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 .
The action is responsive to the following communication: an application filed on 03/21/2024 where:
Claims 1-19, and 26 are currently pending.
Claims 20-25 and 27-29 are cancelled.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(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.
Claim(s) 1-19, and 26 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Robinson et al. (US 2021/0137384, hereinafter Robinson).
Regarding claim 1, Robinson teaches: A system (Abstract, disclosed herein are systems and method. See fig. 4, system 400) comprising:
a database ([0079], These scores can be stored in a database, see fig. 4, storage device 430); and
a computing device communicatively coupled to the database (see fig. 4, input device 445 connection to storage device 430) and configured to:
receive image data for an organ of a patient ([0073-0074], In some examples, the one or more input mappings can be one or more images from a singular patient. At block 105, at least one processor can receive one or more input mappings (e.g., of a corresponding ventricle, a corresponding atria, etc.).);
determine a recommended target area of the organ for treatment based on the image data ([0076, 0081, 0083, 0116], and see FIGS. 6A, 6B, and 6C are example outputs of the decision support module.);
generate recommended target data characterizing the recommended target area of the organ ([0085, 0087], see FIGS. 6A, 6B, and 6C, The at least one processor can determine one or more targets for ablation, for example, the target for ablation may include one or more cardiac arrhythmia target segments.); and
store the recommended target data in the database (fig. 2, steps 215 and 230, [0121], The data and/or ablation plan for each patient may be stored on a database to inform future patient treatment.).
Regarding claim 2, Robinson teaches: The system of claim 1, wherein the computing device is configured to: receive report data characterizing medical findings of the patient ([0093-0094], As additional patients are treated, the previous treatments can inform future treatments); and
determine the recommended target area based on the report data ([0093-0094], As additional patients are treated, the previous treatments can inform future treatments and can be used to adjust the target of the current patient (for example, by training the neural network). In another example, the target may be adjusted based on a weighting of input mappings or the confidence score of the combined mappings.).
Regarding claim 3, Robinson teaches: The system of claim 2, wherein the computing device is configured to determine the recommended target area by applying a text extracting process to the report data to identify text within the report data ([0093-0094], see fig. 6A. In an example, the decision support module may be displayed, printed, or provided to the physician in any form capable of providing the information. FIGS. 6A, 6B, and 6C are example outputs of the decision support module.).
Regarding claim 4, Robinson teaches: The system of claim 3, wherein the computing device is configured to determine the recommended target area based on applying a rule to the text ([0081-0082]. The labeled data can be the actual target probabilities.).
Regarding claim 5, Robinson teaches: The system of claim 1, wherein the computing device is configured to determine the recommended target area based on applying one or more machine learning models to the image data ([0124], In some examples, method 200 can be used in a machine-learning environment (for example, as shown in FIG. 5). The target can be adjusted automatically using machine learning. Future ablation plans and decision support modules may be automatically adjusted using machine learning. For example, blocks 215, 220, 225, and/or 230 may be adjusted automatically using machine learning. Machine learning tools and predictive analytics can be integrated within method 200 to create a clinical decision support infrastructure such as the decision support module.).
Regarding claim 6, Robinson teaches: The system of claim 5, wherein the computing device is configured to: generate features based on historical image scans; and train the one or more machine learning models based on the generated features ([0124], The target can be adjusted automatically using machine learning. Future ablation plans and decision support modules may be automatically adjusted using machine learning. For example, blocks 215, 220, 225, and/or 230 may be adjusted automatically using machine learning).
Regarding claim 7, Robinson teaches: The system of claim 1, wherein the image data is at least one of magnetic resonance image data ([0005], the anatomic mapping may be at least one of a computer tomography image or a magnetic resonance image) and computed tomography image data ([0005], the anatomic mapping may be at least one of a computer tomography image or a magnetic resonance image).
Regarding claim 8, Robinson teaches: The system of claim 1, wherein the computing device is configured to transmit the recommended target data to a second computing device to treat the patient ([0146, 0150] and see figs. 4 and 5, The output from the trained neural network can be provided to a treatment unit 514 for treating a patient. In some examples, the output from the trained neural network can be inputted directly into a treatment unit to perform a procedure on a patient.).
Regarding claim 9, Robinson teaches: The system of claim 1, wherein the computing device is configured to: generate a 3d structure image based on the image data; and display a segment model superimposed onto the 3d structure image ([0077, 0084], In some examples, a 3D model of the 17 segments can be generated.).
Regarding claim 10, Robinson teaches: The system of claim 9, wherein the computing device is configured to determine a segment of the segment model corresponding to the recommended target area of the organ ([0077, 0084], For example, the input mappings can be combined by overlapping segmentation models, combining a segmentation model and 3D geometries, or combinations thereof.).
Regarding claim 11, Robinson teaches: The system of claim 10, wherein the computing device is configured to determine the segment based on a relative location of the recommended target area to a portion of the organ ([0081] and fig. 6A, Any modeling approach can be used to learn the image features that predict the location of an abnormality (e.g., VT) on each input mapping.).
Regarding claim 12, Robinson teaches: The system of claim 11, wherein the computing device is configured to: determine a distance and a direction from the portion of the organ to the recommended target area; and based on the distance and the direction, determine the segment ([0081] and fig. 6A, Any modeling approach can be used to learn the image features that predict the location of an abnormality (e.g., VT) on each input mapping.).
Regarding claim 13, Robinson teaches: The system of claim 9, wherein the computing device is configured to generate the 3d structure image based on an interactive model ([0085, 0107], In some examples, the non-image data, such as the 12-lead mappings can be displayed to the user and the user can interact with or click on segments of the cardiac arrhythmia target. The cardiac arrhythmia target can be a segment, multiple segments, or a 3D volume).
Regarding claim 14, Robinson teaches: The system of claim 13, wherein the computing device is configured to: receive an input selecting one or more segments of the interactive model; and update the displayed segment model to indicate the selected one or more segments ([0085, 0107], In some examples, the non-image data, such as the 12-lead mappings can be displayed to the user and the user can interact with or click on segments of the cardiac arrhythmia target. The cardiac arrhythmia target can be a segment, multiple segments, or a 3D volume).
Regarding claim 15, Robinson teaches: The system of claim 1, wherein the computing device is configured to: obtain electrocardiogram (EKG) data for the patient; and determine the recommended target area based on the EKG data ([0076,0079,0090], In one example, an input mapping (e.g. a 17-lead ECG) can be divided into 17 segments. The segments can be the same or similar size, different sizes, or combinations thereof. After an input mapping has been segmented, the segments that include the abnormality can be determined.)
Regarding claim 16, Robinson teaches: The system of claim 1, wherein the computing device is configured to: determine a scar location of an organ based on the image data; determine healthy portions of the organ based on the scar location; and display a model of the organ identifying the scar location and the healthy portions ([0076 and [0093], and see fig. 6A, scar size, number of VT's, type of cardiomyopathy (e.g., ischemic or non-ischemic), transmurality (e.g., thick or thin scar), location of abnormality (e.g., certain segments may be more or less successful), age, gender, size of heart, ejection fraction, thickness of heart (e.g., weak or healthy heart)).
Regarding claim 17, Robinson teaches: The system of claim 1, wherein receiving the image data for the organ of the patient comprises receiving image data for each of a plurality of imaging technologies, wherein the computing device is configured to determine a segment of a model based on the image data received for each of the plurality of imaging technologies ([0005], the anatomic mapping may be at least one of a computer tomography image or a magnetic resonance image. [0049, 0051]). As used herein, “magnetic resonance imaging” (MRI) refers to the use of use magnetic fields and radio waves to form images of the body. Typically, when used in cardiac situations, cardiovascular magnetic resonance imaging (CMR) involves ECG gating which combats the artifacts created by the beating of the heart.).
Claims 18 and 26 are rejected for reasons similar to claim 1 above.
Regarding claim 19, Robinson teaches: The computer-implemented method of claim 18 comprising: receive report data characterizing medical findings of the patient ([0093-0094], As additional patients are treated, the previous treatments can inform future treatments); and
determine the recommended target area based on the report data ([0093-0094], As additional patients are treated, the previous treatments can inform future treatments and can be used to adjust the target of the current patient (for example, by training the neural network). In another example, the target may be adjusted based on a weighting of input mappings or the confidence score of the combined mappings.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW H LAM whose telephone number is (571)270-7969 and fax number is 571-270-8969. The examiner can normally be reached on 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benny Tieu can be reached on 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW H LAM/
Primary Examiner, Art Unit 2682