Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 3 and 9 are objected to because of the following informalities:
The claim 3 recites “IP learning.” The limitation should recite “intrinsic plasticity (IP) learning” as recited in the specification paragraph 0108.
The claim 9 recites “MEMS element” The limitation should recite “Micro-Electro-Mechanical System (MEMS) element” as recited in the specification paragraph 0026.
Appropriate correction is required.
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) are in Claim 1:
The limitation of claim 1 “a control unit that is configured to control the time-varying parameter in accordance with a time series having a cycle corresponding to the number of stages of the nonlinear element and the delay elements.” – As described below the control unit is described to be implemented by a processor as defined in paragraph [0117] and [0118] that can comprise a CPU, GPU, DSP, ASIC a combination of circuits.
[0117] In addition, the functions of any components in any device as described above may be implemented by a processor. For example, each processing in the embodiment may be implemented by a processor that operates based on information such as a program and a computer-readable recording medium that stores information such as the program. Here, for the processor, for example, the function of each unit may be implemented by separate hardware, or the function of each unit may be implemented by integrated hardware. For example, the processor includes hardware, which may include at least one of circuitry that processes digital signals and circuitry that processes analog signals. For example, the processor may be constituted by either one or both of one or a plurality of circuit devices or one or a plurality of circuit elements mounted on a circuit board. An integrated circuit (IC) or the like may be used as the circuit device, and a resistor, a capacitor, or the like may be used as the circuit element.
[0118] Here, the processor may be, for example, a CPU. However, the processor is not limited to a CPU, and various processors such as a graphics processing unit (GPU) or a digital signal processor (DSP) may be used. In addition, the processor may, be, for example, a hardware circuit based on an application specific integrated circuit (ASIC). In addition, the processor may be constituted by, for example, a plurality of CPUs, or may be constituted by a hardware circuit based on a plurality of ASICs. In addition, the processor may be constituted by, for example, a combination of a plurality of CPUs and a hardware circuit based on a plurality of ASICs. The processor may also include, for example, one or more of amplifier circuits, filter circuits, or the like that process analog signals.
Examiner notes that the limitation of claim 1 “time-varying parameter is configured to perform a modulation of a state variable of the nonlinear element or a modulation of a state variable of the delay element which is one before the nonlinear element in the ring-shaped reservoir,” looks to be using functional language but does not use any generic place holder.
Because these claim limitation(s) 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 § 112 (a)
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claim 1-13 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, the limitation “the time-varying parameter is configured to perform a modulation of a state variable of the nonlinear element or a modulation of a state variable of the delay element which is one before the nonlinear element in the ring-shaped reservoir,” lack written description as to how a time-varying parameter is configured to perform a modulation of a state variable for the nonlinear element or the delay element. The specification in paragraph [0036] line 1 “Description is given on the assumption that the node 211 is a node A1.” and [0039] line 7-9 “
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” performing a modulation at node 211 that is node A1 and using a nonlinear function unit 232 to perform the modulation of the state variable. The limitation is interpreted as performing a modulation on node A1 using a nonlinear function. All dependent claims inherit the issue.
Claim Rejections - 35 USC § 112 (b)
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 1-13 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.
Regarding claim 1, the claims recite “the time-varying parameter is configured to perform a modulation of a state variable of the nonlinear element or a modulation of a state variable of the delay element” in line 7-8 of the claim. The limitation is rendered indefinite as it is unclear what is meant by a time-varying parameter configured to perform a modulation. One within the art would not understand what is meant for time-varying parameter to perform a modulation rendering the limitation indefinite. Also the limitation “states a modulation of a state variable of the nonlinear element or a modulation of a state variable of the delay element”, the limitations “a modulation” and “a state variable” are recited twice machining unclear as to what the second modulation and sate variable refer to or if it is the same modulation or if it is referring to the nonlinear modulation function recited in the claim. The specification in paragraph [0036] line 1 “Description is given on the assumption that the node 211 is a node A1.” and [0039] line 7-9 “
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” provided information that recites node 211 is node A1 and that the modulation is performed by nonlinear function unit 232. All dependent claims inherit the issue.
Regarding claim 3, the claims recite “IP learning” in line 2 of the claim. The term is not defined previously in the claims it depends on making the claim indefinite. The specification in paragraph 00108 does provide an abbreviation that refers to IP as “intrinsic plasticity (IP)”. The term is interpreted as meaning intrinsic plasticity (IP) learning.
Regarding claim 6, the claims recite “tanh” in line 2 of the claim. The term is not defined previously in the claims it depends on making the claim indefinite. The specification in paragraph 0045 defines formula (3) using tanh however it does not define what tanh. However it is interpreted to mean (hyperbolic tangent) function.
Regarding claim 7, the claims recite “ReLu” in line 2 of the claim. The term is not defined previously in the claims it depends on making the claim indefinite. The specification in paragraph 0047 defines formula (4) using tanh however it does not define what ReLu. However it is interpreted to mean Rectified Linear Unit (ReLU) function.
Regarding claim 9, the claims recite “MEMS learning” in line 2 of the claim. The term is not defined previously in the claims it depends on making the claim indefinite. The specification in paragraph 0026 does provide an abbreviation that refers to MEMS as “Micro-Electro-Mechanical System (MEMS)”. The term is interpreted as meaning Micro-Electro-Mechanical System (MEMS) element.
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.
Claim(s) 1, 2, 5, 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Okumura et al. (US20180293495A1) (“Okumura”) in view of D. -Z. Yue, Z. -M. Wu, Y. -S. Hou and G. -Q. Xia, "Effects of Some Operation Parameters on the Performance of a Reservoir Computing System Based on a Delay Feedback Semiconductor Laser With Information Injection by Current Modulation," in IEEE Access, vol. 7, pp. 128767-128773, 2019, doi: 10.1109/ACCESS.2019.2938552 (“Yue”).
Regarding claim 1, as best understood given the 112(a) and (b) issue identified above.
Okumura teaches A reservoir element comprising: a ring-shaped reservoir constituted by a single nonlinear element and a plurality of delay elements (Okumura Fig 2.
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[A reservoir element comprising: a ring-shaped reservoir]
Para 0055, FIG. 2 is a diagram illustrating a concept of the reservoir computing according to Example 1.
Para 0057 line 1-3, The reservoir unit 112 is constituted with one nonlinear node 200 accompanying time delay. The reservoir unit 112 may include two or more nonlinear nodes 200.
Para 0063, The data q(t) is transmitted to a delay network constituted with virtual nodes 201. Specifically, a value of each component of the equation (3) is emulated as a state value of the virtual node 201. [a single nonlinear element and a plurality of delay elements]),
wherein the nonlinear element has a nonlinear modulation function, the nonlinear element being controllable by a time-varying parameter capable of dynamically changing the nonlinear modulation function (Okumura para 0057, The reservoir unit 112 is constituted with one nonlinear node 200 accompanying time delay. The reservoir unit 112 may include two or more nonlinear nodes 200. When the input data x(t) is received from the input unit 111, the nonlinear node 200 divides the input data x(t) into pieces of data each of which consists of a piece of data with a time width T and executes computation processing by using the divided piece of data with the time width T as one processing unit.
Para 0058, Here, T represents a delay time (length of a delay network). The divided input data x(t) is handled as an N-dimensional vector. N represents the number of virtual nodes.
Para 0059, In the computation processing, the reservoir unit 112 executes nonlinear transformation illustrated in the data equation (2) to calculate N-dimensional data q(t). Each component of the data q(t) is expressed by the equation (3) [wherein the nonlinear element has a nonlinear modulation function].
Para 0063, The data q(t) is transmitted to a delay network constituted with virtual nodes 201. Specifically, a value of each component of the equation (3) is emulated as a state value of the virtual node 201 [the nonlinear element being controllable by a time-varying parameter capable of dynamically changing the nonlinear modulation function].)),
and the reservoir element includes a control unit that is configured to control the time-varying parameter in accordance with a time series having a cycle corresponding to the number of stages of the nonlinear element and the delay elements (Okumura Para 0117, The reservoir unit 112 includes a computation unit 721, a laser 722, an MZ optical modulator 723, a photodiode 724, and an amplifier 725. The MZ optical modulator 723 and the photodiode 724 are connected via an optical fiber.
[0118] The computation unit 721 executes computation processing expressed by the equation (2). That is, the computation unit 721 superimposes the input data x(t) input from the input unit 111 and the data q(t) output from the reservoir unit 112. The computation unit 721 outputs the computation result as a signal to the MZ optical modulator 723.
Para 0119, The reservoir unit 112 includes a computation unit 721, a laser 722, an MZ optical modulator 723, a photodiode 724, and an amplifier 725. The MZ optical modulator 723 and the photodiode 724 are connected via an optical fiber.
Para 0120, The MZ optical modulator 723 is hardware for implementing the nonlinear node 200. In Example 2, a fiber coupled LN (LiNbO3)-MZ modulator was used. The MZ optical modulator 723 modulates intensity of laser light input from the laser 722 using the signal input from the computation unit 721. Light transmission characteristic of the MZ optical modulator 723 corresponds to a square of a sine wave with respect to an input electric signal and thus, an amplitude is nonlinearly transformed [and the reservoir element includes a control unit that is configured to control the time-varying parameter].
Para 0122, The length of the optical fiber to be connected between the MZ optical modulator 723 and the photodiode 724 is a length required for a predetermined time to transmit laser light output from the MZ optical modulator 723. The time required for transmission of laser light is a period of the delay network. In Example 2, the MZ optical modulator 723 and the photodiode 724 are connected by using an optical fiber having a length of 20 km. Accordingly, it takes 100 microseconds to transmit the signal.
Para 0126, The read circuit 731 reads the signal output from the reservoir unit 112. The read circuit 731 operates so as to be synchronized with the mask circuit 711. An operation speed of the read circuit 731 varies at an amplification factor of 1 MHz and the read circuit 731 operates at a cycle of 10 kHz. The amplification factor is determined by learning processing. The read circuit 731 outputs the read signal to the integration circuit 731 [in accordance with a time series having a cycle corresponding to the number of stages of the nonlinear element and the delay elements]).
Okumura does not explicitly teach - the time-varying parameter is configured to perform a modulation of a state variable of the nonlinear element or a modulation of a state variable of the delay element which is one before the nonlinear element in the ring-shaped reservoir,
However Yue teaches the time-varying parameter is configured to perform a modulation of a state variable of the nonlinear element or a modulation of a state variable of the delay element which is one before the nonlinear element in the ring-shaped reservoir (Yue et al.
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(Page 2 II. Experimental Setup line 1-10, Figure 1 shows our experimental setup. A distributed feedback semiconductor laser (DFB-SL) is taken as the nonlinear node of the reservoir, whose bias current and temperature is controlled by a laser controller (LC, ILX-Lightwave, LDC-3724C) with an accuracy of 0.01 mA and 0.01_C, respectively. The modulation signal is generated by an arbitrary waveform generator (AWG, Tektronix, AWG70001A, 1.5KSa/s-50GSa/s) with a 50 Ω output impedance. A Bias Tee (Picosecond, 5541A, 80 KHz-26 GHz) is used to combine the bias current and the modulation signal.)
Page 128768 II. Experimental Setup para 2, In this experiment, data pre-processing (masking in input layer) and post-processing (training and testing in output layer) are performed offline. Figure 2 is a schematic diagram to clearly illustrate the operating principle of the RC. As shown in the diagram, before original data u(k) is fed into the reservoir, it is sampled with a period T (we set T = τ , where τ is the delay time in feedback loop) and multiplied by a mask M(t). The mask defines the coupling weights between input data and virtual nodes, it is a piecewise constant function with a period T and keeps a constant over an interval θ. To maximize the diversity of virtual node states, the mask is usually constituted by random sequences such as binary mask [25], [26], multi-level mask [27], and chaotic mask [28]. The mask used in this experiment is a binary mask, where the mask values are randomly extracted from {0.1, 1} with equal probability. The masked data Im(t), i.e. the signal output by AWG, can be regarded as a continuous time signal which keeps a constant over θ [9], and it is fed into the reservoir serially to modulate the SL current. Under current modulation and optical feedback, the SL presents nonlinear transient responses. The SL responses at different time are sampled and recorded for post-processing. In post-processing procedure, we define N virtual nodes distributed in a period τ with an interval θ. Then, N states of the virtual nodes can be collected within τ, and the state matrix
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of the reservoir is obtained after M successive τ [the time-varying parameter is configured to perform a modulation of a state variable of the nonlinear element]),
Okumura and Yue are considered to be analogous to the claim invention because they are in the same field of reservoir computing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Okumura to incorporate the teachings of Yue and disclose a modulation on the state variable. Doing so to modulate the SL to find optimal parameter settings to realize a good prediction performance(Yue IV. Conclusion line 1-10, In summary, we experimentally investigate the influences of some key operation parameters on the performance of a RC system based on an optical feedback SL with information injection by current modulation. Via a Santa Fe time series prediction task, the influences of the modulation index, the feedback ratio and the SL bias current on the RC performance are assessed in detail. The relation between the system rest state and the RC performance is specified, and the optimal parameter settings are confirmed for realizing a good prediction performance.).
Regarding claim 2, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 1.
Okumura further teaches wherein the time- varying parameter is set by the time series which is a value variable per unit time (Okumura para 0071 In the following description, the stream N(t) in one section is described as a stream [AYit). As illustrated in FIG. 4C, the stream [AYit) has a constant value in one section.
Para 0072, Next, the input unit 111 executes mask processing for modulating intensity for each stream [AYit) every time width i:M to calculate an input stream af(t) (step S104). For example, the input stream af(t) as illustrated in FIG. 4D is obtained. In Example 1, intensity modulation is performed in the range from -1 to +1. Here, τM represents a distance between the virtual nodes and satisfies the equation (6).
Para 0076, Next, the input unit 111 executes time shift processing of generating deviation in time based on the counter value m to transform the input stream af(t) into an input stream d(t) (step S105). Thereafter, the input unit 111 proceeds to step S107.
Para 0084, Next, the input unit 111 inputs the input data x(t) to the nonlinear node 200 of the reservoir unit 112 (step S108). Thereafter, the input unit 111 ends processing [by the time series which is a value variable per unit time]).
Regarding claim 5, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 1.
Okumura teaches wherein the nonlinear modulation function is used to perform modulation using a sigmoid as a nonlinear function (Okumura para 0004, As an example of transformation of the hidden layer, there is nonlinear transformation imitating firing phenomenon of a neuron. The firing phenomenon of neuron is known as a nonlinear phenomenon in which a membrane potential rapidly rises and output varies in a case where a potential exceeding a threshold value is input to the neuron. In order to reproduce the phenomenon described above, for example, a sigmoid function expressed by the equation (1) is used.
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Para 0059, In the computation processing, the reservoir unit 112 executes nonlinear transformation illustrated in the data equation (2) to calculate N-dimensional data q(t). Each component of the data q(t) is expressed by the equation (3) [wherein the nonlinear modulation function is used to perform modulation].
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[using a sigmoid as a nonlinear function]).
Regarding claim 8, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 5.
Okumura teaches wherein the nonlinear modulation function is used to perform modulation using an arbitrary function combined with the nonlinear function (Okumura Para 0073, The modulation may be either amplitude modulation or phase modulation. Specific modulation is performed by multiplying the stream N(t) by a random bit sequence.
para 0077, The time shift processing may be processing of delaying the time or processing of advancing the time. For example, time shift processing represented by the equation (8) is performed.
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Para 0078 line 1-3, The equation (8) is time shift processing that gives a delay to another input stream af(t) by using an arbitrary input stream af(t) as a reference.
Para 0015, The shift register 712 executes computation processing corresponding to processing of step S105 for the input stream af(t). The shift register 712 outputs the calculated input stream d(t) to the computation unit 713. In Example 2, a delay circuit for generating delay in the input stream af(t) using the shift register 712 is implemented. However, the delay circuit may be a delay circuit constituted with a ladder type transmission circuit network constituted with a capacitor and an inductor
Para 0116 The computation unit 713 executes computation processing corresponding to processing of step S107 using the input stream a/t) input from each shift register 712. The computation unit 713 outputs a computation result to the reservoir unit 112 [using an arbitrary function].
Para 0118 The computation unit 721 executes computation processing expressed by the equation (2). That is, the computation unit 721 superimposes the input data x(t) input from the input unit 111 and the data q(t) output from the reservoir unit 112. The computation unit 721 outputs the computation result as a signal to the MZ optical modulator 723. [wherein the nonlinear modulation function is used to perform modulation] (i.e. in combination with the nonlinear function)).
Regarding claim 10, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 1.
Okumura teaches wherein the nonlinear element is constituted by an optical modulation element (Okumura Para 0117, The reservoir unit 112 includes a computation unit 721, a laser 722, an MZ optical modulator 723 [an optical modulation element], a photodiode 724, and an amplifier 725. The MZ optical modulator 723 and the photodiode 724 are connected via an optical fiber.)).
Claim(s) 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Okumura in view of Yue and further in view of Benjamin Schrauwen, Marion Wardermann, David Verstraeten, Jochen J. Steil Dirk Stroobandt, Improving reservoirs using intrinsic plasticity, Neurocomputing, Volume 71, Issues 7–9, 2008, Pages 1159-1171, ISSN 0925-2312 (“Schrauwen”).
Regarding claim 3, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 1.
Okumura and Yue are combine in the same rational as set forth above with respect to claim 1.
Okumura does not explicitly teach wherein the time-varying parameter is set by the time series determined by IP learning.
However Schrauwen teaches wherein the time-varying parameter is set by the time series determined by IP learning (Schrauwen Page 1160 1. Introduction para 6, The ESN consists of a randomly connected recurrent network of analog neurons—the reservoir—that is driven by a (one- or multi-dimensional) temporal input signal. On the output level, the activations of the entire network are treated as high-dimensional spatio-temporal input features for a linear classification/regression algorithm—the linear readout. The ESN was introduced as an improved way to use the computational power of RNNs without training of the internal weights. From a formal viewpoint, the reservoir acts as a complex non-linear dynamic filter that transforms the input signals using a high-dimensional temporal mapping, not unlike the operation of an explicit, temporal kernel function. It is even possible to solve several classification tasks on a single input signal simultaneously by adding multiple readouts to one reservoir.
page 1161, The original work of Triesch assumes a constraint on the mean of the output distribution and derives a gradient descent learning rule from these principles for fermi nonlinearities. In this work, we extend the formalism and study the effects on two types of transfer functions:
Page 162 2.1 Effect of bounded activation values,
We now want to evaluate whether the IP learning rules are able to converge to the desired output distributions. There is however a problem due to the bounded nature of the output of the neurons we use: they are unable to output arbitrary values. The non-linearities we consider in this contribution are bounded to [0,1] for fermi and [-1,1] for tanh. Due to these bounds, when we impose a certain constraint on the moments of the infinite distribution, these moments will not be accurately approximated by a learning rule controlling a neuron with finite output range. The effects of these bounds on the moments of the actual output distribution can be computed as follows
Page 1165 3.1 Experimental Setting para 5, For pre-training a reservoir, the IP rule is applied with a learning rate of 0.0005 for 100 000 time steps (equal to 10 epochs when we use 10 time series, used for the cross validation, each consisting of 1000 time steps). To check whether IP has had sufficient time to adapt after this time, we verified that a and b had converged to small regions and compared the expected probability density with the one estimated from the reservoir’s output [wherein the time-varying parameter is set by the time series determined by IP learning]).
Okumura and Schrauwen are considered to be analogous to the claim invention because they are in the same field of reservoir computing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Okumura to incorporate the teachings of Schrauwen use IP learning. Doing to make reservoir computing more robust in respect to weights or input scaling (Schrauwen Abstract line 6-12, The IP rule is evaluated in a reservoir computing setting, which is a temporal processing technique which uses random, untrained recurrent networks as excitable media, where the network’s state is fed to a linear regressor used to calculate the desired output. We present an experimental comparison of the different IP rules on three benchmark tasks with different characteristics. Furthermore, we show that this unsupervised reservoir adaptation is able to adapt networks with very constrained topologies, such as a 1D lattice which generally shows quite unsuitable dynamic behavior, to a reservoir that can be used to solve complex tasks. We clearly demonstrate that IP is able to make reservoir computing more robust: the internal dynamics can autonomously tune themselves— irrespective of initial weights or input scaling—to the dynamic regime which is optimal for a given task.).
Regarding claim 4, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 1.
Okumura and Yue are combine in the same rational as set forth above with respect to claim 1.
Okumura and Schrauwen are combine in the same rational as set forth above with respect to claim 3.
Schrauwen further teaches wherein the time-varying parameter is set by the time series determined by a logarithmic normal distribution (Page 1160 1. Introduction para 6, The ESN consists of a randomly connected recurrent network of analog neurons—the reservoir—that is driven by a (one- or multi-dimensional) temporal input signal. On the output level, the activations of the entire network are treated as high-dimensional spatio-temporal input features for a linear classification/regression algorithm—the linear readout. The ESN was introduced as an improved way to use the computational power of RNNs without training of the internal weights. From a formal viewpoint, the reservoir acts as a complex non-linear dynamic filter that transforms the input signals using a high-dimensional temporal mapping, not unlike the operation of an explicit, temporal kernel function. It is even possible to solve several classification tasks on a single input signal simultaneously by adding multiple readouts to one reservoir.
Page 1161, 2. Derivation of generalize IP Para 3 line 6-16 The ME distribution for a given mean (first moment) and support in the interval [0,∞] is the exponential distribution. Likewise, the ME distribution for a given mean and standard deviation with support in [-∞,∞] is the Gaussian. Therefore, to target ME output distribution in formal neurons, we use for the first moment case fermi neurons having a positive output range [0,1] and for the second order case tanh neurons with output range [-1,1]. We express the amount the empirical output distribution differs from the desired ME distribution using the Kullback–Leibler divergence:
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[ by a logarithmic normal distribution]).
Claim(s) 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Okumura in view of Yue and further in view of Kudithipudi D, Saleh Q, Merkel C, Thesing J, Wysocki B. Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing. Front Neurosci. 2016 Feb 1;9:502. doi: 10.3389/fnins.2015.00502 (“Kudithipudi”).
Regarding claim 6, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 1.
Okumura and Yue are combine in the same rational as set forth above with respect to claim 1.
Okumura does not explicitly teach wherein the nonlinear modulation function is used to perform modulation using tanh as a nonlinear function.
However Kudithipudi teaches wherein the nonlinear modulation function is used to perform modulation using tanh as a nonlinear function (Kudithipudi page 7-8,
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[using tanh as a nonlinear function]
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[wherein the nonlinear modulation function is used to perform modulation]).
Okumura and Kudithipudi are considered to be analogous to the claim invention because they are in the same field of reservoir computing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Okumura to incorporate the teachings of Kudithipudi to use a nonlinear modulation using tanh function. Doing to have richer reservoir dynamics leading to better classification accuracies in the ESNs (Kudithpudi page 7 5.2 Reservoir Neuron Circuits para 1 line 8-10, In this work, we’ve found that tanh activation functions also result in richer reservoir dynamics leading to better classification accuracies in ESNs.).
Regarding claim 12, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 1.
Okumura and Yue are combine in the same rational as set forth above with respect to claim 6.
Okumura and Kudithipudi are combine in the same rational as set forth above with respect to claim 6.
Okumura further teaches wherein the nonlinear modulation function is used to perform modulation using an arbitrary function combined with the nonlinear function (Okumura Para 0073, The modulation may be either amplitude modulation or phase modulation. Specific modulation is performed by multiplying the stream N(t) by a random bit sequence.
para 0077, The time shift processing may be processing of delaying the time or processing of advancing the time. For example, time shift processing represented by the equation (8) is performed.
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Para 0078 line 1-3, The equation (8) is time shift processing that gives a delay to another input stream af(t) by using an arbitrary input stream af(t) as a reference.
Para 0015, The shift register 712 executes computation processing corresponding to processing of step S105 for the input stream af(t). The shift register 712 outputs the calculated input stream d(t) to the computation unit 713. In Example 2, a delay circuit for generating delay in the input stream af(t) using the shift register 712 is implemented. However, the delay circuit may be a delay circuit constituted with a ladder type transmission circuit network constituted with a capacitor and an inductor
Para 0116 The computation unit 713 executes computation processing corresponding to processing of step S107 using the input stream a/t) input from each shift register 712. The computation unit 713 outputs a computation result to the reservoir unit 112 [using an arbitrary function].
Para 0118 The computation unit 721 executes computation processing expressed by the equation (2). That is, the computation unit 721 superimposes the input data x(t) input from the input unit 111 and the data q(t) output from the reservoir unit 112. The computation unit 721 outputs the computation result as a signal to the MZ optical modulator 723. [wherein the nonlinear modulation function is used to perform modulation] (i.e. in combination with the nonlinear function)).
Claim(s) 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Okumura in view of Yue and further in view of Manneschi, L., A. C. Lin, and E. Vasilaki. "SpaRCe: sparse reservoir computing." arXiv:1912.08124v1 [cs.NE] 4 Dec 2019 (2019) (“Maneschi”).
Regarding claim 7, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 1.
Okumura and Yue are combine in the same rational as set forth above with respect to claim 1.
Okumura does not teach wherein the nonlinear modulation function is used to perform modulation using ReLu as a nonlinear function.
Manneschi teaches wherein the nonlinear modulation function is used to perform modulation using ReLu as a nonlinear function (Manneschi Page 2 1 Introduction para 3 line 16-29, Analogously to the concept of firing thresholds, SpaRCe exploits learnable thresholds to optimize the level of sparsity inside the network. Both the learnable thresholds and the read-out weights (but not the recurrent connections within the reservoir) are optimised by minimising an error function without exploiting any normalization term. We analysed the learning rule derived from this error minimization and found that learning occurs by two antagonist factors: the first raises the thresholds proportionally to the correlated activity of the nodes (thus silencing nodes that are correlated and therefore redundant), while the second lowers the thresholds of nodes that contribute to the correct classification (Fig. 3).
Page 3 2.1 SpaRCe, Let us consider the mean square cost function,
given by
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[ReLu as a nonlinear function.]
Page 7 3.1 Odor Sequence Learning para 4 line 16-26, It is clear that there is an optimal sparsity level of about 50% where the error is minimized for all the training instance. Furthermore, the change in the threshold values obtained through the learning rule is highlighted by the black dashed lines that connect dots of training instances from the top to the bottom of the graph. All these lines tend approximately toward the optimal representation, showing how the learning rule appropriately modulates the percentage of active nodes [wherein the nonlinear modulation function is used to perform modulation]).
Okumura and Manneschi are considered to be analogous to the claim invention because they are in the same field of reservoir computing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Okumura to incorporate the teachings of Manneschi to use a nonlinear modulation using relu function. Doing to use thresholds learned by error functions on the outputs of the network (Maneschi Abstract line 14-28, This approach, which we term SpaRCe, optimizes the sparseness level of the reservoir and applies the threshold mechanism to the information received by the read-out weights. Both the read-out weights and the thresholds are learned by a standard online gradient rule that minimises an error function on the outputs of the network. Threshold learning occurs by the balance of two opposing forces: reducing inter-neuronal correlations in the reservoir by deactivating redundant neurons, while increasing the activity of neurons participating in correct decisions. We test SpaRCe in a set of classification problems and find that introducing threshold learning improves performance compared to standard reservoir computing networks.).
Regarding claim 13, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 7.
Okumura and Yue are combine in the same rational as set forth above with respect to claim 1.
Okumura and Manneschi are combine in the same rational as set forth above with respect to claim 7.
Okumura further teaches wherein the nonlinear modulation function is used to perform modulation using an arbitrary function combined with the nonlinear function (Okumura Para 0073, The modulation may be either amplitude modulation or phase modulation. Specific modulation is performed by multiplying the stream N(t) by a random bit sequence.
para 0077, The time shift processing may be processing of delaying the time or processing of advancing the time. For example, time shift processing represented by the equation (8) is performed.
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Para 0078 line 1-3, The equation (8) is time shift processing that gives a delay to another input stream af(t) by using an arbitrary input stream af(t) as a reference [using an arbitrary function].
Para 0015, The shift register 712 executes computation processing corresponding to processing of step S105 for the input stream af(t). The shift register 712 outputs the calculated input stream d(t) to the computation unit 713. In Example 2, a delay circuit for generating delay in the input stream af(t) using the shift register 712 is implemented. However, the delay circuit may be a delay circuit constituted with a ladder type transmission circuit network constituted with a capacitor and an inductor
Para 0116 The computation unit 713 executes computation processing corresponding to processing of step S107 using the input stream a/t) input from each shift register 712. The computation unit 713 outputs a computation result to the reservoir unit 112.
Para 0118 The computation unit 721 executes computation processing expressed by the equation (2). That is, the computation unit 721 superimposes the input data x(t) input from the input unit 111 and the data q(t) output from the reservoir unit 112. The computation unit 721 outputs the computation result as a signal to the MZ optical modulator 723. [wherein the nonlinear modulation function is used to perform modulation] (i.e. in combination with the nonlinear function)).
Claim(s) 9 are rejected under 35 U.S.C. 103 as being unpatentable over Okumura in view of Yue and further in view of B. Barazani, G. Dion, J. -F. Morissette, L. Beaudoin and J. Sylvestre, "Microfabricated Neuroaccelerometer: Integrating Sensing and Reservoir Computing in MEMS," in Journal of Microelectromechanical Systems, vol. 29, no. 3, pp. 338-347, June 2020, doi: 10.1109/JMEMS.2020.297846 (“Barazani”).
Regarding claim 9, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 1.
Okumura and Yue are combine in the same rational as set forth above with respect to claim 1.
Okumura does not explicitly teach wherein the nonlinear element is constituted by a MEMS element.
However Barazani teaches wherein the nonlinear element is constituted by a MEMS element (Barazani page 339, II. Desing A necessary property of physical RC is the ability to map their input signals into a high-dimensional state, via non-linear dynamics [10]. This mapping allows signals that are originally not linearly separable to be represented in a space where they can be processed by linear models. In this study, the non-linear expansion of the input results from the dynamical response of a clamped-clamped beam oscillating at large amplitudes [11], [12]. We have shown previously [8] that this dynamical response could be exploited to achieve significant neuromorphic computational capabilities, in a very small and energy efficient device. In this work, we leverage the mechanical nature of the clamped-clamped beam computing system by coupling it to a suspended proof mass that implements the sensing functions of the neuromorphic MEMS.
The neuroaccelerometer thus comprises two principal mechanical elements: the non-linear oscillating beam, which has a high natural frequency (section II-B); and a larger suspended inertial mass with a much lower natural frequency, designed to be sensitive to external accelerations (section II-A). When in operation, a pump voltage applied to the inertial mass induces an electrostatic force over the beam, driving it near resonance with large displacements, in its non-linear regime. External accelerations displace the inertial mass, thus modulating the amplitude of the driving force over the beam and consequently the beam oscillation amplitude. The displacement of the beam is measured with piezoresistive strain gauges. The signal from the gauges is digitized, delayed and fed back to the pump voltage, in a scheme described in section V-A that is useful to increase the computational power of simple dynamical systems, at the cost of reduced processing speed (wherein the nonlinear element is constituted by a MEMS element)).
Okumura and Barazani are considered to be analogous to the claim invention because they are in the same field of reservoir computing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Okumura to incorporate the teachings of Barazani to use MEMS neuroacceleromter. Doing to accurately emulate non-linear autoregressive moving models (Barazani page 338 Abstract line 18-20, The neuromorphic MEMS accelerometer was able to accurately emulate non-linear autoregressive moving average models and compute the parity of random bit streams).
Claim(s) 11 are rejected under 35 U.S.C. 103 as being unpatentable over Okumura in view of Yue and further in view of D. Marković, N. Leroux, M. Riou, F. Abreu Araujo, J. Torrejon, D. Querlioz, A. Fukushima, S. Yuasa, J. Trastoy, P. Bortolotti, J. Grollier; Reservoir computing with the frequency, phase, and amplitude of spin-torque nano-oscillators. Appl. Phys. Lett. 7 January 2019; 114 (1): 012409 (“Markovic”).
Regarding claim 11, as best understood given the 112(a) and (b) issue identified above, Okumura in view of Yue teach the reservoir element according to claim 1.
Okumura and Yue are combine in the same rational as set forth above with respect to claim 1.
Okumura does not explicitly teach wherein the nonlinear element is constituted by a spin element.
However Markovic teaches wherein the nonlinear element is constituted by a spin element (Markovic Page 1 para 1 line 20-31 Spin-torque induced magnetization dynamics indeed takes place in nanoscale magnetic volumes, which makes them sensitive to thermal fluctuations. In addition, phase noise is enhanced by amplitude noise due to the inherent coupling between the phase and the amplitude of magnetization oscillations.13 In this work, we show that these issues can be circumvented by working in a regime where the oscillator is synchronized to the input waveform that it has to process which considerably reduces magnetization fluctuations.14 For this purpose, we use a sinusoidal input waveform that carries information encoded in its modulated frequency, chosen close to the spin-torque oscillator frequency.
Page 2 FIG. 1
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[a spin element]
Page 3, The fact that the frequency, amplitude, and phase are all non-linear functions of input frequency enables us to use them for neuromorphic computing. We now demonstrate this capability on a task that consists in classification of sine and square waves of equal periods but different amplitudes. For this, we use a method called single node reservoir computing.6,7,24,25 This method uses time multiplexing in order to emulate a reservoir with a single nano-oscillator that plays a role of a different effective virtual neuron at each time step (i.e. the spin element is the nonlinear element)).
Okumura and Markovic are considered to be analogous to the claim invention because they are in the same field of reservoir computing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Okumura to incorporate the teachings of Markovic and use a Spin element. Doing to classify sine and square waveforms with high accuracy (Markovic page 1 Abstract line 5-8, We show that this method allows classifying sine and square wave forms with an accuracy above 99% when decoding the output from the oscillator amplitude, phase, or frequency. We find that recognition rates are directly related to the noise and non-linearity of each variable. These results prove that spin-torque nano-oscillators offer an interesting platform to implement different computing schemes leveraging their rich dynamical features).
Conclusion
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/ALFREDO CAMPOS/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129