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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/25/2024 was filed after the mailing is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 03/28/2025 was filed after the mailing 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 § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (Mathematical concepts) without significantly more. Examiner views claim 1 and 11’s training as a mathematical process as shown by claim 2 and 12.. The dependent claims which outline claim 1’s training recite a loss function used for training (claims 2 and 12), an equation that expresses the variable, “Lmatch” (claim 3 and 13), a threshold “γt” of the previous equation (Claim 4 and 14), an equation that defines “γt” (claim 5 and 15 ), and an equation that expresses “Lcc” (Claim 7 and 17 ). This judicial exception is not integrated into a practical application because the loss function and the expression of its variables, which are a mathematical concepts, describe how instructions given in a computerized environment are mapped and interacted with. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a formula standing alone in a computerized environment directed towards an object detection model’s confidence score calibration is mere instructions given to a processor to execute said formulations or the methodology that would inherently need to be ran in a computerized environment via instructions given to a processor. Moreover, the expressions of the variables in claims 3 and 13, and 17 and 7 are just mathematical definitions of the variables within claim 2. Similarly , claims 4-5 and 14-15 are definitions of threshold yt previously presented in claim 3. The claims above do not transform the abstract idea into a practical, patentable application nor do they reflect improvement in the functioning of a computer nor an object detection model. Furthermore claims 6, 8, 9, 10, 16 18, 19, and 20 all rely on the same training logic which is at its root an abstract idea directed towards a mathematical concept and are also subsequently rejected.
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.
(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1 and 11 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Patel et al (Patel hereinafter US 20240070516 A1)
As per claim 1
Patel teaches training an object detection model to generate confidence scores using calibration that is based on confidence, correlation, and matching (Figure 1, Figure 2, Figure 5, Figure 8 Paragraph [0022] “In some implementations, a clustering algorithm that clusters based on embedding information is applied to object instances that have substantially lower calibrated confidence scores (as compared to their original confidence scores) in order to identify commonly occurring types of ground truth annotation inconsistencies” ) with accuracy of a location of bounding boxes being used with accuracy of object labels to keep the confidence scores close to an actual probability of correctness (Figure 2, Figure 5 Paragraph [0040] In some embodiments, the confidence calibrator 212 is trained using a binary classification process… For each object instance produced from an input image, the object instance prediction model 210 computes coordinates of a bounding box 222, an original confidence score 226, an instance embedding information 230, and optionally a classification 224, which is input to the confidence calibrator 212 under training. The confidence calibrator 212 under training generates a calibrated confidence score which can be compared to a labeled ground truth image of the input image to compute a binary classification score…given the predicted bounding box coordinates 222, a binary classification score of 1 is assigned if the labeled ground truth image has a corresponding bounding box (e.g., a bounding box generally at the same coordinates and/or same classification) and a score of 0 if there is no corresponding bounding box found in the labeled ground truth image…binary classification score is fed back as a training correction for the confidence calibrator 212” Paragraph [0044] The machine learning model under training generates an accuracy prediction (a calibrated confidence score), and the location of the predicted bounding box is compared to a labeled ground truth image of the input image to the first machine learning model to compute a binary classification score”) performing object detection on an image using the object detection model to generate a bounding box around an object (Figure 3B, Figure 5) a label for the object, and a confidence score (Figure 2. Paragraph [0033] “The object instance prediction model 210 generates, for each object instance 225, coordinates of a bounding box 222 (with reference to the input image), a classification 224 of the object within a bounding box, and a confidence score 226 that indicates a probability of the object instance prediction being correct.” ) performing an action responsive to the object and the confidence score. (Paragraph [0036] “ As such, in embodiments, the application 110 inputs and utilizes the calibrated confidence scores 228 to make better informed determinations regarding whether an object instance 225 is a sufficiently accurate prediction to use. In some embodiments, such as discussed below, the application 110 uses both the calibrated confidence score 228 and original confidence score 226 for an object instance 225 to make further determinations regarding a predicted object instance 225.”)
As per claim 11
Claim 11 is the system claim that parallels claim 1’s method claim and will be rejected under the same premise.
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 9, 10, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Patel et al (Patel hereinafter US 20240070516 A1) in view of Abou Shousha et al (Abou Shousha hereinafter US 10468142 B1)
As per claim 9
Patel teaches all claim limitations previously rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Patel does not teach the object detection model is a machine learning model that is trained using medical image information and the label is used for medical decision making.
Abou Shousha teaches the object detection model is a machine learning model that is trained using medical image information (Paragraph 66: ‘the image data includes a temporal sequence of images taken at different time that capture how the cornea state has evolved. Such image data may be used with respect to AI models 12 trained to predict effective treatment, effectiveness of current or future treatments, recovery time, disease of condition, or other predictions “ Paragraph 73: “Tasks executed by the AI model 12 may include classification, object detection …object detection may include first finding the label of image then finding a bounding box that contain the object in the image. Semantic segmentation may include utilizing artificial intelligence to find a boundary of detected objects.” Paragraph 84: the analysis subsystem 10 may collect output class probabilities, associated condition or classification scores, or class labels and associate such probabilities or scores to semantic classifications for presentation to a user. The system 10 may also translate probabilities into confidence scores or other another user friendly format…. In one example, the analysis subsystem 10 may include one or more of the above in a health report .) and the label is used for medical decision making. (Figure 1I, Figure 1J, Figure 10, Figure 9, Paragraph 166: “Each label corresponds to a corneal diagnosis.”)
Accordingly, a person of ordinary skill in the art, at the time this invention was effectively filed, would have found it obvious to modify Patel’s methodology with Abou Shousha’s concept of using the label for medical decision making. A person of ordinary skill in the art knows that Patel is addressing machine-learning models used for detection and classification and their proneness to over-confident predictions. A person of ordinary skill in the art knows that overconfidence in object detection in medical imaging can lead to incorrect predictions and hide critical uncertainties. Because of this fact, person of ordinary skill in the art is aware that Patel’s methodology is critical and can be shifted in medical applications such as Abou Shousha’s who methodology feeds corneal/anterior-segment images or maps into AI models that learn disease patterns and output scores or predictions for diagnosis. Using Abou Shousha’s medical images as the data used in training in Patel’s object detection pipeline expands Patel’s generic /nonspecific object detection and confidence score calibration into shiftable and suitable range of application.
As per claim 10
Patel and Abou Shousha teach all claim limitations previously rejected in claim 9’s 103 rejection. See claim 9’s 103 rejection.
Abou Shousha teaches wherein action includes automatically altering a patient’s treatment responsive to the label and the confidence score. (Figure 1B Paragraph 84; As introduced above with respect to FIG. 1B, the system 10 may include an analysis subsystem 20 …the analysis subsystem 10 may collect output class probabilities, associated condition or classification scores, or class labels and associate such probabilities or scores to semantic classifications for presentation to a user. The system 10 may also translate probabilities into confidence scores or other another user friendly format. In one example, the analysis subsystem 10 may include one or more of the above in a health report “Paragraph 151 “after the system 10 generates a prediction of a disease of condition, the input data upon which the prediction in based may be automatically fed to submodels to generate ancillary aspect predictions corresponding to the disease or condition, such as severity, risk, action, treatment, progression, prognosis, etc. “ The label and confidence scores input that were used in AI model 12 within system 10 are also used in Analysis subsystem (also part of system 10) and are automatically fed into the subsystem and used for a modified/ancillary health report outputted by the analysis subsystem within system 20)
As per claim 19
Patel teaches all claim limitations previously rejected in claim 11’s 102 rejection See claim 11’s 102 rejection.
Claim 19 is the system claim that parallels method claim 9 and will be rejected under the same premise.
As per claim 20
Patel and Abou Shousha teach all claim limitations previously rejected in claim 19’s 103 rejection. See claim 19’s 103 rejection.
Claim 20 is the system claim that parallels method claim 10 and will be rejected under the same premise.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pathiraja et al (Pathiraja hereinafter “Multiclass Confidence and Localization Calibration for Object Detection”)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE WRENSFORD CODRINGTON whose telephone number is (571)272-8130. The examiner can normally be reached 8:00am-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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/SHANE WRENSFORD CODRINGTON/ Examiner, Art Unit 2667
/MATTHEW C BELLA/ Supervisory Patent Examiner, Art Unit 2667
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