DETAILED ACTION
Claim Interpretation
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: acquisition unit and detection unit in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof:
Referring to the specifications as filed, the acquisition unit corresponds to ¶20 & Fig. 2 110, and the detection unit corresponds to ¶22 & Fig. 2 112. Furthermore, ¶19 discloses “The processor 11 operates in accordance with a predetermined program to exhibit functions as an acquisition unit 110, an image processing unit 111, a detection unit 112, and a training unit 113.”
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 § 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 and 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over CN112781791A in view of Minematsu et al (NPL: Analytics of Deep Neural Network-Based Background Subtraction).
Regarding claim 1, CN112781791A discloses an information processing device (abstract VOCs gas leak detection method and system based on optical gas imaging) comprising:
an acquisition unit (pg. 12 processing equipment comprises a processor) that acquires an inspection image obtained by capturing an inspection target (pg. 9 Example 1: acquiring a frame of thermal image, namely an infrared image, of the area to be detected); and
a detection unit (pg. 12 processing equipment comprises a processor) that detects presence of a gas in a vicinity of the inspection target (pg. 11 Example 1: if the infrared image count value satisfying the detection condition is greater than a preset threshold value T8(T8 is 10), it is determined that gas leakage occurs in the region to be detected), and in which a temporal and spatial change rate in the inspection image is set as an input parameter (pg. 11 Example 1: The area change rate is a characteristic quantity representing the rate of change of the target area with time) and information where whether or not the gas is present in the inspection image from which the fluctuation of the background is removed is detectable is set as an output parameter (pg. 9-10 example 1: As shown in fig. 2, a background subtraction method is used to extract a dynamic change region of the current frame infrared image from the background image).
CN112781791A fails to teach where Minematsu teaches using a prediction model that is trained by an image captured in a case where the gas is not present to learn a fluctuation of a background (pg. 1 abstract Deep neural network-based (DNN-based) background subtraction has demonstrated excellent performance for moving object detection; We designed better background features to ignore changes that are not of interest in the dynamic backgrounds; pg. 5 We prepared a background image from all the training images in each sequence by using a temporal median filter method; pg. 6 In Scenarios 3 and 4, the modified network correctly classified waving trees and a pedestrian shadow to a background class because of learning background changes from the training images of Scenarios 3 and 4 containing waving tree and pedestrian shadows. Therefore, this implies that the modified network should learn background information of testing sequences for improving detection accuracy).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of using a prediction model that is trained by an image captured in a case where the gas is not present to learn a fluctuation of a background from Minematsu into the information processing device for detecting presence of gas as disclosed by CN112781791A. The motivation for doing this is to improve methods and devices for detecting objects and changes in images.
Regarding claim 2, the combination of CN112781791A and Minematsu disclose the information processing device according to claim 1, wherein the change rate is a brightness change rate in the inspection image (CN112781791A pg. 10 a background subtraction method is used to extract a dynamic change region of the current frame infrared image from the background image; calculating the mean m and variance delta of the gray levels of the extracted dynamic change regions; pg. 11 Example 1: The area change rate is a characteristic quantity representing the rate of change of the target area with time).
Regarding claim(s) 6 (drawn to a method):
The rejection/proposed combination of CN112781791A and Minematsu, explained in the rejection of method claim(s) 1, anticipates/renders obvious the steps of the method of claim(s) 6 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 1 is/are equally applicable to claim(s) 6.
Regarding claim(s) 7 (drawn to a CRM):
The rejection/proposed combination of CN112781791A and Minematsu, explained in the rejection of method claim(s) 1, anticipates/renders obvious the steps of the computer readable medium of claim(s) 7 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 1 is/are equally applicable to claim(s) 7.
Claim(s) 3 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of CN112781791A and Minematsu as applied to claim 1 above, and further in view of Huang et al (US 12008466 B1).
Regarding claim 3, the combination of CN112781791A and Minematsu disclose the information processing device according to claim 1, but fail to teach where Huang teaches wherein the prediction model is a machine learning model trained by using a long short-term memory (LSTM) (col. 6 lines 28-39 Long Short-Term Memory (LSTM) is a frequently used recurrent neural network variant).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein the prediction model is a machine learning model trained by using a long short-term memory (LSTM) from Huang into the processing device as disclosed by the combination of CN112781791A and Minematsu. The motivation for doing this is to improve systems and methods for operating a neural network for outputting predictions .
Regarding claim 5, the combination of CN112781791A and Minematsu disclose the information processing device according to claim 1, but fail to teach where Huang teaches further comprising: an image processing unit that performs a convolution operation on the inspection image to generate a texture image, wherein the detection unit acquires the input parameter from the texture image (col 7 lines 65-67 to col 8 lines 1-24 The outputs of the second fully-connected 412 layer are the output predictions 414; col 8 lines 57-64 a convolutional neural network can include multiple convolution layers, with each layer refining the features extracted by a previous layer. Each convolution layer may be, but need not be, followed by pooling. The output of a combination of these layers represent high-level features of the input image, such as the presence of certain shapes, colors, textures, gradients, and so on).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of an image processing unit that performs a convolution operation on the inspection image to generate a texture image, wherein the detection unit acquires the input parameter from the texture image from Huang into the processing device as disclosed by the combination of CN112781791A and Minematsu. The motivation for doing this is to improve systems and methods for operating a neural network for outputting predictions.
Allowable Subject Matter
Claim 4 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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 4, the prior art of record, alone or in combination, fails to teach at least “wherein the input parameter includes an average and a standard deviation of a brightness change rate and an average and a standard deviation of a corner movement amount in an element included in the inspection image, and a histogram shape change rate in a brightness of the inspection image”.
Conclusion
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/KEVIN KY/Primary Examiner, Art Unit 2671
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