Prosecution Insights
Last updated: April 19, 2026
Application No. 18/558,001

SYSTEM AND METHOD FOR OPTICAL PRESSURE SENSING OF BODY PART

Non-Final OA §102§103
Filed
Oct 30, 2023
Examiner
MAYNARD, JOHNATHAN A
Art Unit
3798
Tech Center
3700 — Mechanical Engineering & Manufacturing
Assignee
The General Hospital Corporation
OA Round
1 (Non-Final)
39%
Grant Probability
At Risk
1-2
OA Rounds
3y 10m
To Grant
46%
With Interview

Examiner Intelligence

Grants only 39% of cases
39%
Career Allow Rate
74 granted / 189 resolved
-30.8% vs TC avg
Moderate +7% lift
Without
With
+6.9%
Interview Lift
resolved cases with interview
Typical timeline
3y 10m
Avg Prosecution
31 currently pending
Career history
220
Total Applications
across all art units

Statute-Specific Performance

§101
7.0%
-33.0% vs TC avg
§103
50.8%
+10.8% vs TC avg
§102
16.8%
-23.2% vs TC avg
§112
20.8%
-19.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 189 resolved cases

Office Action

§102 §103
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 Election/Restrictions Claims 8-10, 15, and 22-24 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 12/01/2025 of species groupings 1a and 2b. 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. Claims 1-4, 6-7, and 11-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by O’Connor et al. (U.S. Pub. No. 2014/0121479), hereinafter “O’Connor.” Regarding claim 1 , O’Connor discloses a system for analyzing a body part of a subject (a device for imaging a human foot, Abstract), comprising: a sensor interface having a surface for receiving the body part (a transmissive sheet with a n upper surface configured to accommodate a sole of the foot, Abstract); a light source for emitting light having multiple wavelengths within the sensor interface to be reflected by the body part interacting with the surface (a light source positioned below the sheet for emitting light toward the sheet, Abstract; light source emits light having multiple wavelengths, [0040]); an imaging system for capturing images of the reflected light (an image capture system configured to image the bottom of a patient’s foot, [0038], total internal reflection based image capture, [0106]); and a computing device (image data processor, [0053]-[0054], [0057], [0134]) for generating a pressure distribution map of the body part on the surface based on the images (total internal reflection image is processed to map a pressure distribution of the foot on the transmissive sheet/surface, [0156]-[0157]). Regarding claim 2 , O’Connor discloses the light source emits a combination of red, blue, and green light (light source can emit a combination of red, green, and blue light, [0066]). Regarding claim 3 , O’Connor discloses force sensors for measuring force applied by the body part to the surface (strain gauges for measuring patient weight/load, [0144], [0146], [0150], [0158]). Regarding claim 4 , O’Connor discloses the computing device generates the pressure distribution map based on the images and the measured force (absolute values of pressure of the pressure distribution map are determined using the relative pressures from the total internal reflection image and the patient weight/load measured using the strain gauges, [0158]). Regarding claim 6 , O’Connor discloses the pressure distribution map plots force versus contact area for the body part across the surface (pressure is determined by force distributed over area, [0138]; total internal reflection image is processed to map a pressure distribution of the foot on the transmissive sheet/surface, [0156]-[0157]). Regarding claim 7 , O’Connor discloses the surface is planar (a transmissive sheet with an upper surface configured to accommodate a sole of the foot, Abstract; patient contact surface is a planar sheet, [0039], [0048], Fig. 1). Regarding claim 11 , O’Connor discloses the computing device is configured to generate a spatial mechanical properties map of the body part based on the images (total internal reflection image is processed to map a pressure distribution of the foot on the transmissive sheet/surface, [0156]-[0157]; see also mapping tissue parameters such moisture, temperature, tissue color, and perfusion to assess circulation in the foot, Abstract, [0008], [0037], [0135], [0137], [0138], [0144], [0146]-[0149], [0153], [0160], [0176]). Regarding claim 12 , O’Connor discloses the images comprise a distribution of light intensity and different light colors (light source can emit a combination of red, green, and blue light of which the brightness of the reflected light is captured in the images, [0066]; camera images capture photons of light, [0078], [0126]; total internal reflectance image captures brightness, [0157], [0158], [0176]). Claims 13, 16-18, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by O’Connor. Regarding claim 13 , O’Connor discloses a method for analyzing a body part of a subject (a method for imaging a human foot, Abstract), comprising: receiving the body part on a contact surface of a sensing interface (a transmissive sheet with a n upper surface configured to accommodate a sole of the foot, Abstract); emitting light having multiple wavelengths within the sensing interface (a light source positioned below the sheet for emitting light toward the sheet, Abstract; light source emits light having multiple wavelengths, [0040]); acquiring images of the light reflected by the body part interacting with the contact surface (an image capture system configured to image the bottom of a patient’s foot, [0038], total internal reflection based image capture, [0106]); and generating a pressure distribution map of the body part on the contact surface based on the images (total internal reflection image is processed to map a pressure distribution of the foot on the transmissive sheet/surface, [0156]-[0157]). Regarding claim 16 , O’Connor discloses generating a spatial mechanical properties map of the body part based on the images (total internal reflection image is processed to map a pressure distribution of the foot on the transmissive sheet/surface, [0156]-[0157]; see also mapping tissue parameters such moisture, temperature, tissue color, and perfusion to assess circulation in the foot, Abstract, [0008], [0037], [0135], [0137], [0138], [0144], [0146]-[0149], [0153], [0160], [0176]). Regarding claim 17 , O’Connor discloses generating a perfusion map assessing blood circulation in the body part based on the spatial mechanical properties map (mapping tissue parameters such moisture, temperature, tissue color, and perfusion to assess circulation in the foot, Abstract, [0008], [0037], [0135], [0137], [0138], [0144], [0146]-[0149], [0153], [0160], [0176]). Regarding claim 18 , O’Connor discloses assessing blood circulation in the body part based on a distribution of different colored light in the reflected light images (mapping tissue parameters such moisture, temperature, tissue color, and perfusion to assess circulation in the foot, Abstract, [0008], [0037], [0135], [0137], [0138], [0144], [0146]-[0149], [0153], [0160], [0176]; light source can emit a combination of red, green, blue, infrared and infra-red light sources for capturing images, [0040], [0066], [0091], [0097], [0129], [0132], [0146]-[0148]). Regarding claim 21 , O’Connor discloses the contact surface is planar (a transmissive sheet with an upper surface configured to accommodate a sole of the foot, Abstract; patient contact surface is a planar sheet, [0039], [0048], Fig. 1). 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over O’Connor as in claim 1 above, and further in view of Koh et al. (U.S. Pub. No. 2022/0270297), hereinafter “Koh.” Regarding claim 5, while O’Connor discloses analyzing at least one factor extracted from at least one of the images (features are extracted from the images of the patient’s foot and analyzed using trained machine learning algorithms, [0139], [0176]), O’Connor does not appear to disclose a trained machine learning algorithm to generate the pressure distribution map based on at least one factor extracted from at least one of the images. However, in the same field of endeavor of predicting pressure maps, Koh teaches a trained machine learning algorithm to generate the pressure distribution map based on at least one factor extracted from at least one of the images (a trained deep learning network generates a pressure map based on features extracted from images of the patient’s foot, Abstract, [0007]-[0009]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Koh’s known technique of extracting features from images of the patient’s foot and using these features in a trained deep learning network to generate a pressure map to O’Connor’s known system of capturing images of the foot and generate a pressure map therefrom and analysis of image features using trained machine learning algorithms to achieve the predictable result that the accuracy of the predicted pressure map may be increased by using large populations for training that better reflect an expert’s knowledge and allowing for the incorporation of feedback to improve the algorithm. See, e.g., Koh, [0100] and [0102]. Additionally, see also machine learning allowing for significant reduction of the number of images required to predict the pressure map, e.g., Koh, [0111]. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over O’Connor as in claim 13 above, and further in view of Koh. Regarding claim 14 , while O’Connor discloses analyzing at least one factor extracted from at least one of the images (features are extracted from the images of the patient’s foot and analyzed using trained machine learning algorithms, [0139], [0176]), O’Connor does not appear to disclose inputting the at least one factor into a trained machine learning algorithm to generate the pressure distribution map. However, in the same field of endeavor of predicting pressure maps, Koh teaches extracting at least one factor from at least one of the images (a trained deep learning network generates a pressure map based on features extracted from images of the patient’s foot, Abstract, [0007]-[0009]); and inputting the at least one factor into a trained machine learning algorithm to generate the pressure distribution map (a trained deep learning network generates a pressure map based on features extracted from images of the patient’s foot, Abstract, [0007]-[0009]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Koh’s known technique of extracting features from images of the patient’s foot and using these features in a trained deep learning network to generate a pressure map to O’Connor’s known process of capturing images of the foot and generate a pressure map therefrom and analysis of image features using trained machine learning algorithms to achieve the predictable result that the accuracy of the predicted pressure map may be increased by using large populations for training that better reflect an expert’s knowledge and allowing for the incorporation of feedback to improve the algorithm. See, e.g., Koh, [0100] and [0102]. Additionally, see also machine learning allowing for significant reduction of the number of images required to predict the pressure map, e.g., Koh, [0111]. Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over O’Connor as in claim 13 above, and further in view of Freeman et al. (U.S. Pub. No. 2015/0133754), hereinafter “Freeman.” Regarding claim 20 , O’Connor discloses a first pressure distribution map is generated before a treatment of the body part begins and a second pressure distribution map is generated after the treatment of the body part begins (obtain index maps before and after treatment to select, plan, monitor, and/or assess therapy, [0038], [0118]-[0119], [0120], [0183], [0226]-[0227], [0247]; index maps include pressure information of a patient’s foot, Abstract; instrument for generating the index map, [0087]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Freeman’s known technique of obtaining index maps including pressure information of a patient’s foot before and after treatment to O’Connor’s known process of obtaining pressure distribution maps of a patient’s foot to achieve the predictable result that having tissue information before treatment is just as important as monitoring the tissue information during therapy in ensuring optimal treatment. See, e.g., Freeman, [0038]. Allowable Subject Matter Claim 19 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. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. O’Connor et al. (U.S. Pub. No. 2014/0121535) discloses a planar platform comprising multi-wavelength light sources and a multi-wavelength camera to capture intensity of reflected light from the feet to generate a pressure distribution map of the foot surface. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT Johnathan Maynard whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-7977 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 10 AM - 6 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, FILLIN "SPE Name?" \* MERGEFORMAT Keith Raymond can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-270-1790 . 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. /J.M./ Examiner, Art Unit 3798 /KEITH M RAYMOND/ Supervisory Patent Examiner, Art Unit 3798
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Prosecution Timeline

Oct 30, 2023
Application Filed
Dec 18, 2025
Non-Final Rejection — §102, §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
39%
Grant Probability
46%
With Interview (+6.9%)
3y 10m
Median Time to Grant
Low
PTA Risk
Based on 189 resolved cases by this examiner. Grant probability derived from career allow rate.

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