DETAILED ACTION
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 .
Status of Claims
Claims 1-14 are pending and are examined herein.
Claims 1-14 are rejected under 35 USC 112(b).
Claims 1-7 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Information Disclosure Statement
The attached information disclosure statement(s) (IDS) is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the attached information disclosure statement(s) is/are being considered by the examiner.
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.
Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “inputting a time series with the weight”; however, the limitation calculates a weight of each input vector. Consequently, it is unclear which weight is being input. For the purposes of examination, the limitation is being interpreted as “inputting a time series with each of the calculated weights into the T-LSTM network”. Dependent claims 2-14 do not resolve the issue and are rejected with the same rationale.
Claim 1 recites “inputting a time series with the weight into the T-LSTM network, and calculating and predicting a global phase”. The phrasing and formatting as a single step make it unclear whether the calculating and predicting is a separate step from the inputting or part of the single step. For the purposes of examination, the limitation is being interpreted as “inputting a time series with the weight into the T-LSTM networkto calculate and predict a global phase”. Dependent claims 2-14 do not resolve the issue and are rejected with the same rationale.
Claim 2 recites “the another duration T”; however, this limitation lacks proper antecedent basis. For the purposes of examination, this limitation is being interpreted as “
Claim 2 recites “where F can represent is realized in a neural network.” This phrase is ungrammatical. For the purposes of examination, this limitation is being interpreted as “where F is realized as a neural network”. Dependent claim 9 does not resolve the issue and is rejected with the same rationale.
Claim 3 recites “an” and “Wa”; however, the claim does not explain what either subscript indicates. For the purposes of examination, n is being interpreted as t and a is being interpreted as corresponding to the combined weights an across all times n (i.e., t). Dependent claim 10 does not resolve the issue and is rejected with the same rationale.
Claim 5 recites the values “i” and “j”, but does not indicate what these variables represent. For the purposes of examination, these will be interpreted as arbitrary indices. Dependent claim 12 does not resolve the issue and is rejected with the same rationale.
Claim 5 recites “the process”; however, this limitation lacks proper antecedent basis. For the purposes of examination, “the process” is being interpreted as referring to the quantization of Dfloat32 into Dfix. Dependent claim 12 does not resolve the issue and is rejected with the same rationale.
Claim Rejections - 35 USC § 101 – Abstract Idea
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis
Each of the claims fall within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2 Analysis
Claim 1 includes the following recitation of an abstract idea:
step 1: constructing a filter matrix to filter a count of a detector to obtain a pure count; (This is a recitation of a mathematical concept.)
step 2: constructing an input vector xₜ of a Time-Aware Long-Short Term Memory (T-LSTM) network, where the input vector xₜ comprises a pure count Sₜ obtained by the filter matrix, and a temperature Tₜ and humidity Hₜ at a time t, and input vectors at different times constitute an input time series; and (This is a recitation of a mathematical concept.)
calculating a weight of each input vector in the input time series …(This is a recitation of a mathematical concept.)
…calculating and predicting a global phase. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components. This is a recitation of a mental process. This is also a recitation of a mathematical concept.)
Claim 1 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
… by an attention layer; and (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
… step 3: inputting a time series with the weight into the T-LSTM network, and (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
Claim 1 does not reflect an improvement to computer technology or any other technology.
Claim 2 recites at least the abstract idea identified above in the claim upon which it depends, and further recites
where a noise suppression process of the filter matrix F is expressed as: [Ñ₀, ~M₀, ~N1, ~M1]F = [N₀, M₀, N₁, M₁] = Sₜ, (This is a recitation of a mathematical concept.)
Claim 2 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
applying any initial voltage Vᵢ to a phase modulation PM for a duration T, and recording counts ~N₀ and ~M₀ of two channels of the detector in the duration T; and then increasing a voltage by a half-wave voltage Vhalf of half of the PM, namely, applying a voltage Vᵢ + Vhalf/2 for another duration T and recording counts ~N₁ and ~M₁ of two channels of the detector in the another duration T, (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).)
… where F can represent is realized in a neural network. (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 2 does not reflect an improvement to computer technology or any other technology.
Claim 3 recites at least the abstract idea identified above in the claim upon which it depends, and further recites
calculates the weight aₙ of each input vector in the time series with the formula as follows: an = softmax (xTWaxt-5n), where x represents a matrix formed by all the input vectors in parallel, Wₐ is a weight value obtained by training, and softmax is a normalized exponential function. (This is a recitation of a mathematical concept.)
Claim 3 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
the attention layer (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 3 does not reflect an improvement to computer technology or any other technology.
Claim 4 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 4 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
wherein in the step 3, the T-LSTM network comprises a first T-LSTM Block and a second T-LSTM Block, the first T-LSTM Block serving as an encoder for the input time series, and the second T-LSTM Block serving as a decoder for an output time series. (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 4 does not reflect an improvement to computer technology or any other technology.
Claim 5 recites at least the abstract idea identified above in the claim upon which it depends, and further recites
step 3.1: quantizing the weight matrix and the bias vector, and quantizing a 32-bit floating-point number Dfloat32 into a fixed-point number Dfix with a 1-bit sign bit, a Nᵢₙₜ-bit integer bit and a Ndec-bit decimal bit, where the process is expressed as:
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where N = 1 + Nᵢₙₜ + Ndec represents a quantized digit bit number, and round(x) represents a rounding operation; (This is a recitation of a mathematical concept.)
step 3.2: pruning a quantized weight matrix, as follows: each row of the weight matrix is divided into a plurality of blocks with equal size, only a weight value with a maximum absolute value is retained in each block of the weight matrix, and other weight values in the block are replaced with zero; and (This is a recitation of a mathematical concept.)
Claim 5 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
step 3.3: storing a pruned weight matrix as follows: non-zero elements in the weight matrix and indices of the non-zero elements in a corresponding block are stored; and an index length Lindex for a sparse matrix Msparseixj having each row divided into Nbank blocks is Lindex = ceil[log2(j/Nbank)], where ceil(x) represents a ceiling operation. (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, storing or retrieving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example iv. Storing and retrieving information.)
Claim 5 does not reflect an improvement to computer technology or any other technology.
Claim 6 recites at least the abstract idea identified above in the claim upon which it depends, and further recites
wherein an activation function Sigmoid(x) in the T-LSTM network is fitted using a piecewise linear function, and…, which comprises following steps: (1) evenly dividing Sigmoid(x) into Npw segments over [-8,8], where Npw = 2ᵃ, α being a positive integer; (2) fitting a ith segment of Sigmoid(x) to a linear function y = kᵢx + bᵢ, where 0 ≤ i < Npw; (3) quantizing …, where 0 ≤ i < Npw; and …and calculating …y = kᵢx + bᵢ. (This is a recitation of a mathematical concept.)
Claim 6 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
parameters of the piecewise linear function are stored in a lookup table… and storing kᵢ and bᵢ into the lookup table … (4) taking kᵢ and bᵢ from the lookup table according to a value of an input variable, (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, storing or retrieving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example iv. Storing and retrieving information.)
and outputting (This is a mere instruction to apply the judicial exception, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 6 does not reflect an improvement to computer technology or any other technology.
Claim 7 recites at least the abstract idea identified above in the claim upon which it depends, and further recites
wherein an activation function Tanh(x) in the T-LSTM network is fitted using a piecewise linear function, and…, which comprises following steps: (1) evenly dividing Tanh(x) into Npw segments over [-4,4], where Npw = 2ᵃ, α being a positive integer; (2) fitting a ith segment of Tanh(x) to a linear function y = kⱼx + bⱼ, where 0 ≤ j < Npw; (3) quantizing …, where 0 ≤ j< Npw; and …calculating … y = kⱼx + bⱼ. (This is a recitation of a mathematical concept.)
Claim 7 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
parameters of the piecewise linear function are stored in a lookup table… and storing kⱼ and bⱼ into the lookup table…(4) taking kⱼ and bⱼ from the lookup table according to a value of an input variable, and (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, storing or retrieving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example iv. Storing and retrieving information.)
…and outputting (This is a mere instruction to apply the judicial exception, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 7 does not reflect an improvement to computer technology or any other technology.
Allowable Subject Matter
Claims 1-14 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 112(b) and 35 U.S.C. 101 set forth in this office action.
Regarding claim 1, Liu (Practical Phase-Modulation Stabilization in Quantum Key Distribution via Machine Learning) teaches
step 2: constructing an input vector xₜ of a Time-Aware Long-Short Term Memory (T-LSTM) network, where the input vector xₜ comprises … a temperature Tₜ and humidity Hₜ at a time t, and input vectors at different times constitute an input time series; and … (Liu, Figure 1 and caption.)
step 3: inputting a time series … into the T-LSTM network, and calculating and predicting a global phase. (Liu, Figure 3 and caption. Note that the LSTM does not include an attention layer, but the claim requires an attention layer. For example, contrast with instant application Figure 1.)
Regarding claim 1, Liu (CN110365473B) teaches substantially the same subject matter as the previously cited paper with overlapping inventors/authors. Note in particular Figures 1 and 3 and accompanying description in the specification.
Regarding claim 1, Wang (Machine learning for optimal parameter prediction in quantum key distribution) teaches
A … method suitable for a twin-field quantum key distribution system, comprising following steps: (Wang, Abstract and Section I.A., first paragraph)
step 1: … obtain a pure count; (Wang, page 2, last paragraph)
step 2: constructing an input vector xₜ of a … [neural, not necessarily T-LSTM] network, where the input vector xₜ comprises a pure count Sₜ …, and input vectors at different times constitute an input time series; and calculating a weight of each input vector in the input time series by an attention layer; and (Wang, page 3 describes assembling the input vector e, which includes a dark count probability. Figure 2 shows the neural network, including a first layer, which produces weights for each of the input vectors.)
step 3: inputting a time series with the [neural, not necessarily T-LSTM] network, and calculating and predicting [parameters]. (Wang, Figure 2, shows the neural network accepting inputs. Figure 3 shows that a prediction is generated. The prediction is described on page 5, right hand column, first two paragraphs. However, none of the outputs could be reasonably interpreted as a global phase.)
Regarding claim 1, Xu (Twin-Field Quantum Key Distribution with Discrete-Phase-Randomized Sources) teaches
a temperature Tₜ and humidity Hₜ at a time t, and input vectors at different times constitute an input time series; calculating and predicting a …phase (Xu, page 3108, last paragraph indicates that there is an association between phase evolution and temperature and humidity.)
Regarding claim 1, Chen (Quantum Key Distribution over 658 km Fiber with Distributed Vibration Sensing) teaches
a… count St …a temperature Tₜ … at a time t, and input vectors at different times constitute an input time series; calculating and predicting a global phase (Chen, Abstract indicates that the temperature must be accounted for. Page 3, left hand column, last paragraph indicates that the global phase in the fiber may be extracted.)
However, the prior art considered together does not fairly teach or suggest claim 1 as a whole. Claims 2-14 are not fairly taught or suggested by the prior art by virtue of their dependence on claim 1.
Conclusion
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/MARKUS A. VASQUEZ/ Primary Examiner, Art Unit 2121
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