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 .
Priority
Acknowledgement is made of Applicant’s claim of the present application being a National Stage of International Patent Application No. PCT/EP2022/064991, filed on June 2, 2022, which claims priority and the benefit of U.S. Provisional Patent Application No. 63/208,452, filed on June 8, 2021.
Information Disclosure Statement
The information disclosure statement (“IDS”) filed on 12/05/2023 was reviewed and the listed references were considered.
Drawings
The 6-page drawings have been considered and placed on record in the file.
Status of Claims
Claims 1-17 are pending.
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 1-2, 5, 8-9, and 15-17 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (“Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data”).
Consider Claim 1, Zhang discloses “A computer-implemented method of predicting a shape of an anatomical region” (Zhang, Page 1117, right column, last paragraph discloses:
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), “the method comprising:
receiving historic volumetric image data representing the anatomical region at a historic point in time” (Zhang, Page 1116, right column, first paragraph discloses:
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and Page 1117, right column, last paragraph at Xt at t=1:
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);
“receiving subsequent projection image data representing the anatomical region at a subsequent point in time that is subsequent to the historical point in time” (Zhang, Page 1117, right column, last paragraph at Xt at t=2:
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; “and
predicting subsequent volumetric image data representing the anatomical region at the subsequent point in time based on the historic volumetric image data and the subsequent projection image data” (Zhang, Page 1117, right column, last paragraph and Page 1118, left column, first paragraph disclose:
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), “wherein the prediction of the subsequent volumetric image data is constrained by the subsequent projection image data” (Zhang, Page 1118, left column, second paragraph discloses
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; therefore, Zhang constrains its predicted volumetric image data by the subsequent image slices in its spatio-temporal convolutional LSTM).
Consider Claim 16, Zhang discloses “The computer-implemented method according to claim 1, further comprising: inputting the received historic volumetric image data into a neural network; and using the neural network, predicting the subsequent volumetric image data representing the anatomical region at the subsequent point in time, wherein the neural network is trained to predict, from first volumetric image data representing the anatomical region at a first point in time and second projection image data representing the anatomical region at a second point in time subsequent to the first point in time, second volumetric image data representing the anatomical region at the second point in time, the prediction of the second volumetric image data is constrained by the second projection image data” (Zhang, Page 1117, Fig. 2., wherein the neural network receives slices as the input of the volumetric image data at time 1 and at time 2, subsequent to time 1, to predict the subsequent volumetric image data
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).
Consider Claim 2, Zhang discloses “The computer-implemented method according to claim 16, wherein the method further comprising: inputting, into the neural network, a time difference between the historic point in time and the subsequent point in time, and generating, using the neural network, the predicted subsequent volumetric image data based further on the time difference; and wherein the neural network is trained to predict second volumetric image data based further on a time difference between the first point in time and the second point in time” (Zhang, Section III, subsection 3 discloses : Introduction, last paragraph discusses use of time interval into the neural network computation of the predicted volumetric image data
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).
Consider Claim 5, Zhang discloses “The computer-implemented method according to claim 1, further comprising: segmenting the anatomical region in at least one of the received historic volumetric image data or in-the received subsequent projection image data prior to, respectively, at least one of inputting the received historic volumetric image data into the neural network or using the received subsequent projection image data to constrain the predicted subsequent volumetric image data” (Zhang, Page 1116 last line and Page 1117, first three lines, wherein it is disclosed organ regions are first roughly cropped and registered to post-contrast CT using ITK implementation of mutual information based B-spline registration. See also Fig. 1: ROI Crop
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).
Consider Claim 8, Zhang discloses “The computer-implemented method according to claim 16, further comprising: inputting patient data into the neural network; and generating the predicted subsequent volumetric image data based on the patient data; and wherein the neural network is further trained to predict the volumetric image data based on patient data” (Zhang, Page 1120, Section IV.C, wherein it is disclosed
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).
Consider Claim 9, Zhang discloses “The computer-implemented method according to claim 1, further comprising at least one of: computing a measurement of the anatomical region represented in the predicted subsequent volumetric image data or generating one or more clinical recommendations based on the predicted subsequent volumetric image data” (emphasis added) (Zhang, Page 1120 discloses computing measurement of the anatomical region:
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).
Claims 15 and 17 recite a non-transitory computer-readable storage medium storing a computer program with instructions corresponding to the steps of the method recited in Claims 1 and 16, respectively. Therefore, the recited instructions of this claim are mapped to the Zhang reference in the same manner as the corresponding steps of the method Claims 1 and 16, respectively. Additionally, the Zhang reference discloses use of a workstation with a memory (Zhang, Page 1120, left column, under Section IV.B(3) Comparison: Dell Tower 7910 Workstation).
Allowable Subject Matter
Claims 3-4, 6-7, and 10-14 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: consider Claim 3, Jerebko et al. (US 2006/0050991) discloses a computer-aided detection and diagnosis (CAD), which uses the image data “to quantify certain characteristics of the polyp. CAD techniques such as virtual endoscopy based on two-dimensional (2D) and three-dimensional (3D) analysis of image data acquired during diagnostic CT scans can be used locate and identify lung nodules or aneurisms by using geometric features and/or volumetric properties of the lung and its vessel trees.” However, none of the cited prior art references, alone or in combination, provides a motivation to teach the ordered combination of the limitations recited in Claim 3. Claim 4 is dependent from Claim 3, and therefore, include the above-described allowable subject matter. Furthermore, none of the cited prior art references, alone or in combination, provides a motivation to teach the ordered combination of the limitations recited in Claim 6. Claim 7 is dependent from Claim 6, and therefore, include the above-described allowable subject matter. Additionally, consider limitations recited in claims 10 and 11, none of the cited prior art references, alone or in combination, provides a motivation to teach the ordered combination of the limitations recited in Claims 10 and 11. Claims 12-14 are dependent from Claim 11, and therefore, include the above-described allowable subject matter.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion and Contact Information
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure: Dinh et al. (US 2021/0272308), Paragraph [0040].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Siamak HARANDI whose telephone number is (571)270-1832. The examiner can normally be reached Monday - Friday 9:30 - 6:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on (571)272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Siamak Harandi/Primary Examiner, Art Unit 2662
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