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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/30/2023 is being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 3-7 are rejected under 35 U.S.C. 103 as being unpatentable over Nakachi et al (Nakachi et al: "Intelligent Monitoring of Optical Fiber Transmission Using Sparse Coding," IEICE Technical Report, October 2019, 119(229), pages 77-82. Hereinafter Nakachi NPL1) in view of Nakachi et al (Nakachi et al: "Secure Computation of Sparse Dictionary Learning," IEICE Technical Report, March 2019, 118(473), pages 35-40. Hereinafter Nakachi NPL2).
1). With regard to claim 1, Nakachi NPL1 discloses a state estimation system (“a sparse coding-based intelligent constellation diagram analyzer for optical fiber communications”, and Figure 8 etc.) comprising:
a concealment signal generation unit (the unit/circuit that performs the functions shown in Figure 5), implemented using one or more computing devices (inside the DSP of Figure 8), configured to:
acquire learning constellation data and identification constellation data output from a signal processing circuit (Figures 3 and 8, signal processing circuit in the DSP) for optical communication (page 78, right column, Section “2.1 Constellation”, page 81 left column, Section “4.1 Acquisition of constellation data”. Figure 4 shows “an outline of sate estimation using sparse coding. It consists of two steps: sparse dictionary learning and identification”. In Figure 4, the top left input is the learning constellation data, and the top right input is the identification constellation data; page 79 Section “3.1 Overview”, and “in the step of identification, it is identified whether the identification constellation data is in a normal or error state by using the learned sparse dictionary or the like”),
reduce and conceal a number of pieces of data from each of pieces of constellation data through random projection (page 79 right column, Figures 5-6, “preprocessing for reducing the number of data and reducing the dimension of the constellation data is performed”; Section “3.2 Preprocessing”, using three processes “random sampling”, “distribution calculation” and “pooling”. The processing “pooling” is a kind of random projection. Figure 6, SxT matrix is converted into vector yi, therefore it is a “projection” operation), and
generate a learning concealment signal and an identification concealment signal based on each piece of constellation data after the reduction and concealment of the number of pieces of data (Figures 5-6, and outputs from the two preprocessing units of Figure 4, and Figure 5 is the preprocessing unit);
a sparse dictionary learning unit (“Label Consistent K-SVD” shown in Figure 4), implemented using one or more computing devices (pages 81-82), configured to learn a concealment sparse dictionary using a sparse dictionary learning algorithm based on the learning concealment signal (page 79 – page 80, Section “3.3 Sparse Dictionary Learning”, and Figure 7); and
an identification unit (the OMP in Figure 4, “orthogonal matching pursuit”), implemented using one or more computing devices (computing devices in the DSP), configured to estimate a state of the optical communication using the concealment sparse dictionary based on the identification concealment signal (“in the step of identification, it is identified whether the identification constellation data is in a normal or error state by using the learned sparse dictionary or the like”; and page 80, right column “(2) Learning to Classify: LC K-SVD” to page 81 left column, Section “3.4 Discipline”).
Nakachi NPL1 discloses that in “Preprocessing” stage, the number of data and the dimension of the constellation data are reduced. But, Nakachi NPL1 does not expressly state that the number of pieces of data from each of pieces of constellation data are also concealed. However, in the Preprocessing disclosed by Nakachi NPL1, concealment signal actually generated while reducing the data amount (Figure 6 and Section “3.2 Preprocessing”). E.g., Nakachi NPL1 discloses a similar scheme based on random Unitary matrices, in which a data amount of constellation data is reduced and concealed (page 37, Section “3.1 Concealment Operation Based on Random Unitary Martices”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nakachi NPL2 with Nakachi NPL1 so that in the Preprocessing, a data amount of constellation data can be reduced and concealed, and the calculation amount or computation effort can be reduced.
2). With regard to claim 3, Nakachi NPL1 and Nakachi NPL2 disclose all of the subject matter as applied to claim 1 above. And the combination of Nakachi NPL1 and Nakachi NPL2 further discloses wherein the sparse dictionary learning unit is configured to update a sparse coefficient, a concealment dictionary, and a projection matrix by learning the concealment sparse dictionary (Nakachi NPL1: page 80, Section “Step 2: Update the Dictionary”; and Nakachi NPL2: pages 38-39, Section “3.4 Step 2: Update the Hidden Dictionary”.
3). With regard to claim 4, Nakachi NPL1 and Nakachi NPL2 disclose all of the subject matter as applied to claims 1 and 3 above. And the combination of Nakachi NPL1 and Nakachi NPL2 further discloses wherein the identification unit is configured to estimate the state of the optical communication based on the sparse coefficient estimated by the sparse dictionary learning unit and the concealment dictionary (Nakachi NPL1: page 81, Section “3.4 Discipline”, “In the identification step, a dictionary D estimated by LC-KSVD is used for the observation signal “T” formed from the identification constellation data” etc.).
4). With regard to claim 5, Nakachi NPL1 and Nakachi NPL2 disclose all of the subject matter as applied to claim 1 above. And the combination of Nakachi NPL1 and Nakachi NPL2 further discloses wherein the concealment signal generation unit is provided in plurality (Nakachi NPL1: e.g., two concealment signal generation units, “Preprocessing” units, are used in Figure 4).
5). With regard to claim 6, Nakachi NPL1 discloses a concealment signal generation device (inside the DSP of Figure 8) comprising:
a data acquisition unit (Figures 4-5, the unit that is associated with the “random sampling” and is responsible for receiving the “constellation data”), implemented using one or more computing devices (Figures 3 and 8, computing devices in the DSP), configured to acquire learning constellation data and identification constellation data output from a signal processing circuit for optical communication (page 78, right column, Section “2.1 Constellation”, page 81 left column, Section “4.1 Acquisition of constellation data”. Figure 4 shows “an outline of sate estimation using sparse coding. It consists of two steps: sparse dictionary learning and identification”. In Figure 4, the top left input is the learning constellation data, and the top right input is the identification constellation data; page 79 Section “3.1 Overview”, and “in the step of identification, it is identified whether the identification constellation data is in a normal or error state by using the learned sparse dictionary or the like”); and
a concealment signal generation unit (the unit/circuit that performs the functions shown in Figure 5), implemented using one or more computing devices (Figures 4-6 and 8, computing devices in the DSP), configured to:
reduce and conceal a number of pieces of data from each of pieces of constellation data through random projection (page 79 right column, Figures 5-6, “preprocessing for reducing the number of data and reducing the dimension of the constellation data is performed”; Section “3.2 Preprocessing”, using three processes “random sampling”, “distribution calculation” and “pooling”. The processing “pooling” is a kind of random projection. Figure 6, SxT matrix is converted into vector yi, therefore it is a “projection” operation), and
generate a learning concealment signal and an identification concealment signal based on each of constellation data after the reduction and concealment of the number of pieces of data (Figures 5-6, and outputs from the two preprocessing units of Figure 4, and Figure 5 is the preprocessing unit).
Nakachi NPL1 discloses that in “Preprocessing” stage, the number of data and the dimension of the constellation data are reduced. But, Nakachi NPL1 does not expressly state that the number of pieces of data from each of pieces of constellation data are also concealed. However, in the Preprocessing disclosed by Nakachi NPL1, concealment signal actually generated while reducing the data amount (Figure 6 and Section “3.2 Preprocessing”). E.g., Nakachi NPL1 discloses a similar scheme based on random Unitary matrices, in which a data amount of constellation data is reduced and concealed (page 37, Section “3.1 Concealment Operation Based on Random Unitary Martices”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nakachi NPL2 with Nakachi NPL1 so that in the Preprocessing, a data amount of constellation data can be reduced and concealed, and the calculation amount or computation effort can be reduced.
6). With regard to claim 7, Nakachi NPL1 discloses a state estimation method comprising:
acquiring learning constellation data output from a signal processing circuit (Figures 3 and 8, signal processing circuit in the DSP) for optical communication (page 78, right column, Section “2.1 Constellation”, page 81 left column, Section “4.1 Acquisition of constellation data”. Figure 4 shows “an outline of sate estimation using sparse coding. It consists of two steps: sparse dictionary learning and identification”. In Figure 4, the top left input is the learning constellation data, and the top right input is the identification constellation data);
reducing and concealing a number of pieces of data from the learning constellation data through random projection (page 79 right column, Figures 5-6, “preprocessing for reducing the number of data and reducing the dimension of the constellation data is performed”; Section “3.2 Preprocessing”, using three processes “random sampling”, “distribution calculation” and “pooling”. The processing “pooling” is a kind of random projection. Figure 6, SxT matrix is converted into vector yi, therefore it is a “projection” operation);
generating a learning concealment signal based on the learning constellation data after the reduction and concealment of the number of pieces of data from the learning constellation data (Figures 5-6, and outputs from the left side preprocessing unit of Figure 4, and Figure 5 is the preprocessing unit);
learning a concealment sparse dictionary using a sparse dictionary learning algorithm based on the learning concealment signal (“Label Consistent K-SVD” shown in Figure 4; and page 79 – page 80, Section “3.3 Sparse Dictionary Learning”, and Figure 7);
acquiring identification constellation data output from the signal processing circuit for optical communication (page 78, right column, Section “2.1 Constellation”, page 81 left column, Section “4.1 Acquisition of constellation data”. Figure 4 shows “an outline of sate estimation using sparse coding. It consists of two steps: sparse dictionary learning and identification”. In Figure 4, the top right input is the identification constellation data; page 79 Section “3.1 Overview”, and “in the step of identification, it is identified whether the identification constellation data is in a normal or error state by using the learned sparse dictionary or the like”);
reducing and concealing a number of pieces of data from the identification constellation data through the random projection (page 79 right column, Figure 6, “preprocessing for reducing the number of data and reducing the dimension of the constellation data is performed”; Section “3.2 Preprocessing”, using three processes “random sampling”, “distribution calculation” and “pooling”. The processing “pooling” is a kind of random projection. Figure 6, SxT matrix is converted into vector yi, therefore it is a “projection” operation);
generating an identification concealment signal based on the identification constellation data after the reduction and concealment of the number of pieces of data from the identification constellation data (Figures 5-6, and outputs from the right side preprocessing unit of Figure 4, and Figure 5 is the preprocessing unit. “in the step of identification, it is identified whether the identification constellation data is in a normal or error state by using the learned sparse dictionary or the like”; and page 80, right column “(2) Learning to Classify: LC K-SVD” to page 81 left column); and
estimating a state of the optical communication using the concealment sparse dictionary based on the identification concealment signal (Nakachi NPL1: page 81, Section “3.4 Discipline”, “In the identification step, a dictionary D estimated by LC-KSVD is used for the observation signal “T” formed from the identification constellation data” etc.).
Allowable Subject Matter
Claim 2 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230018846 A1
US 20180211140 A1
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/LI LIU/Primary Examiner, Art Unit 2634 September 27, 2025