Prosecution Insights
Last updated: April 19, 2026
Application No. 18/474,927

ANIMAL HEALTH ASSESSMENT

Non-Final OA §103
Filed
Sep 26, 2023
Examiner
LU, TOM Y
Art Unit
2667
Tech Center
2600 — Communications
Assignee
Ollie Pets Inc.
OA Round
5 (Non-Final)
88%
Grant Probability
Favorable
5-6
OA Rounds
2y 8m
To Grant
91%
With Interview

Examiner Intelligence

Grants 88% — above average
88%
Career Allow Rate
826 granted / 941 resolved
+25.8% vs TC avg
Minimal +3% lift
Without
With
+3.0%
Interview Lift
resolved cases with interview
Typical timeline
2y 8m
Avg Prosecution
23 currently pending
Career history
964
Total Applications
across all art units

Statute-Specific Performance

§101
12.6%
-27.4% vs TC avg
§103
28.7%
-11.3% vs TC avg
§102
37.2%
-2.8% vs TC avg
§112
11.6%
-28.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 941 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 . Response to Amendment Request of Continued Examination filed on 01/30/2026 has been entered. Upon entry of the RCE, the amendment and written response filed 01/30/2026 is now entered and considered. Claims 1-20 and 24 have been cancelled. Claims 21, 37 and 39 have been amended. Claim 41 has been added. Claims 21-23 and 25-41 are pending. Information Disclosure Statement The information disclosure statement (IDS) submitted on 01/30/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Response to Arguments Applicant’s arguments, see remarks, filed 01/30/2026, with respect to the rejections of claims 21-23 and 25-40 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Tran, Cumming and Chou et al (“Chou”, U.S. Publication No. 2025/0283819 A1). 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. Claims 21-23 and 25-41 are rejected under 35 U.S.C. 103 as being unpatentable over Tran (“Tran” hereinafter, U.S. Publication No. 2020/0405148 A1) in view of Cumming et al (“Cumming” hereinafter, U.S. Publication No. 2021/0248419 A1), and further in view of Chou et al (“Chou” hereinafter, U.S. Publication No. 2025/0283819 A1). As per claim 21, Tran discloses a method of analyzing an image taken without specialized equipment to provide a health assessment of a human in non-laboratory environments (abstract), the method comprising: receiving an image from an unmodified camera of a smartphone (the examiner notes the camera in Tran is a smartphone camera. Various kinds of adaptor 10 were able to attached to the camera to enhance the imaging, but the camera itself was never modified), the image including a biological sample of the human, wherein the image is a non-microscopic image of the biological sample, and wherein the biological sample includes an external physiological area of the human; (paragraph [0019] & figures 3A and 3B: “the user device” may be a “smart phone” with a camera for capturing images of an eye); identifying and extracting one or more regions of interest within the image for further analysis, the one or more regions of interest including at least a first region of interest having only the biological sample therein (paragraph [0054]: “A small color block 62 is shown in the picture 52, which indicates the area of focusing zone”, the focusing zone is the claim “region of interest”); calculating one or more of a geometric attribute, a texture attribute, and a color attribute in the first region of interest to identify one or more features of the biological sample (paragraph [0060]: “The phone cameras can register corneal curvature changes”; paragraph [0066]: “the smart-phone can be adjusted for a range of scope settings. For example, with ophthalmoscopes in particular, red filters may be used to decrease the percentage of color spectrum received that is in the red spectrum. This would increase image contrast while imaging the retina, as it is mostly pigmented red. Also, it is often difficult for a clinician to obtain a good view of the retina through an undilated pupil for long periods of time which would typically be required for good photography”); and without performing a laboratory analysis of the biological sample (the analysis in Tran is done on image data captured by a smartphone camera), applying a model to the one or more features of the biological sample, the model predicting a health characteristic of a human from which the biological sample originated (abstract & paragraph [0005]: deep learning machines), wherein the health characteristic is related to a weight of the human (paragraph [0301]: the machine learning model is also capable of predicting patient’s weight factor and recommending the patient for weight loss program) based on correlating image-based features with health characteristics using the model (as explained above in response to arguments, the neural network model is capable of using image feature vectors to identify a plurality of diseases). Tran teaches the above analysis for a human. However, Tran does not explicitly teach performing the analysis for an animal. Cumming teaches analyzing stool sample to detect parasite ova in an animal. At the time of the invention, it would have obvious to a person of ordinary skill in the art to modify Tran in light of Cumming’s teaching to apply the smartphone imaging for animals. One would be motivated to do so because it would extend Tran’s system to be applicable in the field of animal health. Tran teaches using smartphone with an additional adaptor 10 for capturing images of subjects/biological sample for medical diagnosis and treatment. However, Tran does not explicitly teach “without an intermediate viewing aid between a biological sample and the unmodified camera”. Chou in paragraphs [0185] & [0331] teaches using Iphone or fluorescent microscope for capturing biological sample at cellular level. At the time of the invention, it would have been obvious to a person of ordinary skill in the art modify Tran in view of Chou’s teaching to employ an Iphone or smartphone device that has an imaging sensor/camera with a high resolution. One would be motivated to do so because as the smartphone camera become more and more advanced, the image quality will be higher and higher, and it would no longer require an additional adaptor for image improvement as taught in Tran. As per claim 22, Tran teaches wherein the biological sample includes one or more of skin of the animal, fur of the animal, a portion of a mouth of the animal, a portion of an ear of the animal, a portion of an eye of the animal, and a portion of a nose of the animal (as explained above, Tran teaches imaging patient’s eye using a smartphone). As per claim 23, as explained above, Tran teaches an eye is a portion of the animal. As per claim 25, as explained above, Tran recommends weight loss treatment for patients in paragraph [0301]. As per claim 26, Tran teaches wherein the biological sample includes one or more of urine, vomit, bile, blood, and biological discharge (paragraph [0123]: “watery discharge”). As per claim 27, as explained above, Tran teaches an eye. As per claim 28, Tran teaches for Paget’s disease, the physician may need to examiner arms and legs. As per claim 29, Tran teaches imaging blood vessel in paragraph [0019] & [0021]. As per claim 30, Tran teaches retinal injury (paragraph [0012]). As per claim 31, Tran teaches treatment plan from physicians (paragraph [0012]) as well as recommending weight-loss program if required in paragraph [0301]. As per claim 32, Tran’s treatment plan from physician is personalized for each patient’s injury. As per claim 33, the treatment plan in Tran may be food, supplement or medicine. As per claim 34, as explained above, for eye injury, the treatment may be eye drops. As per claim 35, Tran teaches convolutional neural network (CNN) in paragraph [0047]. As per claim 36, Tran teaches patient history in paragraph [0174] and Cumming discloses receiving metadata associated with the image, the metadata including a questionnaire response related to one or more of a health, a behavior, a current diet, a supplement, a medication, ethnographic information, a breed, a of the animal, a weight of the stool sample, and a size of the animal, and wherein the metadata is used at least in part in predicting the health characteristic (paragraph [0205]: “the algorithm may accept information such as patient history, any existing condition or other infections, geographical location, age, ethnicity, species, breed or any other information which may be used to increase the precision of identification”). As per claim 37, see explanation in claim 21. The examiner notes Cumming’s system is a computer-like system, which inherently includes a non-transitory computer readable medium. As per claim 38, see explanation in claim 22. As per claim 39, see explanation in claim 21. The examiner notes Cumming’s system is a computer system, which is connected to a server. As per claim 40, see explanation in claim 22. As per claim 41, Chou in paragraph [0462] teaches comparing biological sample, epithelial cells, of a smoker with a non-smoker in an oral cancer diagnosis. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOM Y LU whose telephone number is (571)272-7393. The examiner can normally be reached Monday - Friday, 9AM - 5PM. 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, Matthew Bella can be reached at (571) 272 - 7778. 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. /TOM Y LU/Primary Examiner, Art Unit 2667
Read full office action

Prosecution Timeline

Sep 26, 2023
Application Filed
Feb 02, 2024
Response after Non-Final Action
Apr 20, 2024
Non-Final Rejection — §103
Aug 26, 2024
Response Filed
Nov 30, 2024
Final Rejection — §103
Mar 25, 2025
Request for Continued Examination
Mar 26, 2025
Response after Non-Final Action
May 03, 2025
Non-Final Rejection — §103
Jul 31, 2025
Response Filed
Nov 01, 2025
Final Rejection — §103
Jan 30, 2026
Request for Continued Examination
Feb 02, 2026
Response after Non-Final Action
Feb 25, 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

5-6
Expected OA Rounds
88%
Grant Probability
91%
With Interview (+3.0%)
2y 8m
Median Time to Grant
High
PTA Risk
Based on 941 resolved cases by this examiner. Grant probability derived from career allow rate.

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