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 .
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 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.
Information Disclosure Statement
Acknowledgment is made of Applicant’s Information Disclosure Statement (IDS) form PTO 1449.These IDS has been considered.
Examiner’s Note
The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages, paragraph and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
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 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 of this title, 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, 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHOU et al. (BR 112019014338 A2) (herein after ZHOU) in view of XIAOHONG et al. (CN 110874594 A) (herein after XIAOHONG).
As to claim(s) 1, ZHOU discloses a method for dust identification utilizing a multimodal neural network, comprising following steps:
S1: collecting multi-source [depth separable convolutional network and a residual error network] data related to dust [@figs. 1-2: collecting images with raised dust and images without raised dust as training samples…page 8];
S2: preprocessing the multi-source data to obtain processed data [preprocessing a training sample…page 8];
S3: constructing a training set for a dust identification model using the processed data [building a training model based on a depth separable convolutional network and a residual error network, and inputting the preprocessed training sample into the training model for training to obtain a raise dust identification model…page 8];
S4: constructing the dust identification model [inputting the image of the position to be detected into the dust identification model for identification, and outputting an identification result by the dust identification model so as to realize the identification of the dust…page 8];
S5: training the dust identification model based on the training set to obtain a final model [the training module is used for inputting the preprocessed training samples into the training model for training to obtain a raise dust recognition model; the real-time acquisition module is used for acquiring an image of the position to be detected in real time…page 9]; and
S6: identifying dust based on the final model [the identification module is used for inputting the image of the position to be detected into the dust identification model for identification, and the dust identification model outputs an identification result, so that the identification of the dust is realized…page 9].
[Note: while each unit configured to perform as claimed may be recited either structurally or functionally, claims directed to an apparatus must be distinguished from the prior art in terms of structure rather than function, because apparatus claims cover what a device is, not what a device does].
ZHOU discloses all the features of the claimed invention except the limitation such as: “wherein the dust identification model comprises a backbone network, an output network, and a fusion network”.
However, XIAOHONG from the same field of endeavor discloses an identification model comprises a backbone network [backbone network], an output network [output network], and a fusion network [regional candidate network] [@fig.2: the activation functions in the backbone network of the initial model, the regional candidate network, and the output network adopt a prilu function, and the backbone network adopts a densenert 121 model. As an optional implementation manner, the fusion module 204 performs feature fusion on the first feature map extracted by the area candidate network and the second feature map extracted by the output network, and a manner of obtaining a final feature map specifically includes: splitting a first feature map extracted from the regional candidate network into a plurality of first sub-feature maps…page 15].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention was made to modify the device/method/system of ZHOU such that the dust identification model comprises the backbone network, the output network, and the fusion network; as taught by XIAOHONG, for the advantages such as: in order to obtain an optimum measurement.
As of claim 4, ZHOU discloses the method according to claim 1, wherein in the step S3, a process of constructing the training set of the dust identification model is as follows: performing spectral analysis on a corresponding channel of the image data of the dust event to obtain a channel suitable for distinguishing dust [collecting images with raised dust and images without raised dust as training samples…page 6][ the real-time acquisition module is used for acquiring an image of the position to be detected in real time… the image enhancement module is used for enhancing data of the training sample after the image segmentation…page 7]; and in the channel suitable for distinguishing the dust, manually marking corresponding image data of the dust event, distinguishing between a dust region and a non-dust region, and completing construction of the training set [the identification module is used for inputting the image of the position to be detected into the dust identification model for identification, and the dust identification model outputs an identification result, so that the identification of the dust is realized…page 7].
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHOU et al. in view of XIAOHONG et al. and further in view of LI et al. (CN 113064221 A) (herein after LI).
As of claim 2, ZHOU discloses the image data of a dust event [It should be noted that the acquisition of the training sample is realized by erecting a camera sensing terminal to obtain a target point image and then dividing the image into an image with raised dust and an image without raised dust…page 8].
ZHOU when modified by XIAOHONG discloses all the features of the claimed invention except the limitation such as: “The method according to claim 1, wherein the multi-source data comprises: inversion data of an imaging spectrometer, data of a ground meteorological observation station”.
However, LI from the same field of endeavor discloses inversion data of an imaging spectrometer [the imager can directly acquire the spatial information of the target, the spectrometer can acquire the material structure information of the target according to the characteristic spectrum of the target, and the spectral imager has the dual functions of the imager and the spectrometer…page 10], data of a ground meteorological observation station [Fig. 3 shows a schematic diagram of the second composite wing drone meteorological observation subsystem of the drone meteorological observation system of the present invention…page 12].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention was made to modify the device/method/system of ZHOU when modified by XIAOHONG such that the inversion data of the imaging spectrometer, data of the ground meteorological observation station; as taught by LI, for the advantages such as: realize high-definition resolution detection of disaster areas.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHOU et al. in view of XIAOHONG et al. and further in view of LUO (CN 104635242 B) (herein after LUO).
As of claim 3, ZHOU when modified by XIAOHONG discloses all the features of the claimed invention except the limitation such as: “The method according to claim 1, wherein the preprocessing in the step S2 specifically comprises: fusing the multi-source data to obtain fused data; and combining a normalized difference dust index and a thermal infrared dust index of the image data of the dust event to obtain a comprehensive dust distinguish index.”.
However, LUO from the same field of endeavor discloses a multi-source data to obtain fused data; and combining a normalized difference dust index and a thermal infrared dust index of the image data of the dust event to obtain a comprehensive dust distinguish index [Fig. 2, differs primarily in that sand and dust recognition sequence, and Fig. 3 completes the basic threshold of Thermal infrared bands Value is sentenced after knowledge, first carries out sentencing knowledge using near-infrared and visible ray ratio, then reuses mid-infrared and does with thermal infrared dual channel difference Sand and dust sentence knowledge…page 12][Step 215, according to sand and dust identification data, reads the near-infrared albedo and the infrared ripple of division window 2 of sand
and dust area pixel Duan Liangwen, using dust intensity computation model, obtains the dust intensity index of each sand and dust pixel…page 12][see claim 3].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention was made to modify the device/method/system of ZHOU when modified by XIAOHONG such that the multi-source data to obtain fused data; and combining the normalized difference dust index and the thermal infrared dust index of the image data of the dust event to obtain the comprehensive dust distinguish index; as taught by LUO, for the advantages such as: reduces sand and dust identification range, improves accuracy of identification.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHOU et al. in view of XIAOHONG et al. and further in view of WANG et al. (CN 113449743B) (herein after WANG).
As of claim 5, ZHOU when modified by XIAOHONG discloses all the features of the claimed invention except the limitation such as: “The method according to claim 1, wherein in the step S4, the backbone network has a U-net architecture; and the U-net outputs a confidence coefficient of a dust category to which each pixel belongs.”.
However, WANG from the same field of endeavor discloses a U-net architecture; and the U-net outputs a confidence coefficient of a dust category to which each pixel belongs [see abstract][@fig.1: a U-Net network, and the Ghost-SE-Unet network comprises a Feature extraction trunk Feature Backbone for extracting Feature information of coal dust particles in an image…page 3].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention was made to modify the device/method/system of ZHOU when modified by XIAOHONG such that the U-net architecture; and the U-net outputs the confidence coefficient of the dust category to which each pixel belongs; as taught by WANG, for the advantages such as: the segmentation precision of the coal dust particles and accurately acquire more detailed information of particle characteristics.
Claim(s) 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHOU et al. in view of XIAOHONG et al. and further in view of JIE et al. (CN 119537967 A) (herein after JIE).
As of claims 8-10, ZHOU when modified by XIAOHONG discloses all the features of the claimed invention except the limitation such as: “The method according to claim 1, wherein in the step S5, an Adam optimization algorithm is used to optimize model parameters during the training of the dust identification model.
A storage device, wherein the storage device stores an instruction and data for implementing the method according to claim 1.
A dust identification device based on a multimodal neural network, comprising a processor and a storage device, wherein the processor loads and executes an instruction and data in the storage device to implement the method according to claim 1”.
However, JIE from the same field of endeavor discloses an Adam optimization algorithm is used to optimize model parameters during the training of the dust identification model [improving the nonlinear representation capability of the network, evaluating the classification result by using a cross entropy loss function, and accelerating the network training by using an Adam optimization algorithm. Through the arrangement, the load characteristic identification model can be obtained by iterating the constructed first model through the acquired electricity data sample set, and accuracy of model load identification is improved…page 9]; a storage device, wherein the storage device stores an instruction and data [the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored…page 16]; an identification device based on a multimodal neural network, comprising a processor and a storage device, wherein the processor loads and executes an instruction and data in the storage device [the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored…page 16][ the load identification method of the industrial park may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11…page 17].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention was made to modify the device/method/system of ZHOU when modified by XIAOHONG such that the Adam optimization algorithm is used to optimize model parameters during the training of the dust identification model; the storage device, wherein the storage device stores an instruction and data; the dust identification device based on the multimodal neural network, comprising the processor and the storage device, wherein the processor loads and executes the instruction and data in the storage device; as taught by JIE, for the advantages such as: accuracy and efficiency of the load identification is improved to satisfy requirement of intelligent load management.
Allowable Subject Matter
Claims 6-7 are 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.
The following is a statement of reasons for the indication of allowable subject matter:
As to claim 6, the prior arts alone or in combination fails to disclose the claimed limitations such as “wherein the output network adopts an eXtreme Gradient Boosting (XGboost) tree; and the XGboost tree outputs a confidence coefficient indicating that a corresponding grid point of a to-be identified region is a dust region.” along with all other limitations of the claim.
Claim 7 is/are allowable due to their dependencies.
Conclusion
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MD M. RAHMAN
Primary Patent Examiner
Art Unit 2886
/MD M RAHMAN/Primary Examiner, Art Unit 2877