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 .
In a RCE and Amendments dated 05/01/2026, applicant(s) amended claims 1, 9 and 18. Claims 1 – 3, 5 – 9 and 11 – 20 are still pending in this application.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/01/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1 – 3, 5 – 9 and 11 – 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1 - 3, 5 - 9 and 11 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (U.S PreGrant Publication No. 2020/0349434 A1, hereinafter ‘Zhang’) in view of Lyman et al. (U.S PreGrant Publication No. 2020/0161005 A1, hereinafter ‘Lyman’).
With respect to claim 1, Zhang teaches a computing apparatus (e.g., an apparatus, ¶0025) operated by at least one processor (e.g., at least a processor, ¶0008), the computing apparatus comprising: a memory (e.g., a memory, ¶0008) configured to store instructions (e.g., configured to store computer executable components , ¶0008); and at least one processor (e.g., said processor, ¶0008) configured to execute the instructions (e.g. configured to execute/run said computer executable components , ¶0008), wherein the at least one processor is configured, by executing the instructions, to execute: a target artificial intelligence model (e.g., an AI model, ¶0033) configured to learn at least one task (e.g., configured to learn at least a task, ¶0025, ¶0033, ¶0035), and perform a task for an input medical image to output a result (e.g., perform at least the task for the medical image(s) to provide/output a result, ¶0025, ¶0028, ¶0033 - ¶0035, ¶0039); and a confidence prediction model configured to obtain at least one impact factor that affects result (e.g. a ML model, for prediction, configured to obtain at least condition (e.g. presence or absence) that affects the result based on the inputted medical image, ¶0026 - ¶0030, ¶0034, ¶0039), and estimate confidence information for the result (e.g., measure confidence score for the result based on the influence/difference between probabilities, ¶0009, ¶0048), but fails to teach that said task is performed for an input medical image to output an analysis result for the input medical image; and said confidence prediction model configured to obtain at least one impact factor that affects the analysis result, and estimate confidence information for said analysis result using the at least one impact factor, wherein the at least one impact factor comprises at least one of: a first impact factor extracted from additional information of the input medical image; and a second impact factor inferred from the input medical image.
However, the aforementioned claim limitations are well-known in the art as evidenced by Lyman. In particular, Lyman teaches perform a task for an input medical image to output an analysis result for the input medical image; and a confidence prediction model configured to obtain at least one impact factor that affects the analysis result, and estimate confidence information for the analysis result using the at least one impact factor, wherein the at least one impact factor comprises at least one of: a first impact factor extracted from additional information of the input medical image; and a second impact factor inferred from the input medical image (Lyman: e.g. perform a remediation for an input of a medical scan to output a result/report of said input, and predict abnormality that are present, and calculate/compute confidence scores for the result based on at least an impact, ¶0078, ¶0087 ¶0112 - ¶0115 and ¶0136; said impact can be any influence or data attributes extracted from information of the input of the medical scan, ¶0069 - ¶0071, ¶0115 - ¶0118 with ¶0416 - ¶0417; and/or influence, impact or data attributes inferred from said medical scan, ¶0058 - ¶0059, ¶0121 - ¶0124, ¶0145 - ¶0150 & ¶0154).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the apparatus of Zhang as taught by Lyman since Lyman suggested within ¶0078, ¶0087 ¶0112 - ¶0115 and ¶0136; ¶0069 - ¶0071, ¶0115 - ¶0124, ¶0145 - ¶0154 with ¶0416 that such modification would greater improvement over existing systems, allowing more precise inference data to be generated by utilizing particular models trained to process the type of scan presented and/or trained to process a particular type of detected abnormality in the type of scan presented.
With respect to claim 2, Zhang in view of Lyman teaches the computing apparatus of claim 1, wherein the at least one impact factor is determined based on a characteristic of the target artificial intelligence model and/or a characteristic of the input medical image (e.g., in various embodiments, the data samples comprise images and the ML model comprises an inferencing model configured to automatically classify the images and/or features in the images. For instance, in some implementations, the ML model can be or include a medical image processing model configured to classify presence or absence of a medical condition in medical images, a state of a medical condition reflected in medical images, and the like. In accordance with these embodiments, the standard feature space can comprise a plethora (e.g., thousands, millions, etc.) of annotated images of various objects with various features, such as ImageNet. ImageNet is an open source image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. Thus, in some embodiments, in which the ML model is configured to perform an image-based classification on image data samples, the disclosed techniques can employ the ImageNet feature space as the standard feature space (e.g., an ImageNet feature space based on the VGG16 network). However, various other standard features spaces can be utilized, ¶0028).
With respect to claim 3, Zhang in view of Lyman teaches the computing apparatus of claim 1, wherein the at least one impact factor comprises at least one of: a task-related medical factor of the target artificial intelligence model; an input image-related factor of the target artificial intelligence model; a disease-related factor detected by the target artificial intelligence model; a patient-related demographic factor; or a patient characteristic-related factor (e.g. several tasks are taught within Zhang’s, wherein at least a task is related to medical processing of the AI model, ¶0033 - ¶0035).
With respect to claim 5, Zhang in view of Lyman teaches the computing apparatus of claim 1, wherein the at least one first impact factor extracted from the additional information of the input medical image comprises at least one of age, gender, race, regional characteristics, or imaging details including an imaging method (e.g., the system of Lyman can review or edit measurement data in order to provide diagnosis result, ¶0046 - ¶0047, ¶0051, ¶0099), and wherein the at least one second impact factor inferred from the input medical image comprises at least one of tissue density, presence of an object in the input medical image, resolution, image quality, a lesion type, or a change in lesion size (e.g., another feature is a user is prompted to draw a diameter for a lesion presented to the user, indicate endpoints of a diameter of a lesion presented to the user, and/or otherwise provide measurement data for a lesion presented to the user. The user can be prompted to provide and/or edit segmentation data for the detected abnormality, where the user is prompted to draw and/or indicate vertices for a polygon surrounding the detected abnormality. These selections can be included in the additional annotation data, ¶0455).
With respect to claim 6, Zhang in view of Lyman teaches the computing apparatus of claim 1, wherein the target artificial intelligence model comprises a model trained to detect a lesion from a medical image or to infer medical diagnostic information or treatment information (e.g., said AI model is also configured to detect anomaly from the medical image(s), ¶0035).
With respect to claim 7, Zhang in view of Lyman teaches the computing apparatus of claim 1, wherein Lyman teaches the confidence prediction model comprises a model that has learned a relationship between at least one impact factor associated with a medical image for training and a confidence score for an analysis result inferred from the medical image for training (e.g., One of the models is a model that has learned a relationship between an abnormality associated with said medical scan and confidence score for analysis report, ¶0081 - ¶0084 and ¶0087 - ¶0088).
With respect to claim 8, Zhang in view of Lyman teaches the computing apparatus of claim 1, wherein the confidence information for the target result is: provided together with the analysis result; used for correction of the analysis result; used as an indicator for recommending to retake the input medical image and/or for recommending an imaging method; or used as an indicator for discarding the analysis result output from the target artificial intelligence model (e.g., this is simply a result upon using confidence score in order to allow a medical staff or clinician(s) to evaluate (e.g., the medical staff may review, correct, retake or discard as desired per quality), ¶0043, ¶0061, ¶0078, ¶0083).
With respect to claim 9, this is a method claim corresponding to the apparatus claim 1. Therefore, this is rejected for the same reasons as the apparatus claim 1.
With respect to claim 10, this is a method claim corresponding to the apparatus claim 4. Therefore, this is rejected for the same reasons as the apparatus claim 4.
With respect to claim 11, this is a method claim corresponding to the apparatus claim 5. Therefore, this is rejected for the same reasons as the apparatus claim 5.
With respect to claim 12, Zhang in view of Lyman teaches the method of claim 9, wherein the obtaining the at least one impact factor comprises: in response to the medical image being a mammogram image, determining density inferred from the mammogram image as the at least one impact factor; or in response to the medical image being a chest X-ray image, determining posterior anterior (PA) or anterior-posterior (AP) information extracted from additional information of the chest X-ray image as the at least one impact factor (e.g., refer to ¶0035 wherein teaches several clinical practices to medical imaging processing).
With respect to claim 13, Zhang in view of Lyman teaches the method of claim 9, further comprising: correcting confidence information for the analysis result based on the analysis result; and providing the corrected confidence information as final confidence information for the analysis result (e.g., this is simply a result upon using confidence score in order to allow a medical staff or clinician(s) to evaluate (e.g., the medical staff may review, correct, update, take additional medical scans or delete/reject as desired per quality), ¶0043, ¶0061, ¶0078, ¶0083).
With respect to claim 14, Zhang in view of Lyman teaches the method of claim 9, wherein Lyman further teaches comprising providing the confidence information together with the analysis result (e.g., the confidence score along with analysis, ¶0087, ¶0095).
With respect to claim 15, Zhang in view of Lyman teaches the method of claim 9, further comprising correcting the target result based on the confidence information for the analysis result (e.g., this is simply a result upon using confidence score in order to allow a medical staff or clinician(s) to evaluate (e.g., the medical staff may review, correct, retake or discard/reject as desired per quality), Col 29 (lines 38 – 53)).
With respect to claim 16, Zhang in view of Lyman teaches the method of claim 9, further comprising discarding the analysis result in response to the confidence information for the analysis result being equal to or less than a reference (e.g., this is simply the result upon using confidence score that less or equal to a defined threshold in order to allow a medical staff or clinician(s) to evaluate (e.g., the medical staff may review, correct, retake or discard/reject as desired per quality), ¶0048, ¶0061, ¶0078, ¶0083).
With respect to claim 17, Zhang in view of Lyman teaches the method of claim 9, wherein Lyman further teaches comprising based on the confidence information for the analysis result, requesting to retake the medical image input to the target artificial intelligence model or recommending an imaging method (e.g., If confidence score is too low, the user can preview the result in order to take further action (e.g. edit, confirm, etc.), ¶0098 - ¶0099, ¶0124, ¶0149).
With respect to claim 18, it's rejected for the similar reasons as those described in connection with claim 1 or 9; in addition, Lyman teaches that the impact factor that affect an analysis result is performed on a medical image by a target artificial intelligence model (e.g., an abnormality classifier categories can correspond to Response Evaluation Criteria in Solid Tumors (RECIST) eligibility and/or RECIST evaluation categories. For example, an abnormality classifier category 444 corresponding to RECIST eligibility can have corresponding abnormality classification data 445 indicating a binary value “yes” or “no”, and/or can indicate if the abnormality is a “target lesion” and/or a “non-target lesion.” As another example, an abnormality classifier category 444 corresponding to a RECIST evaluation category can be determined based on longitudinal data 433 and can have corresponding abnormality classification data 445 that includes one of the set of possible values “Complete Response”, “Partial Response”, “Stable Disease”, or “Progressive Disease, ¶0081, ¶0112 - ¶0118 with ¶0128 - ¶0130 and ¶0138).
With respect to claim 19, Zhang in view of Lyman teaches the computing apparatus of claim 18, wherein the at least one processor is further configured to: receive the medical image; obtain the at least one impact factor determined based on a characteristic of the target artificial intelligence model and/or a characteristic of the medical image; and estimate the confidence information for the analysis result based on the at least one impact factor (e.g., input data are received to use the ML model and compute/calculate/estimate confidence score, Col 28 (lines 12 – 40); Col 29 (lines 17 – 66); Col 58 (lines 10 – 35); Col 112 (line 49) to Col 113 (line 9), Col 144 (lines 10 - 34), Figs. 19, 20 & 32).
With respect to claim 20, Zhang in view of Lyman teaches the computing apparatus of claim 19, wherein the at least one processor is further configured to perform at least one of: correcting the confidence information for the analysis result based on the analysis result, and providing the corrected confidence information as final confidence information for the target result; correcting the analysis result based on the confidence information for the analysis result and providing the corrected analysis result; based on the confidence information for the target result, requesting to retake the medical image or recommending an imaging method; or discarding the analysis result in response to the confidence information for the target result being equal to or less than a reference (e.g., this is simply a result upon using confidence score in order to allow a medical staff or clinicians to evaluate (e.g., the medical staff or clinician may review, correct, retake or discard as desired per quality), Col 28 (lines 12 – 40); Col 29 (lines 17 – 66); Col 58 (lines 10 – 35); Col 112 (line 49) to Col 113 (line 9), Col 144 (lines 10 - 34), Figs. 19, 20 & 32).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN M GUILLERMETY whose telephone number is (571)270-3481. The examiner can normally be reached 9:00AM - 5:00PM.
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/JUAN M GUILLERMETY/Primary Examiner, Art Unit 2682
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