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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-9 and 16-21 in the reply filed on 2/4/2026 is acknowledged.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-9, 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the generation of images to input into a machine learning and process to output a combination of input images and target images into a display without significantly more.
The claim(s) 1 and 16 recite(s):
“receiving at least one input image (the captured images as relied on as the input image”
“providing the at least one input image as an input to a machine learning algorithm”
“generating a target image from the at least one input image using the machine learning algorithm”
“providing the at least one input image and the target image to a display driver”
This judicial exception is not integrated into a practical application because The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recitation of " machine learning algorithm " merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element " machine learning algorithm " limits the identified judicial exceptions " generating a target image from the at least one input image using the machine learning algorithm" this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h)
The claim(s) does/do not include additional elements that are sufficient to amount to
significantly more than the judicial exception because Additional elements (computing device); (display driver) and (display device) and “endoscope” were both found to be insignificant extra-solution activity in Step 2A,
Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
As discussed above, the broadest reasonable interpretation of steps (b) is that those
steps fall within the mental process groupings of abstract ideas because they cover concepts
performed in the human mind, including observation, evaluation, judgment, and opinion. See
MPEP 2106.04(a)(2), subsection III.
Specifically, the step recites " generating a target image from the at least one input image using the machine learning algorithm " which may be practically performed in the human mind using observation, evaluation, judgment, and opinion. Under its broadest reasonable interpretation when read in light of the specification, the "generating" encompasses mental observations or evaluations that are practically performed in the human mind.
As discussed above, the broadest reasonable interpretation of dividing also encompasses mathematical concepts that can be performed mentally. The limitations "receiving, providing and generating are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) ("whether the limitation is significant"). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP
Further, limitation “computing device” is recited as being performed by a microprocessor. The
computing device is recited at a high level of generality. The computing device is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). In limitations (b) and (c),
the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such
that it amounts to no more than mere instructions to apply the exception using a generic
computer. See MPEP 2106.05(f)
These limitations "using machine learning algorithm" provide nothing more than mere
instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP
2106.05(f).
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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) 1-3, 5-7, 16-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zur (Pub. No.: US 2020/0387706)
Regarding claims 1, 16, Zur discloses a system for generating a target image, comprising:
an endoscope having an image collection component (camera) [see 0045, 0047, 0092];
a computing device (204) communicatively connected to the image collection component of the endoscope [see 0077];
comprising a non-transitory computer-readable medium with instructions stored thereon, which when executed by a processor perform steps [see 0066, 0079] comprising:
receiving at least one input image (the captured images as relied on as the input image, emphasis added) from the image collection component of the endoscope [see 0075, 0095, 0134-0135] by disclosing the 3D reconstruction neural network may be trained using a training dataset of pairs of 2D endoscopic images defining input images [see 0135];
providing the at least one input image as an input to a machine learning algorithm [see 0099, 0102] by disclosing the image is fed into a detection neural network [see 0099] and one or more endoscopic images of a sequential sub-set of the endoscopic images are fed into the detection neural network [see 0103];
generating a target image (the 3D reconstructed image is relied on as the target image, emphasis added) from the at least one input image using the machine learning algorithm [see fig 8, 0134-0135];
providing the at least one input image and the target image to a display driver [see 0064, 0103] by disclosing the transformed location is the location that is presented on the display with frame number i±2 [see 0064];
a display device, communicatively connected to the computing device, and configured to display the images provided to the display driver [see 0064, 0080] by disclosing Computing device 204 is connected between imaging probe 212 and display 226 [see 0080-0083]
Regarding claims 2, 17, Zur discloses wherein the at least one input image comprises a sequence of at least five frames of a video recorded by the image collection component [see 0135-0136, 0161] by disclosing the 3D reconstruction process may be trained using a large dataset (e.g., at least 100,000 images of the colon from at least 100 different colonoscopy videos, or other smaller or larger values) [see 0136].
Regarding claim 3, Zur discloses wherein the image collection component is a camera [see 0045, 0047, 0092].
Regarding claim 5, Zur discloses wherein the computing device is positioned in the display device [see 0080] by disclosing computing device 204 may be installed for each colonoscopy workstation (e.g., includes imaging probe 212 and/or display 226) [see 0080]
Regarding claim 6, Zur discloses wherein the computing device is positioned in the endoscope [see 0080] by disclosing computing device 204 may be installed for each colonoscopy workstation (e.g., includes imaging probe 212 and/or display 226) [see 0080]
Regarding claims 7, 18, Zur discloses wherein the machine learning algorithm is selected from a convolutional neural network [see 0051, 0147], a generative/adversarial neural network, or a U-Net.
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 (i.e., changing from AIA to pre-AIA ) 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, 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) 4 is rejected under 35 U.S.C. 103 as being unpatentable over Zur (Pub. No.: US 2020/0387706) in view of Abitbol (Pub. No.: US 2018/0160885).
Regarding claim 4, Zur discloses the endoscope further comprising a tube with the image collection component positioned at a distal end of the tube [see 0002]
Zur doesn’t disclose the tube having an outer diameter of at most 10 mm
Nonetheless, Abitbol discloses the tube having an outer diameter of at most 10 mm [see 0107] by disclosing carrier element 1604 may be elongate, such as in the form of a hollow cylinder (or tube) having a diameter of approximately 1/16″ (2.5 mm) [see 0107].
Therefore, it is obvious to one skilled in the art at the time the invention was filed and would have been motivated to combine Zur and Abitbol by the tube having an outer diameter of at most 10 mm; to have a diameter small enough to navigate easily.
Claim(s) 8-9, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zur (Pub. No.: US 2020/0387706) in view of Linard et al (Pub. No.: US 2016/0253801).
Regarding claim 8, Zur doesn’t disclose buffering a sequence of input images to process with the machine learning algorithm.
Nonetheless, Linard et al disclose buffering a sequence of input images [see abstract, 0018-0019, 0065].
Therefore, it is obvious to one skilled in the art at the time the invention was filed and would have been motivated to combine Zur and Linard et al by buffering a sequence of input images; because the buffer acts as a temporary storage tank, allowing the camera to keep capturing images even while the memory card is being written to; Without a buffer, the camera would have to wait for each image to be fully written to the card before taking the next shot. The buffer eliminates this delay, letting you shoot faster and more fluidly.
Regarding claims 9, 20, Zur discloses wherein the sequence comprises at least five input images [see 0135-0136] by disclosing the 3D reconstruction process may be trained using a large dataset (e.g., at least 100,000 images of the colon from at least 100 different colonoscopy videos, or other smaller or larger values) [see 0136].
Claim(s) 21 is rejected under 35 U.S.C. 103 as being unpatentable over Zur (Pub. No.: US 2020/0387706) in view of Godard et al (Pub. No.: US 2019/0213481)
Regarding claim 21, Zur doesn’t disclose upsampling the at least one input image using bilinear interpolation or strided transpose convolution.
Nonetheless, Godard et al disclose upsampling the at least one input image using bilinear interpolation or strided transpose convolution [see 0011, 0047].
Therefore, it is obvious to one skilled in the art at the time the invention was filed and would have been motivated to combine Zur and Godard et al by upsampling the at least one input image using bilinear interpolation or strided transpose convolution; Upsampling corrects imbalanced data, which can lead to better model performance in machine learning and data analysis; Upsampling can reduce distortion and provide a more detailed representation of audio signals, potentially improving sound quality; Upsampling allows digital filters to operate at higher frequencies, reducing the impact of aliasing and ringing in audio streams and Upsampling can lead to better conversion accuracy in DACs, reducing artifacts and improving overall sound quality.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOEL F BRUTUS whose telephone number is (571)270-3847. The examiner can normally be reached Mon-Sat, 11:00 AM to 7:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Kozak can be reached at 571-270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOEL F BRUTUS/ Primary Examiner, Art Unit 3797