structure to neural architecture with nonlinear activation functions…” Wang is not just tensorizing random weights, or weights set to some arbitrary starting value, that means the weights are trained weights. Wang sec. 4.2.2 also says that the DNN is trained on MNIST and UCF11 before the weights are compressed.)
Wang is not explicit about the trained NN.
However, Zniyed teaches tensorizing a trained machine learning routine. (Zniyed abs “This technique fuses the first and zeroth order information of the NN, where the first-order information is contained in a Jacobian tensor, following a constrained canonical polyadic decomposition (CPD). The proposed algorithm can handle different decomposition bases. The goal of this method is to compress large pretrained NN models, by replacing subnetworks, i.e., one or multiple layers of the original network, by a new flexible layer. The approach is applied to a pretrained convolutional neural network (CNN) used for character classification.” (emphasis added))
Zniyed, Wang and the claims all tensorize NNs. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to tensorize an already trained network because the goal of tensorizing is to “compress large pretrained NN models…” Zniyed abs.
Wang teaches claim 3. The apparatus of claim 1…
Wang does not teach gauge optimizations.
However, Tindall teaches a system further configured to add at least one gauge optimization in the converted neural network, the at least one gauge optimization being selected from a predetermined set of gauge optimizations. (Tindall p. 16 “The first of these algorithms we term simple update gauging… The second algorithm, which we term eager gauging...” p. 21 “but any gauging method (simple update gauging, eager gauging, or our new belief propagation gauging) can be used.”)
Tindall, Wang and the claims are all tensorized neural networks. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use gauging on a tensor network because “[e]ffectively compressing and optimizing tensor networks requires reliable methods for fixing the latent degrees of freedom of the tensors, known as the gauge.” Tindall abs.
Wang teaches claim 5. The apparatus of claim 1, further comprising at least one memory adapted to store the neural network and the tensorized neural network, wherein the tensorized neural network occupies less space in the at least one memory than the neural network. (Wang sec. 6 “a novel compression method termed as NTT format for both weight matrices and convolutional kernels. Our NTT can significantly ameliorate the accuracy loss existing widely in traditional TT DNNs…”)
Wang teaches claim 6. The apparatus of claim 1, wherein the converted one or more layers of the plurality of layers of the neural network comprises all layers of the plurality of layers of the neural network. (Wang sec. 3.1 tensorizes all “d layers” in the DNN.)
Wang teaches claims 7 and 13. The apparatus of claim 1, further configured to train the predetermined machine learning routine in the form of the tensorized neural network with a training data set. (Wang sec. 4.2.2 “For MNIST dataset, we follow …to make each image as a sequence of vectors, and design two hidden layers with 625 and 1296 neurons, respectively. Concretely, 625 is … During training, the initial learningrateis0.0001…” MNIST is the training dataset. Wang sec. 4.3.1 “Except training the above 3 CNNs in original, TT, and NTT formats, NTT with residual connections …, have also been examined.” NTT is the tensorizes format.)
Kale teaches claims 8 and 14. The apparatus of claim 1, further configured to obtain at least part of the data set from at least one or more sensors or one or more computing devices or a combination thereof. (Kale abs. “An artificial neural network (ANN) is configured to receive the sensor data stream…”)
Kale teaches claims 9 and 15. The apparatus of claim 1, further configured to provide, at least based on the at least one output, at least one instruction for (Kale abs. “An artificial neural network (ANN) is configured to receive the sensor data stream and predict a maintenance service for the machine based on the sensor data stream.”)
Kale doesn’t teach an actuator.
However, Pfeil teaches actuation of one or more actuators or controllers… (Pfeil para 45 “Control unit 13 controls an actuator as a function of the output variable of deep neural network 12…”)
Kale, Pfeil and the claims are NN applied to industry. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use Kale’s NN to control an actuator so as to implement the maintenance service prescribed by Kale’s NN.
Kale teaches claims 10, 16 and 19. The apparatus of claim 1, wherein the target machine or system or process comprises one of: a computing device or system, a factory line or a machine thereof, a factory, a production process, means of transportation or an automatic control unit thereof, an automatic transportation controlling process, an electric grid or network, an energy power plant, an electric power station, an electric power generation process, and an electrical energy allocation process. (Kale para 25 “The machine may be, for example, a manufacturing machine in an assembly line of several machines (e.g., in a semiconductor wafer fabrication facility), an autonomous vehicle, or systems that provide electrical, chemical, mechanical, data, and/or communication services for a physical structure (e.g., a building or house).”)
Kale teaches claims 11, 17 and 20. The apparatus of claim 1, wherein the at least one output comprises one of: prediction of a failure of a machine, determination of a predictive maintenance of a machine, production amount of energy, production amount of a substance or an object, and actuation of a control unit of means of transportation. (Kale para 25 “The machine may be, for example, a manufacturing machine in an assembly line of several machines (e.g., in a semiconductor wafer fabrication facility), an autonomous vehicle, or systems that provide electrical, chemical, mechanical, data, and/or communication services for a physical structure (e.g., a building or house).” Kale abs. “An artificial neural network (ANN) is configured to receive the sensor data stream and predict a maintenance service for the machine based on the sensor data stream.”)
Conclusion
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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