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 .
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:
acquisition unit
determination unit
reconstruction unit
training unit
training data generating module
training data processing module
neural network training module
detector
in claims 1-16 and 19-20.
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:
acquisition unit – processor, see Specification, paragraph [0125]
determination unit - processor, see Specification, paragraph [0125]
reconstruction unit - processor, see Specification, paragraph [0125]
training unit - processor, see Specification, paragraph [0125]
training data generating module - processor, see Specification, paragraph [0125]
training data processing module - processor, see Specification, paragraph [0125]
neural network training module - processor, see Specification, paragraph [0125]
detector – photoelectric sensor assembly, see Specification, paragraph [0052]
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.
Allowable Subject Matter
Claims 5 and 7-15 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. Reasons for allowance will be provided in the event the application becomes in condition for allowance.
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) 1-4, 6, and 17-19 rejected under 35 U.S.C. 103 as being unpatentable over Li (“Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning”, copy provided, see PTO-892) in view of Ardila (“End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography discloses:”, copy provided, see PTO-892).
Regarding claim 1, Li (“Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning”, copy provided, see PTO-892) discloses:
A medical image processing apparatus, characterized by comprising:
an acquisition unit configured to acquire raw local projection data obtained by a detector after an object to be examined is scanned (see Abstract, a local scan of high spatial and spectral resolution is performed initially; see also page 8 – 3. Scan the patient optically with surface scanner to generate S(local))
a processing unit configured to recover the raw local projection data to estimate first global data (page 8, 1c, the two surface models are registered to align S(global) with S(local));
a determination unit configured to determine second global data according to the raw local projection data and the first global data (page 8, 1e, the registration parameters are aligned in reference to the registration between V(global) and V(local)); and
a reconstruction unit configured to reconstruct the second global data to obtain a diagnostic image (page 8-9, an aligned global reconstruction occurs to provide a diagnostic image)
Even assuming arguendo that Li does not explicitly disclose:
a determination unit configured to determine second global data according to the raw local projection data and the first global data
Ardila (“End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography discloses:”, copy provided, see PTO-892) discloses:
an acquisition unit configured to acquire raw local projection data obtained by a detector after an object to be examined is scanned (Figure 10, page 962, “Model development and training”, nodule level local information is extracted);
a determination unit configured to determine second global data according to the raw local projection data and the first global data (Figures 7, 10, page 962, the resulting predictions are based on both the nodule-level local information and global context from the entire volume)
a reconstruction unit configured to reconstruct the second global data to obtain a diagnostic image (Figures 7, 10, page 962, the system obtains a global reconstructed diagnostic image)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the system of Ardila with the system of Li such that the system would determine second global data according to the raw local projection data and the first global data as described. The suggestion/motivation would have been in order to implement a system that can “provide…global context for every candidate region” (page 962 of the Ardila reference).
Regarding claim 2, Li additionally discloses:
characterized in that the processing unit recovers the raw local projection data to obtain estimated missing data, and determines the first global data according to the estimated missing data and the raw local projection data (page 8, the surface models are registered to align orientation and position- correcting them for previously missing context – with S(global) being configured)
Regarding claim 3, Li additionally discloses:
characterized in that the processing unit processes the raw local projection data to obtain a first reconstructed image or a first sinogram (page 8, the steps in “1) Registration” involve reconstruction and the steps in 2) result in a sinogram), and inputs the first reconstructed image or the first sinogram into a pre-trained neural network model to estimate the first global data (page 9, reconstruction is done via an algorithm developed in the deep learning framework)
Regarding claim 4, Li additionally discloses:
characterized in that the determination unit fuses the raw local projection data with the first global data to obtain the second global data (page 8, 1e, the registration parameters are aligned in reference to the registration between V(global) and V(local));
Regarding claim 6, Li additionally discloses:
characterized in that the first global data comprises a first global image or a first global sinogram (page 8, the aligned S(global) is a global image)
Regarding claim 17, the structural elements of apparatus claim 1 perform all of the steps of method claim 17. Thus, claim 17 is rejected for the same reasons discussed in the rejection of claim 1.
Regarding claim 18, the structural elements of apparatus claim 2 perform all of the steps of method claim 18. Thus, claim 18 is rejected for the same reasons discussed in the rejection of claim 2.
Regarding claim 19, arguments analogous to claim 1 are applicable. The medical device of claim 19 is taught by the same disclosed elements performing their described functions detailed in the rejection of claim 1.
Claim(s) 16 and 20 rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Ardila in further view of Yu et al. (U.S.P.G. Pub. No. 2020/0000422).
Regarding claim 16, the combination of Li and Ardila discloses the apparatus of the parent claim (claim 1).
The combination of Li and Ardila does not explicitly disclose:
characterized in that the detector is an incomplete detector having partial off-center detector modules removed from a plurality of detector modules arranged in an array.
Yu et al. discloses:
characterized in that the detector is an incomplete detector having partial off-center detector modules removed from a plurality of detector modules arranged in an array (Figures 3, 4, paragraphs [0039]-[0049], the detector module includes a set of submodules that are off center arranged in an array)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the system of Yu et al. with the combination of Li and Ardila such that the system would have been characterized in that the detector is an incomplete detector having partial off-center detector modules removed from a plurality of detector modules arranged in an array as described in Li. The suggestion/motivation would have been in order to implement a system capable of “improving the quality of the reconstructed image” (paragraph [0044] of the Yu et al. reference reference).
Regarding claim 20, arguments analogous to claim 16 are applicable. The medical device of claim 20 is taught by the same disclosed elements performing their described functions detailed in the rejection of claim 16.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN R WALLACE whose telephone number is (571)270-1577. The examiner can normally be reached Monday-Friday from 8:30-5 PM.
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/JOHN R WALLACE/ Primary Examiner, Art Unit 2682