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
The amendment and written response filed 04/30/2026 have been entered and considered.
Claims 21, 37 and 39 were amended.
Claim 24 was cancelled.
Claims 21-23 and 25-41 are pending.
Response to Arguments
Applicant's arguments filed 04/30/2026 have been fully considered but they are not persuasive.
The combination of Tran, Cumming and Chou:
Applicant argues the Tran reference teaches a “geometrical measurement” that “could be taken from the captured images” in imaging of the eardrum. The applicant further argues “neither Tran’s prospective ‘geometrical measurement’ remark nor its eardrum pattern recognition disclosure teaches or suggests calculating a body contour feature, a silhouette area, a perimeter, or an aspect ratio of a biological sample in an extracted region of interest containing only the biological sample”. Upon further review of the reference, and in light of applicant’s arguments, the examiner respectfully disagrees as follows: Tran in paragraph [0023] teaches “Preprocessing steps, such as shade-correction and matched filter post-processing to this basic framework, can improve performance. Algorithms of this kind function by detecting candidate microaneurysms of various shapes, based on their response to specific image filters”. The examiner notes the detection of candidate microaneurysms of various shapes requires at least one “geometrical measurement” of “body contour feature”. And the examiner notes as the candidates are related to retinal vessels, which is an “external physiological area” in an eye. With regard to the argument of “adaptor” in Tran as “an intermediate viewing aid”, the examiner notes the Chou reference provides teaching of using a high-resolution iPhone camera instead of microscopic image sensor to capture high resolution images as long as the camera is able to provide high resolution image data. Thus, when the smartphone camera’s quality is high enough, the camera adaptor in Tran is no longer required for “detecting candidate microaneurysms of various shapes”. In conclusion, the combination of Tran, Cumming and Chou still teaches all the limitations in independent claims 21, 37 and 39.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/29/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 a geometric attribute in the first region of interest to identify one or more features of the biological sample (Tran in paragraph [0023] teaches “Preprocessing steps, such as shade-correction and matched filter post-processing to this basic framework, can improve performance. Algorithms of this kind function by detecting candidate microaneurysms of various shapes, based on their response to specific image filters, which corresponds to at least one geometric attribute of body contour feature); 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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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.
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/TOM Y LU/Primary Examiner, Art Unit 2667
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