DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim(s) 1-17 are rejected under 35 U.S.C. 102(a1).
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.
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: “a collection module”, “a determination module”, “a recognition module”, “an acquisition module”, “a detection module” in claim 9.
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, they 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 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 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 them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-17 are rejected under 35 U.S.C. 102(a1) as being anticipated by US Publication 2014/0319351 to Yamada et al.
In regards to claims 1-17, Yamada discloses and shows in Figures 1-7, a processing system and method comprising:
a light source (10) assembly configured to illuminate a to-be-collected object, wherein the to-be-collected object includes a detection object (3) and a background object (2) (par.5, 18-20, 22);
a spectral image collection assembly (20) configured to collect a first spectral image of the to- be-collected object (par. 5, 20, 26-29); and
a processing device (30) (par. 20, 32-33) configured to:
perform spectral image collection (S01) on a to-be-collected target to obtain a first spectral image (par. 5-6, 26-29, 34-35; wherein a hyper-spectral image is obtained for an imaging area of a conveyor belt with various objects thereon) (Figure 2-3);
determine spectral characteristics (S02) corresponding to different image regions in the first spectral image (par. 5-6, 32, 35; wherein spectral data is obtained for each pixel of the detection unit; wherein object change characteristics may be determined from the obtained spectral data and reference spectral data) (Figures 2-6);
recognize attribute parameters of to-be-collected objects (S03) corresponding to the different image regions (A, B, C, D, E) according to the spectral characteristics corresponding to the different image regions (par. 32, 36-41; wherein target pixels are identified through various analysis methods and algorithms), the attribute parameters being related to a material of the to-be-collected object, and the to-be-collected object including a detection object and a background object (par. 18-19, 28, 32) (Figures 2-6);
determine a target region (S04) corresponding to the detection object in the first spectral image according to the attribute parameters corresponding to the different image regions in the first spectral image (par. 42-47; wherein the target pixels can be grouped together according to various desired ranges), and obtaining a second spectral image formed by the target region (par. 32, 35, 39, 42-44; wherein the image-capturing is done continuously, and “spectral data regarding inspection objects, the belt conveyor and the carrying container are acquired beforehand”); and
perform abnormality detection on the detection object based on region characteristics of different image regions of in the second spectral image (par. 5-6, 18-19, 32; wherein different kind or defective quality of various objects may be determined; wherein object change characteristics may be determined from the obtained spectral data and reference spectral data);
[claims 2, 11] wherein the processing device is further configured to:
extract spectral information of sampling points of the different image regions in the first spectral image (par 5-6, 26-29); and
determine reflectance of the sampling points for different wavelengths of light based on the spectral information to obtain spectral distribution characteristics of the sampling points (par. 26-29, 34-35) (Figures 2-3);
wherein the spectral distribution characteristics of the sampling points corresponding to the different image regions in the first spectral image are used as the spectral characteristics of the different image regions (par. 26-29, 43-47) (Figures 2-6);
[claims 3, 12] wherein the processing device is further configured to:
recognize the attribute parameters of the to-be-collected objects corresponding to the different image regions according to the spectral distribution characteristics of the sampling points corresponding to the different image regions and a first recognition rule, wherein the first recognition rule includes reflectance ranges for each wavelength of light of different wavelengths of the light corresponding to the different attribute parameters (par. 26-29, 31-32, 37, 41; wherein various wavelength threshold values and comparisons may be utilized); or
process the spectral distribution characteristics of the sampling points corresponding to the different image regions using a pre-trained first recognition model to obtain the attribute parameters of the to-be-collected objects corresponding to the different image regions (par. 40; wherein various pre-trained support vector machines may be utilized);
[claims 4, 13] wherein the processing device is further configured to: segment an image of the target region from the first spectral image to obtain the second spectral image (par. 42-47; wherein the target pixels can be grouped together according to various desired ranges (3x3, 5x5 pixels), columns (A, B, C, D, E) and rows (1, 2, 3, 4, 5));
[claims 5, 14] wherein the processing device is further configured to: extract visual characteristics of the different image regions in the second spectral image (par. 18-19, 32, 59-60, 71; wherein intensity values for a plurality of wavelengths are determined for each pixel; and wherein changes in object characteristics may be determined); and
perform abnormal type recognition on detection object regions corresponding to the different image regions in the second spectral image according to the extracted visual characteristics (par. 18-19, 59-60, 71);
[claims 6, 15] wherein the processing device is further configured to: recognize whether the detection object regions corresponding to the different image regions in the second spectral image are abnormal and an abnormal type corresponding to an abnormal situation according to the extracted visual characteristics and a second recognition rule, wherein the second recognition rule includes reference visual characteristics corresponding to different abnormal types of the detection object (par. 26-29, 31-32, 37, 41; wherein various wavelength threshold values and comparisons may be utilized); (par. 35, 39, 42-44; wherein the image-capturing is done continuously, and “spectral data regarding inspection objects, the belt conveyor and the carrying container are acquired beforehand”); (par. 5-6, 18-19; wherein different kind or defective quality of various objects may be determined); or
process the extracted visual characteristics using a pre-trained second recognition model to obtain an abnormality recognition result, wherein the abnormality recognition result at least includes the abnormal type when the detection object regions corresponding to the different image regions are abnormal in the second spectral image (par. 40; wherein various pre-trained support vector machines may be utilized);
[claims 7, 16] wherein the processing device is further configured to: extract the spectral characteristics of the different image regions in the second spectral image (par. 18-19, 32, 59-60, 71; wherein intensity values for a plurality of wavelengths are determined for each pixel; and wherein changes in object characteristics may be determined); and
perform the abnormal type recognition on the detection object regions corresponding to the different image regions in the second spectral image according to the extracted spectral characteristics (par. 18-19, 59-60, 71);
[claims 8, 17] wherein the processing device is further configured to: identify whether the detection object regions corresponding to the different image regions in the second spectral image are abnormal and the abnormal type corresponding to an abnormal situation according to the extracted spectral characteristics and a third recognition rule, wherein the third recognition rule includes reference spectral characteristics corresponding to different abnormal types of the detection object (par. 35, 39, 42-44; wherein the image-capturing is done continuously, and “spectral data regarding inspection objects, the belt conveyor and the carrying container are acquired beforehand”); or
process the extracted spectral characteristics using a pre-trained third recognition model to obtain an abnormal recognition result, wherein the abnormal recognition result at least includes an abnormal type when the detection object regions corresponding to the different image regions in the second spectral image (par. 40; wherein various pre-trained support vector machines may be utilized).
Conclusion
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JONATHAN M. HANSEN
Primary Examiner
Art Unit 2877
/JONATHAN M HANSEN/Primary Examiner, Art Unit 2877
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