Prosecution Insights
Last updated: April 19, 2026
Application No. 17/238,024

METHODS AND SYSTEMS FOR CHARACTERIZING TISSUE OF A SUBJECT UTILIZING MACHINE LEARNING

Non-Final OA §103
Filed
Apr 22, 2021
Examiner
CATTUNGAL, SANJAY
Art Unit
3798
Tech Center
3700 — Mechanical Engineering & Manufacturing
Assignee
Stryker Corporation
OA Round
6 (Non-Final)
83%
Grant Probability
Favorable
6-7
OA Rounds
3y 3m
To Grant
94%
With Interview

Examiner Intelligence

Grants 83% — above average
83%
Career Allow Rate
850 granted / 1024 resolved
+13.0% vs TC avg
Moderate +12% lift
Without
With
+11.5%
Interview Lift
resolved cases with interview
Typical timeline
3y 3m
Avg Prosecution
28 currently pending
Career history
1052
Total Applications
across all art units

Statute-Specific Performance

§101
2.4%
-37.6% vs TC avg
§103
36.1%
-3.9% vs TC avg
§102
38.7%
-1.3% vs TC avg
§112
7.2%
-32.8% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1024 resolved cases

Office Action

§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 . Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/05/26 has been entered. 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, 7-14, 17-24, and 27-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over U. S. Publication No. 2010/0036217 to Choi et al in view of U. S. Publication No. 2018/0204111 to Zadeh et al.; U. S. Publication No. 2008/0139920 to Bigilieri et al.; and further in view of U. S. Publication No. 2011/0246413 to Shibayama et al. Regarding Claims, 1, 11, and 21, Choi teaches a system, method and program comprising a computer system having one or more processors: receiving time series data of fluorescence from tissue of the subject, the time series data related to perfusion of the tissue and the time series data being or having been captured by an image capture system (abstract and claim 1 teaches time series data of fluorescence imaging representing perfusion for tissue diagnosis) . Choi does not expressly teach classifying a plurality of regions in the time series data, wherein the plurality of regions are classified using one or more machine learning models; and plurality of regions are classified by providing a numerical value representing a condition of the tissue. Zadeh teaches classifying a plurality of regions in the time series data, wherein the plurality of regions are classified using one or more machine learning models (abstract; para 0114, 0125 and 02431 teaches classifying images using machine learning models). It would be obvious to one of ordinary skill in the art at the time of filing to modify Choi with a setup such that the plurality of regions are classified using machine learning models as taught by Zadeh, since such a setup would result in a more precise and fast analysis of the images. Choi and Zadeh teaches all of the above claimed limitations but does not expressly teach plurality of regions are classified by providing a numerical value representing a condition of the tissue. Bigilieri teaches plurality of regions are classified by providing a numerical value representing a condition of the tissue (para 0051 teaches assigning numerical values to inflammatory condition of tissues on a numeric scale). It would be obvious to one of ordinary skill in the art at the time of filing to modify Choi and Zadeh with a numerical value representing a condition of tissue as taught by Bigilieri, since such a setup would result in easy observation of different tissue conditions by the doctor. Furthermore, it would also speed up the time spent by doctors on evaluating images. Choi, Zadeh, and Bigilieri teaches all of the above claimed limitations but does not expressly teach classifying the plurality of regions in the time series data comprises providing a risk estimate for the plurality of regions. Shibayama teaches classifying the plurality of regions in the time series data comprises providing a risk estimate for the plurality of regions (para 0050 teaches a recurrence risk score). It would be obvious to one of ordinary skill in the art at the time of filing to modify Choi, Zadeh, and Bigilieri with a setup to provide a risk estimate, as taught by Shibayama, since such a setup would result in easy and faster diagnosis. Regarding Claim 2-4, 12-14, and 22-24, Zadeh teaches that the predicted tissue condition comprises inflammation, malignancy, abnormality or disease (para 02431 teaches caner/tumor detection). Regarding Claim 7, 17, and 27, Choi teaches that the time series data comprises raw data, pre-processed data, or a combination thereof (abstract; para 0046 and claim 1 teaches time series data is processed data). Regarding Claim 8, 18, and 28, Zadeh teaches that the time series data comprises pre-processed data that has been pre-processed by applying data compression, principal component analysis, autoencoding, or a combination thereof (para 02267 teaches principal component analysis). Regarding Claim 9, 19, and 29, Zadeh teaches that the plurality of regions are classified using an unsupervised clustering algorithm (para 02100 teaches classification using clustering algorithm). Regarding Claim 10, 20, and 30, Choi teaches that the time series data of fluorescence from tissue of the subject comprises fluorescence intensity data (para 0046 teaches fluorescence intensity data). Response to Arguments Applicant’s arguments, see Remarks, filed 11/04/25, with respect to the rejection(s) of claim(s) 1-4, 7-14, 17-24 and 27-30 under 103 have been fully considered but are moot in view of new grounds of rejection. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANJAY CATTUNGAL whose telephone number is (571)272-1306. The examiner can normally be reached M-F 9-5 EST. 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, Keith Raymond can be reached on 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. /SANJAY CATTUNGAL/Primary Examiner, Art Unit 3793
Read full office action

Prosecution Timeline

Apr 22, 2021
Application Filed
Jun 07, 2021
Response after Non-Final Action
Nov 18, 2023
Non-Final Rejection — §103
Feb 21, 2024
Applicant Interview (Telephonic)
Feb 24, 2024
Examiner Interview Summary
Feb 26, 2024
Response Filed
Mar 26, 2024
Non-Final Rejection — §103
Jul 01, 2024
Response Filed
Aug 21, 2024
Final Rejection — §103
Dec 27, 2024
Request for Continued Examination
Jan 07, 2025
Response after Non-Final Action
Feb 21, 2025
Non-Final Rejection — §103
May 27, 2025
Response Filed
Sep 01, 2025
Final Rejection — §103
Nov 04, 2025
Response after Non-Final Action
Jan 05, 2026
Request for Continued Examination
Jan 21, 2026
Response after Non-Final Action
Jan 24, 2026
Non-Final Rejection — §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

6-7
Expected OA Rounds
83%
Grant Probability
94%
With Interview (+11.5%)
3y 3m
Median Time to Grant
High
PTA Risk
Based on 1024 resolved cases by this examiner. Grant probability derived from career allow rate.

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