DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/23/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“an interface for” in claim 9
“optical element” in claim 9
“network component of the artificial neural network for” in claim 9
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112b
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 12-14 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 12 recites limitation “The system of claim 1, wherein the use of the property specific for the spectral and/or temporal phase of the optical signal includes changing a spectral phase profile of the optical signal nonlinearly and linearly to change the property, to execute a nonlinear function of the at least one neuron and a linear weighting of the weight”; however, there is no system of claim 1. Therefore, the claim fail to particularly point out and distinctly claim the subject matter.
Claim 13 recites limitation “The system of claim 1, wherein the output and/or an output signal of the at least one network component is evaluated in that the property of the optical signal specific for the spectral and/or temporal phase of the optical signal is evaluated”; however, there is no system of claim 1. Therefore, the claim fail to particularly point out and distinctly claim the subject matter.
Claim 14 recites limitation “The system of claim 1, wherein the optical signal is provided as an optical input signal, which is specific for an item of input information, and is processed by the at least one network component to obtain an optical output signal specific for the output.”; however, there is no system of claim 1. Therefore, the claim fail to particularly point out and distinctly claim the subject matter.
For examination purposes, Examiner will interpret this is a typo and should be “The system of claim 9”
In reference to dependent claims, dependent claims do not cure the deficiencies noted in the rejection of independent claim 1 and 14. Therefore, these claims are rejected under the same rationale as claim 1 and 14.
Claim Rejections - 35 USC § 112b for Claim interpretation
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 limitation “an interface for”, “optical element”, “network component of the artificial neural network for” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. no association between the structure and the function can be found in the specification. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
For examiner purposes, Examiner will interpret an interface as any device that transmits a signal; an optical element as any device handling signals; a network component as a neural network layer.
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 4 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 4:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 1, wherein the output and/or an output signal of the at least one network component is evaluated in that the property of the optical signal specific for the spectral and/or temporal phase of the optical signal is evaluated” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate the output signal .
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 13:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The system of claim 1, wherein the output and/or an output signal of the at least one network component is evaluated in that the property of the optical signal specific for the spectral and/or temporal phase of the optical signal is evaluated” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate the output signal .
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-7, 9, 11, 13-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhixing Lin et al; “Temporal optical neurons for serial deep learning” published Sep 4, 2020 (hereinafter “Lin”)
Regarding claim 1, Lin anticipates A method for providing an artificial neural network the method comprising: providing an optical signal for the artificial neural network to obtain an output of the artificial neural network by processing of the optical signal by the artificial neural network; (Lin Fig 1; “The objects to be processed are temporal waveforms, which are sampled in amplitude by an optical pulse train (with a constant phase) and then fed into the SONN… C. Schematic of the interconnections between layers. The “weights” are applied across the neurons at each layer through a process of temporal complex-field (e.g., phase) modulation of the incoming temporal sequence.” Examiner notes that an optical signal (temporal waveforms sampled by optical pulse train) for the artificial neural network (Serial optical neural network/SONN) to obtain an output of the artificial neural network (output layer) by processing the optical signal by the artificial neural network (Schematic C shows the process flow of processing the incoming temporal sequence))
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using a property of the optical signal, which is specific for a spectral phase and/or a temporal phase of the optical signal, to provide at least one network component of the artificial neural network for the processing, (Lin Fig 2 and Page 5 Paragraph 2; “Figure 2a shows the model flowsheet of the proposed SONN and related functions that are critical to construct the model. In the forward propagation, every layer contains the processes of temporal phase modulation and dispersive propagation.” Examiner notes that a property of the optical signal (input waveform), which is specific for a temporal phase of the optical signal (temporal phase modulation means temporal phase is used), is used to provide at least one network component (layer) of the artificial neural network (SONN) for processing (forward propagation))
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wherein the at least one network component comprises at least one neuron and/or one weight of the artificial neural network. (Lin Fig 1; “The “weights” are applied across the neurons at each layer through a process of temporal complex-field (e.g., phase) modulation of the incoming temporal sequence.” Examiner notes that network component (neural network layer) comprises a neuron with a weight applied to it)
Regarding claim 2, Lin anticipates The method of claim 1, wherein the provision of the optical signal includes transmission of a light signal into the network component (Lin Fig 1 and Page 3 Paragraph 1; “The input of this SONN is a continuous sequence of short optical pulses modulated by the input pattern (or object) under analysis.” Examiner notes that providing/transmission of an optical signal (short optical pulses) is a light signal)
wherein the light signal is a carrier of an item of information which is processed by the processing of the artificial neural network, to obtain an assessment of the information as the output (Lin Page 5 Paragraph 2; “After propagation through several layers, the final measured output waveform together with the target (ideal) output waveform are used to calculate the corresponding mean square error (MSE) as the cost function.” Lin Page 9 Paragraph 1; “Figures 5a and 5b show the input waveforms corresponding to the English letters of ‘u’ & ‘c’ and ‘a’ & ‘s’, respectively.” Examiner notes that the light signal (input waveform) is a carrier of an item of information (corresponding to the English letters) which is processed by the processing of the artificial neural network (propagation through several layers), to obtain an assessment of the information as the output (calculate the corresponding mean square error))
Regarding claim 4, Lin anticipates The method of claim 1, wherein the output and/or an output signal of the at least one network component is evaluated in that the property of the optical signal specific for the spectral and/or temporal phase of the optical signal is evaluated (Lin Fig 2 and Lin Page 5 Paragraph 2; “After propagation through several layers, the final measured output waveform together with the target (ideal) output waveform are used to calculate the corresponding mean square error (MSE) as the cost function.” Examiner notes that the output signal (output waveform) of the at least one network component (Layer 4) is evaluated in that the property of the optical signal for the temporal phase of the optical signal (phase modulation) is evaluated (Cost function))
Regarding claim 5, Lin anticipates The method of claim 1, wherein the optical signal is provided as an optical input signal, which is specific for an item of input information, and is processed by the at least one network component to obtain an optical output signal specific for the output (Lin Fig 1 and Lin Page 9 Paragraph 1; “Figures 5a and 5b show the input waveforms corresponding to the English letters of ‘u’ & ‘c’ and ‘a’ & ‘s’, respectively.” Examiner notes that the optical signal is provided as an optical input signal (sampled pulses) and is processed by the at least one network component (Layers within Serial optical neural network) to obtain an optical output signal specific for the output (Pred 1-4 is output layer); which is specific for an item of input information (input signals represent/correspond to English letters))
Regarding claim 6, Lin anticipates The method of claim 1, wherein the plurality of network components including the at least one network component are provided, wherein the plurality of network components which comprise neurons and/or weights, which are provided with one another in different levels of the artificial neural network and are optically connected to one another. (Lin Fig 1; “The “weights” are applied across the neurons at each layer through a process of temporal complex-field (e.g., phase) modulation of the incoming temporal sequence. The blue dash lines represent the trained phase applied to the neurons/pulses. The optical spectral components (arrows with different colors) of each pulse/neuron in layer j will be retarded or advanced with respect to each other, and then added coherently with the components of other neurons to form the new neuronal pattern in layer j+1.” Examiner notes that the plurality of components including the at least one network component are provided (Serial optical neural network contains Layers), wherein the plurality of network components which comprise neurons (temporal optical neuron) and weights, which are provided with one another in different levels of the artificial neural network (each layer contains neurons and weights) and are optically connected to one another (connected via optical spectral components))
Regarding claim 7, Lin anticipates The method of claim 1, wherein the property specific for the spectral and/or temporal phase of the optical signal is the spectral and/or temporal phase itself or a group delay or a group delay dispersion or a group velocity dispersion or a third order or higher order dispersion of the derivative of the spectral phase. (Lin Page 3 Paragraph 2; “In the SONN shown in Fig. 1c, the neurons of consecutive layers are connected using group-velocity dispersion. As it is well known, dispersion retards or advances the different spectral components of a propagating signal to different time slots.” Examiner notes that the property specific for the spectral phase of the optical signal is the group velocity dispersion)
Regarding claim 9, Lin anticipates A system for providing an artificial neural network, the system comprising: an interface for providing an optical signal for the artificial neural network to obtain an output of the artificial neural network by processing of the optical signal by the artificial neural network; (Lin Fig 1; “The objects to be processed are temporal waveforms, which are sampled in amplitude by an optical pulse train (with a constant phase) and then fed into the SONN… C. Schematic of the interconnections between layers. The “weights” are applied across the neurons at each layer through a process of temporal complex-field (e.g., phase) modulation of the incoming temporal sequence.” Lin Page 17 Paragraph 1; “The light source we use in this ONN strategy is a pulse train emitted by an actively mode-locked laser” Examiner notes that an interface (actively mode-locked laser) for providing optical signal (temporal waveforms sampled by optical pulse train) for the artificial neural network (Serial optical neural network/SONN) to obtain an output of the artificial neural network (output layer) by processing the optical signal by the artificial neural network (Schematic C shows the process flow of processing the incoming temporal sequence))
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At least one optical element for a use of a property of the optical signal, which is specific for a spectral phase and/or a temporal phase of the optical signal, to provide at least one network component of the artificial neural network for the processing, (Lin Fig 2 and Page 5 Paragraph 2; “Figure 2a shows the model flowsheet of the proposed SONN and related functions that are critical to construct the model. In the forward propagation, every layer contains the processes of temporal phase modulation and dispersive propagation.” Examiner notes that a property of the optical signal (input waveform), which is specific for a temporal phase of the optical signal (temporal phase modulation means temporal phase is used), is used to provide at least one network component (layer) of the artificial neural network (SONN) for processing (forward propagation))
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wherein the at least one network component comprises at least one neuron and/or one weight of the artificial neural network. (Lin Fig 1; “The “weights” are applied across the neurons at each layer through a process of temporal complex-field (e.g., phase) modulation of the incoming temporal sequence.” Examiner notes that network component (neural network layer) comprises a neuron with a weight applied to it)
Regarding claim 11, Lin anticipates The system of claim 9, wherein the provision of the optical signal includes transmission of a light signal into the network component (Lin Fig 1 and Page 3 Paragraph 1; “The input of this SONN is a continuous sequence of short optical pulses modulated by the input pattern (or object) under analysis.” Examiner notes that providing/transmission of an optical signal (short optical pulses) is a light signal)
wherein the light signal is a carrier of an item of information which is processed by the processing of the artificial neural network, to obtain an assessment of the information as the output (Lin Page 5 Paragraph 2; “After propagation through several layers, the final measured output waveform together with the target (ideal) output waveform are used to calculate the corresponding mean square error (MSE) as the cost function.” Lin Page 9 Paragraph 1; “Figures 5a and 5b show the input waveforms corresponding to the English letters of ‘u’ & ‘c’ and ‘a’ & ‘s’, respectively.” Examiner notes that the light signal (input waveform) is a carrier of an item of information (corresponding to the English letters) which is processed by the processing of the artificial neural network (propagation through several layers), to obtain an assessment of the information as the output (calculate the corresponding mean square error))
Regarding claim 13, Lin anticipates The system of claim 1, wherein the output and/or an output signal of the at least one network component is evaluated in that the property of the optical signal specific for the spectral and/or temporal phase of the optical signal is evaluated (Lin Fig 2 and Lin Page 5 Paragraph 2; “After propagation through several layers, the final measured output waveform together with the target (ideal) output waveform are used to calculate the corresponding mean square error (MSE) as the cost function.” Examiner notes that the output signal (output waveform) of the at least one network component (Layer 4) is evaluated in that the property of the optical signal for the temporal phase of the optical signal (phase modulation) is evaluated (Cost function))
Regarding claim 14, Lin anticipates The system of claim 1, wherein the optical signal is provided as an optical input signal, which is specific for an item of input information, and is processed by the at least one network component to obtain an optical output signal specific for the output (Lin Fig 1 and Lin Page 9 Paragraph 1; “Figures 5a and 5b show the input waveforms corresponding to the English letters of ‘u’ & ‘c’ and ‘a’ & ‘s’, respectively.” Examiner notes that the optical signal is provided as an optical input signal (sampled pulses) and is processed by the at least one network component (Layers within Serial optical neural network) to obtain an optical output signal specific for the output (Pred 1-4 is output layer); which is specific for an item of input information (input signals represent/correspond to English letters))
Regarding claim 15, Lin anticipates The system of claim 9, wherein the plurality of network components including the at least one network component are provided, wherein the plurality of network components which comprise neurons and/or weights, which are provided with one another in different levels of the artificial neural network and are optically connected to one another. (Lin Fig 1; “The “weights” are applied across the neurons at each layer through a process of temporal complex-field (e.g., phase) modulation of the incoming temporal sequence. The blue dash lines represent the trained phase applied to the neurons/pulses. The optical spectral components (arrows with different colors) of each pulse/neuron in layer j will be retarded or advanced with respect to each other, and then added coherently with the components of other neurons to form the new neuronal pattern in layer j+1.” Examiner notes that the plurality of components including the at least one network component are provided (Serial optical neural network contains Layers), wherein the plurality of network components which comprise neurons (temporal optical neuron) and weights, which are provided with one another in different levels of the artificial neural network (each layer contains neurons and weights) and are optically connected to one another (connected via optical spectral components))
Regarding claim 16, Lin anticipates The system of claim 9, wherein the property specific for the spectral and/or temporal phase of the optical signal is the spectral and/or temporal phase itself or a group delay or a group delay dispersion or a group velocity dispersion or a third order or higher order dispersion of the derivative of the spectral phase. (Lin Page 3 Paragraph 2; “In the SONN shown in Fig. 1c, the neurons of consecutive layers are connected using group-velocity dispersion. As it is well known, dispersion retards or advances the different spectral components of a propagating signal to different time slots.” Examiner notes that the property specific for the spectral phase of the optical signal is the group velocity dispersion)
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.
Claim(s) 3, 10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Zhixing Lin et al; “Temporal optical neurons for serial deep learning” published Sep 4, 2020 (hereinafter “Lin”) in view of FOSCO; “Nonlinear Techniques and Devices in Optical Signal Processing” available online Sep 23, 2020 (hereinafter “Fosco”)
Regarding claim 3, Lin teaches The method of claim 1, wherein the use of the property specific for the spectral and/or temporal phase of the optical signal includes changing a spectral phase profile of the optical signal nonlinearly and linearly to change the property, to execute a nonlinear function of the at least one neuron and a linear weighting of the weight (Lin page 2 Paragraph 3; “sequential set of temporal neurons that are subsequently interconnected through group-velocity dispersion, e.g., easily implemented by linear propagation through a section of optical fiber. In this way, the optical neurons are distributed and interconnected with each other along a single channel, namely, the time domain. The desired ‘weights’ on the different neurons are realized by imposing a prescribed modulation on the coded pulses along the time domain, e.g., through widely available temporal modulation.” Lin Page 10 Paragraph 1; “the proposed SONN is ideally suited to the realization of activation functions as all neurons pass through the same single optical fiber. Thus, only one nonlinear activation device would be required for each neural network layer.” Lin Page 20 Paragraph 2; “the phase profile is shifted by half of the phase period to prevent the pulses from meeting with the phase jump points in the even-numbered layers.” Examiner notes that both properties for the spectral and temporal phase of the optical signal is used; including changing a spectral phase profile of the optical signal (phase modulation) nonlinearly (group-velocity dispersion) and linearly (shifted by half of the phase period) to change the property, to execute a nonlinear function of the at least one neuron (activation function) and a linear weighting of the weight (linear propagation to realize weights))
Lin does not teach changing a spectral phase profile of the optical signal nonlinearly
However, Fosco does teach changing a spectral phase profile of the optical signal nonlinearly (Fosco Paragraph 1; “An all-optical approach would simply send the channel to a nonlinear optical device (called the wavelength converter) that changes the carrier wavelength without affecting its data contents. Another example is provided by optical regenerators that clean u pan optical signal and amplify it without any optical to electrical conversion. This tutorial focuses on a variety of signal processing devices that make use of the same nonlinear effects, such as self-phase modulation (SPM), cross-phase modulation (XPM), and four-wave mixing (FWM), that are otherwise harmful for Lightwave systems.” Examiner notes that this tutorial shows changing spectral phase profile of the optical signal nonlinearly)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Lin and Fosco. Lin teaches a serial optical deep learning concept that is specifically designed to directly process high-speed temporal data. Fosco teaches nonlinear techniques for optical signal processing. One of ordinary skill would have motivation to combine Lin and Fosco to implement nonlinear effects to enhance the nonlinear parameter y “All three can be implemented using a piece of optical fiber designed to enhance the nonlinear effects. Such fibers are known as highly nonlinear fibers and are designed such that the effective area of the fundamental mode is reduced considerably compared to that of standard fibers. As a result, the nonlinear parameter γ, defined to scale inversely with the effective mode area Aeff, is enhanced considerably. Its value typically exceeds 10 W-1/km for silica-based highly nonlinear fibers and becomes even larger than 1000 W-1/km for specially designed non-silica fibers.” (Fosco Paragraph 2).
Regarding claim 10, Lin teaches The system of claim 9, further comprising a plurality of optical elements including the at least one optical element, wherein the plurality of optical elements are each designed to change a spectral phase profile of the optical signal [at least nonlinearly] for the use of the property, (Lin Fig 2 and Page 3 Paragraph 2; “In the SONN shown in Fig. 1c, the neurons of consecutive layers are connected using group-velocity dispersion. As it is well known, dispersion retards or advances the different spectral components of a propagating signal to different time slots.” Lin Page 18 Paragraph 2; “After the phase modulation, the wave will go through a dispersive medium with a specific dispersion value.” Examiner notes that a plurality of optical elements including the at least one optical element (each layer has a dispersive medium), wherein the plurality of optical elements are each designed to change a spectral phase profile of the optical signal for the use of the property (group-velocity dispersion is performed with dispersive medium))
to provide an output signal of a neuron of the artificial neural network in each case, at least one spectral combiner of the artificial neural network provided to spectrally combine output signals of the neurons, (Lin Fig 1 and Page 4 Paragraph 1; “the connections among neurons are produced through coherent addition of the temporally dispersed optical spectral components of the phase-modulated neurons of the former layer” Examiner notes that a spectral combiner (optical spectral components) of the artificial neural network provided to spectrally combine output signals of the neurons (to produce connections among neurons); Fig 1.C shows the flow of output signals of neurons between each layer of the artificial neural network)
and a spectral phase analyzer provided to evaluate a phase of the combined output signal for the provision of the output (Lin Page 5 Paragraph 2; “After propagation through several layers, the final measured output waveform together with the target (ideal) output waveform are used to calculate the corresponding mean square error (MSE) as the cost function.” Examiner notes that the calculating the MSE from the output waveform is a spectral phase analyzer provided to evaluate a phase of the combined output signal for the provisional of the output)
Lin does not teach change a spectral phase profile of the optical signal at least nonlinearly
However, Fosco does teach change a spectral phase profile of the optical signal at least nonlinearly (Fosco Paragraph 1; “An all-optical approach would simply send the channel to a nonlinear optical device (called the wavelength converter) that changes the carrier wavelength without affecting its data contents. Another example is provided by optical regenerators that clean u pan optical signal and amplify it without any optical to electrical conversion. This tutorial focuses on a variety of signal processing devices that make use of the same nonlinear effects, such as self-phase modulation (SPM), cross-phase modulation (XPM), and four-wave mixing (FWM), that are otherwise harmful for Lightwave systems.” Examiner notes that this tutorial shows changing spectral phase profile of the optical signal nonlinearly)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Lin and Fosco. Lin teaches a serial optical deep learning concept that is specifically designed to directly process high-speed temporal data. Fosco teaches nonlinear techniques for optical signal processing. One of ordinary skill would have motivation to combine Lin and Fosco to implement nonlinear effects to enhance the nonlinear parameter y “All three can be implemented using a piece of optical fiber designed to enhance the nonlinear effects. Such fibers are known as highly nonlinear fibers and are designed such that the effective area of the fundamental mode is reduced considerably compared to that of standard fibers. As a result, the nonlinear parameter γ, defined to scale inversely with the effective mode area Aeff, is enhanced considerably. Its value typically exceeds 10 W-1/km for silica-based highly nonlinear fibers and becomes even larger than 1000 W-1/km for specially designed non-silica fibers.” (Fosco Paragraph 2).
Regarding claim 12, Lin teaches The system of claim 1, wherein the use of the property specific for the spectral and/or temporal phase of the optical signal includes changing a spectral phase profile of the optical signal [nonlinearly and] linearly to change the property, to execute a nonlinear function of the at least one neuron and a linear weighting of the weight (Lin page 2 Paragraph 3; “sequential set of temporal neurons that are subsequently interconnected through group-velocity dispersion, e.g., easily implemented by linear propagation through a section of optical fiber. In this way, the optical neurons are distributed and interconnected with each other along a single channel, namely, the time domain. The desired ‘weights’ on the different neurons are realized by imposing a prescribed modulation on the coded pulses along the time domain, e.g., through widely available temporal modulation.” Lin Page 10 Paragraph 1; “the proposed SONN is ideally suited to the realization of activation functions as all neurons pass through the same single optical fiber. Thus, only one nonlinear activation device would be required for each neural network layer.” Lin Page 20 Paragraph 2; “the phase profile is shifted by half of the phase period to prevent the pulses from meeting with the phase jump points in the even-numbered layers.” Examiner notes that both properties for the spectral and temporal phase of the optical signal is used; including changing a spectral phase profile of the optical signal (phase modulation) linearly (shifted by half of the phase period) to change the property, to execute a nonlinear function of the at least one neuron (activation function) and a linear weighting of the weight (linear propagation to realize weights))
Lin does not teach changing a spectral phase profile of the optical signal nonlinearly
However, Fosco does teach changing a spectral phase profile of the optical signal nonlinearly (Fosco Paragraph 1; “An all-optical approach would simply send the channel to a nonlinear optical device (called the wavelength converter) that changes the carrier wavelength without affecting its data contents. Another example is provided by optical regenerators that clean u pan optical signal and amplify it without any optical to electrical conversion. This tutorial focuses on a variety of signal processing devices that make use of the same nonlinear effects, such as self-phase modulation (SPM), cross-phase modulation (XPM), and four-wave mixing (FWM), that are otherwise harmful for Lightwave systems.” Examiner notes that this tutorial shows changing spectral phase profile of the optical signal nonlinearly)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Lin and Fosco. Lin teaches a serial optical deep learning concept that is specifically designed to directly process high-speed temporal data. Fosco teaches nonlinear techniques for optical signal processing. One of ordinary skill would have motivation to combine Lin and Fosco to implement nonlinear effects to enhance the nonlinear parameter y “All three can be implemented using a piece of optical fiber designed to enhance the nonlinear effects. Such fibers are known as highly nonlinear fibers and are designed such that the effective area of the fundamental mode is reduced considerably compared to that of standard fibers. As a result, the nonlinear parameter γ, defined to scale inversely with the effective mode area Aeff, is enhanced considerably. Its value typically exceeds 10 W-1/km for silica-based highly nonlinear fibers and becomes even larger than 1000 W-1/km for specially designed non-silica fibers.” (Fosco Paragraph 2).
Claim(s) 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhixing Lin et al; “Temporal optical neurons for serial deep learning” published Sep 4, 2020 (hereinafter “Lin”) in view of Mariusz Bojarski et al; “End to End Learning for Self-Driving Cars” published Apr 25, 2016 (hereinafter “Bojarski”)
Regarding claim 8, Lin does not teach The method of claim 1, wherein the artificial neural network is used in a transportation vehicle, wherein the optical signal is as an optical input signal, which is specific for an item of input information about the surroundings of the transportation vehicle, and is processed by the at least one network component to obtain the output as a classification of the input information.
However, Bojarski does teach The method of claim 1, wherein the artificial neural network is used in a transportation vehicle, wherein the optical signal is as an optical input signal, which is specific for an item of input information about the surroundings of the transportation vehicle, and is processed by the at least one network component to obtain the output as a classification of the input information. (Bojarski Figure 7 and Page 7 Paragraph 1; “After a trained network has demonstrated good performance in the simulator, the network is loaded on the DRIVETMPX in our test car and taken out for a road test.” Examiner notes that the artificial neural network (trained network) is used in a transportation vehicle (test car), wherein the optical signal is an optical input signal (camera images sent to the CNN), which is specific for an item of input information about the surroundings of the transportation vehicle (images capture the unpaved road), and is processed by the at least one network component to obtain the output as a classification of the input information (CNN learns useful road features))
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It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Lin and Bojarski. Lin teaches a serial optical deep learning concept that is specifically designed to directly process high-speed temporal data. Bojarski teaches using a CNN in a self-driving car. One of ordinary skill would have motivation to combine Lin and Bojarski to apply a serial optical deep learning network to learn road or lane marking detection, semantic abstraction, path planning, and control for steering a car “We have empirically demonstrated that CNNs are able to learn the entire task of lane and road following without manual decomposition into road or lane marking detection, semantic abstraction, path planning, and control.” (Bojarski Page 9 Paragraph 1).
Regarding claim 17, Lin does not teach The system of claim 9, wherein the artificial neural network is used in a transportation vehicle, wherein the optical signal is as an optical input signal, which is specific for an item of input information about the surroundings of the transportation vehicle, and is processed by the at least one network component to obtain the output as a classification of the input information.
However, Bojarski does teach The system of claim 9, wherein the artificial neural network is used in a transportation vehicle, wherein the optical signal is as an optical input signal, which is specific for an item of input information about the surroundings of the transportation vehicle, and is processed by the at least one network component to obtain the output as a classification of the input information. (Bojarski Figure 7 and Page 7 Paragraph 1; “After a trained network has demonstrated good performance in the simulator, the network is loaded on the DRIVETMPX in our test car and taken out for a road test.” Examiner notes that the artificial neural network (trained network) is used in a transportation vehicle (test car), wherein the optical signal is an optical input signal (camera images sent to the CNN), which is specific for an item of input information about the surroundings of the transportation vehicle (images capture the unpaved road), and is processed by the at least one network component to obtain the output as a classification of the input information (CNN learns useful road features))
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It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Lin and Bojarski. Lin teaches a serial optical deep learning concept that is specifically designed to directly process high-speed temporal data. Bojarski teaches using a CNN in a self-driving car. One of ordinary skill would have motivation to combine Lin and Bojarski to apply a serial optical deep learning network to learn road or lane marking detection, semantic abstraction, path planning, and control for steering a car “We have empirically demonstrated that CNNs are able to learn the entire task of lane and road following without manual decomposition into road or lane marking detection, semantic abstraction, path planning, and control.” (Bojarski Page 9 Paragraph 1).
Conclusion
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/D.D.T./Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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