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 .
Statement of positive recitation
Although claim 20 does not mention of non-transitory media, however, specification, paragraph n0057 recites, “Paragraph 0057, "Exemplary computing platforms also include program instructions executed by the processor 320 stored in the ROM 330, the RAM 340, and/or other forms of non-transitory storage media.".
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-9, 14-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lyu et al. (WO 2022067656 A1, IMAGE PROCESSING METHOD AND APPARATUS), hereinafter, “Lyu”, in view of Bort et al. (WO 2022219604 A1, PERSONALIZED HEART RHYTHM THERAPY ), hereinafter, “Bort”.
Regarding claim 1 Lyu teaches, determining one or more images of one or more regions of interest (ROIs) based on a first image of a subject, each of the one or more images of the one or more ROIs corresponding to one of the one or more ROIs (Please note, page 3, 6th. Paragraph. As indicated inputting at least one first feature map of a first image into a region of interest ROI detection model); determining, based on the one or more images of the one or more ROIs, one or more second images, each of the one or more second images corresponding to one of the one or more images of the one or more ROIs (Please note, page 3, 6th. Paragraph. As indicated based on the ROI detection model, outputting a second The feature map, the information of at least one ROI in the second feature map and the level corresponding to each ROI), wherein the image quality of the second image is higher than the image quality of the image of the ROI (Please note, page 8, first paragraph. As indicated the higher the level of the ROI, the higher the attention of the user, the lower the corresponding compression code rate, and the higher the image quality of the ROI in the second image); and obtaining a target image of the subject based on the one or more second images (Please note, page 9, second paragraph. As indicated marking the at least one ROI in the second image, and marking the level corresponding to each ROI).
Lyu does not expressly teach, utilizing on one or more trained machine learning models.
Bort teaches, one or more trained machine learning models utilization (Please note, paragraph 0035. As indicated the process uses analytical tools including signal processing, artificial intelligence and machine learning.).
Lyu & Bort are combinable because they are from the same field of endeavor.
At the time before the effective filing date, it would have been obvious to a person of ordinary skill in the art to utilize this one or more trained machine learning models of Bort in Lyu’s invention.
The suggestion/motivation for doing so would have been as indicated on paragraph 0035, “to detect organized patches”.
Therefore, it would have been obvious to combine Bort with Lyu to obtain the invention as specified in claim 1.
Regarding claim 2 Lyu teaches, wherein different images of ROIs in the one or more images of the one or more ROIs correspond to different regions in the first image, and the different regions partially overlap or the different regions do not overlap. (Please note, page 33, second paragraph. As indicated the NMS module mainly removes overlapping/redundant detection frames for multiple detection frames of the same target. Generally, only one detection frame is reserved for the same target to further achieve the accuracy of ROI detection).
Regarding claim 3 Bort teaches, wherein different images of ROIs in the one or more images of the one or more ROIs are processed by different trained machine learning models, the different trained machine learning models corresponding to different optimization directions. (Please note, paragraph 0074. As indicated supervised machine learning may include methods of training of models with training data that are associated with labels. Techniques in supervised machine learning may include methods that can classify a series of related or seemingly unrelated inputs into one or more output classes. Output labels are typically used to train the learning models to the desired output, such as favorable patient outcomes, accurate therapy delivery sites and so on.).
Regarding claim 4 Bort teaches, determining a first training dataset based on a plurality of pairs of sample images, each pair of the plurality of pairs of sample images including a sample first image and a sample second image of a sample subject, and determining the trained machine learning model by training the preliminary machine learning model based on the first training dataset. (Please note, paragraph 0075. As indicated to generate labels for an unlabeled dataset based on the portion of data that is labeled. Yet another approach is to use training from a different problem or a different dataset to generate labels for these data. Such techniques are used to improve the learning accuracy of models by creating “pseudo labels” for the unknown labels (an approach known as transductive learning) and to improve model learning by adding in more input to output examples (inductive learning).).
Regarding claim 5 Bort teaches, wherein one of the one or more trained machine learning model includes at least one of a U-Net model, a V-Net model, or a U-Net++ model. (Please note, paragraph 0073. As indicated another technique is to use autoencoders, to featurize and compress input data. Autoencoders are sometimes described as ‘self-supervised’ since the model input and output are the same.).
Regarding claim 6 Bort teaches, determining at least one of the one or more trained machine learning models corresponding to the one of the one or more images of the one or more ROIs based on at least one of user information or a feature of the ROI, wherein the feature of the ROI is determined based on the one of the one or more images of the one or more ROIs. (Please note, paragraph 0136. As indicated one or more supervised machine learning and statistical methods can be used to predict the arrhythmia origin including but not limited to neural networks, convolutional neural networks, recurrent neural networks, support vector machines, decision trees, discriminant analysis, naive bayes, and others. The input to the machine learning algorithms can be the voltage time-series data or features derived from the raw voltage time-series such as the aforementioned features.).
Regarding claim 7 Bort teaches, determining a plurality of third images corresponding to the one of the one or more images of the one or more ROIs based on the one or more trained machine learning models, different third images in the plurality of third images corresponding to different optimization directions and determining a second image corresponding to the one of the one or more images of the one or more ROIs based on the plurality of third images. (Please note, paragraph 0136. As indicated one or more supervised machine learning and statistical methods can be used to predict the arrhythmia origin including but not limited to neural networks, convolutional neural networks, recurrent neural networks, support vector machines, decision trees, discriminant analysis, naive bayes, and others. The input to the machine learning algorithms can be the voltage time-series data or features derived from the raw voltage time-series such as the aforementioned features. The output of the machine learning algorithms can be two- class (binary), multi-class, univariate, multivariate, or a combination of different output types.).
Regarding claim 8 Bort teaches, wherein determining an optimization parameter of at least one of the one or more trained machine learning models corresponding to the one of the one or more images of the one or more ROIs based on at least one of user information or a feature of the ROI, the optimization parameter being used to determine training data for training the at least one of the one or more trained machine learning models corresponding to the one of the one or more images of the one or more ROIs and/or as an input to the at least one of the one or more trained machine learning models corresponding to the one of the one or more images of the one or more ROIs. (Please note, paragraph 0074. As indicated supervised machine learning may include methods of training of models with training data that are associated with labels. Techniques in supervised machine learning may include methods that can classify a series of related or seemingly unrelated inputs into one or more output classes. Output labels are typically used to train the learning models to the desired output, such as favorable patient outcomes, accurate therapy delivery sites and so on.).
Regarding claim 9 Bort teaches, wherein the user information includes at least one of a user's primary treatment direction or the user's requirement for the image, and the feature of the ROI includes a type of the ROI, a parameter of an image, or surrounding tissue information of the ROI. (Please note, paragraph 0048. As indicated the directional guidance is tailored by additional data beyond recorded signals. Such data are created as personal digital records for an individual. The personal digital records may capture clinical, pathophysiological, laboratory, genetic or cellular data relevant to the disease being treated. This is pertinent to diseases with considerable variability in treatment outcome, such as heart rhythm disorders, that reflect varying patient profiles.).
Regarding claim 14 Bort teaches, determining the target image by performing a fusion operation on the one or more second images and the first image based on fusion coefficients each of which corresponds to one of the one or more second images. (Please note, paragraph 0319. As indicated throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component.).
Regarding claim 15 Bort teaches, determining the target image by performing a fusion operation on the one or more second images and the first image through a third trained machine learning model. (Please note, paragraph 0098. As indicated the computing server 140 may include one or more computing devices that operate one or more machine learning models 145 that may include one or more predictive models that analyze the information provided by the subject 105 and the physician 130 and data generated from the body surface device to generate recommendations such as therapy recommendations and predictions related to the subject’s conditions.).
Regarding claim 16 Bort teaches, determining, based on the first image, a sixth image, the sixth image being determined based on an overall processing model, wherein the overall processing model is a machine learning model; and obtaining the target image of the subject by performing the fusion operation on the one or more second images and the sixth image. (Please note, paragraph 0168. As indicated the device can accommodate a plurality of these critical regions, and thus be used by multiple operators in different patient types. Different critical region definitions may on occasion coincide in any given patient. For instance, in AF, sites of scar may be adjacent to sites of potential drivers. Several other potential coincident sites may occur and can be provided to the physician operator for him/her to make a decision on which to target.).
Regarding claims (17, 19, 20), analysis similar to those presented for claims (1, 16, 1), are applicable.
Allowable Subject Matter
Claims 10-13 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The closest applied Prior Art of record fails to disclose or reasonably suggest wherein in response to determining that a target ROI exists in the target image, adjusting the one or more trained machine learning models to obtain one or more updated trained machine learning models; determining, based on the one or more images of the one or more ROIs, one or more optimized images, each of the one or more optimized images corresponding to one of the one or more images of the one or more ROIs and being determined based on the one or more updated trained machine learning models; and obtaining an updated target image based on the one or more optimized images.
Examiner’s Note
The examiner cites particular figures, paragraphs, columns and line numbers in the references as applied to the claims for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claims, other passages and figures may apply as well.
It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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/AMIR ALAVI/Primary Examiner, Art Unit 2668 Monday, March 2, 2026