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 .
DETAILED ACTION
1. The following Office action is in response to communications filed on 7/22/2026. Claims 1-20 are currently pending within this application.
Response to Arguments
2. Applicant’s arguments with respect to the previously pending claims have been fully considered but they are moot in view of new ground(s) of rejection necessitated by Applicant’s amendments to the pending claims.
Claim Rejections – 35 USC § 103
3. 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 of this title, 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.
4. Claims 1-8 and 10-20 are rejected under 35 U.S.C 103 as being unpatentable by Halmann (US PGPub 2022/0061813) [hereafter Halmann] in view of Madabhushi (US PGPub 2018/0353149) [hereafter Mad] and Casciaro (US PGPub 2024/0252140) [hereafter Casciaro].
5. As to claim 1, Halmann discloses a method (operational method shown in Figure 3 which is executed by an ultrasound imaging system shown in Figure 1 including medical image processing system shown in Figure 2), comprising: receiving imaging data (ultrasound lung image as shown in Figures 4-7) indicative of a lung of a subject; determining at least one pleural line region in the imaging data (410-422 as shown in Figure 4) by performing a segmentation process (image recognition/shape and edge detection/convolutional detection and quantification operations) across a plurality of pixels of the imaging data; determining one or more values (jumpiness score and irregularity score) of one or more morphological features (local dimness, vertical location, orientation, change between frames, vertical gaps, discontinuous appearance, etc.) of the at least one pleural line region by fitting a function to measured values associated with the at least one pleural line region; and sending, based on the one or more values of one or more morphological features, an indication of a condition of the lung (formation and output of an annotated ultrasound image and suggested diagnosis to a display) (Paragraphs 0019, 0030-0034, 0037-0039, 0043, 0047-0054, 0060-0062).
It is however noted that Halmann fails to particularly disclose determining at least a tortuosity of at least one region by fitting at least a tortuosity function to measured values associated with the at least one region.
On the other hand, Mad discloses determining at least a tortuosity of at least one region (lung region) by fitting at least a tortuosity function to measured values associated with the at least one region (Paragraphs 0019, 0021, 0048-0050, 0054, 0068, 0073-0075, 0082-0087).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to include determining at least a tortuosity of at least one region by fitting at least a tortuosity function to measured values associated with the at least one region as taught by Mad with the method and corresponding operational device of Halmann because the cited prior art references are directed towards imaging devices that acquire images of a patients lung and diagnose conditions of the lung based upon characteristics of the acquired images and because each of the claimed limitations is fully disclosed within the cited prior art references and would yield predictable results of enabling measured tortuosity features, which are intensity invariant and do not exhibit sensitivity to imaging parameters like other features, to quantify a measure of aggressiveness or irregularity in vessels associated with a nodule, tumor, or region of tissue of a lung being imaged.
Also, it is noted that the combination of Halmann and Mad fails to particularly disclose an indication of a condition of the lung comprising at least an indication of a severity of a disease.
On the other hand, Casciaro discloses sending, based on one or more values of one or more morphological features, an indication of a condition of a lung comprising at least an indication of a severity of a disease (pneumonia) (Paragraphs 0010, 0025, 0031-0032, 0113, 0127, 0140-0141, 0157, 0174).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to include providing an indication of a condition of the lung comprising at least an indication of a severity of a disease as taught by Casciaro with the method and corresponding operational device of Halmann and Mad because the cited prior art references are directed towards imaging devices that acquire images of a patients lung and diagnose conditions of the lung based upon characteristics of the acquired images and because each of the claimed limitations is fully disclosed within the cited prior art references and would yield predictable results of allowing an objective disease severity staging and early identification of COVID-19 possible presence before the onset of pulmonary fibrosis in asymptomatic patients.
6. As to claim 2, Halmann discloses the indication of the condition comprises an indication of one or more of a disease, a level of a disease, a viral disease, pneumonia, coronavirus disease, or a COVID-19 infection (Paragraphs 0060-0062).
7. As to claim 3, Halmann discloses the indication of the condition of the lung comprises one or more of an indication of one or more of the values of the one or more morphological features or an indication of a value determined based on one or more of the determined values of the one or more morphological features (Paragraphs 0052-0054, 0060-0062).
8. As to claim 4, Halmann discloses the imaging data comprises lung ultrasound imaging data (Paragraphs 0019, 0033, 0043-0046).
9. As to claim 5, Halmann discloses the one or more morphological features comprise one or more of thickness, thickness variation, nonlinearity, or projected intensity variation (Paragraphs 0033, 0047-0048, 0051-0052).
10. As to claim 6, Halmann discloses determining the one or more values of the one or more morphological features of the at least one pleural line region comprises performing feature extraction of a portion of the imaging data comprising the at least one pleural line region (Paragraphs 0032-0034, 0047-0048).
11. As to claim 7, Halmann discloses sending the indication of the condition of the lung comprises one or more of sending the indication to a computing device, sending the indication to storage, or causing the indication of the condition to be output to via a display (Paragraphs 0038, 0053-0057, 0062).
12. As to claim 8, Halmann discloses determining the at least one pleural line region in the imaging data comprises performing automatic segmentation of the imaging data to detect the at least one pleural line region (Paragraphs 0032-0034, 0047-0048).
13. As to claim 10, Halmann discloses the pleural line region comprises a region of tissue having features within a threshold similarity to a line (as shown in Figure 4) (Paragraphs 0032-0034, 0047-0048).
14. As to claim 11, Halmann discloses determining the one or more values of the one or more morphological features comprises one or more of measuring or calculating of a value of a corresponding morphological feature based on intensity values of pixels of the imaging data comprising the pleural line region (Paragraphs 0032-0034, 0047-0048, 0051-0052).
15. As to claim 12, Halmann discloses determining, based on applying one or more of a rule or a model to the one or more values of the morphological features, the indication of the condition (Paragraphs 0032-0034, 0051-0052).
16. As to claim 13, Halmann discloses the one or more morphological features of the at least one pleural line region comprise features indicative of variations in one or more of shape or linearity of the at least one pleural line region (Paragraphs 0032-0033, 0047-0048, 0051-0052).
17. As to claim 14, Halmann discloses training, based on a set of training images, a machine learning model configured to associate values of the one or more morphological features with corresponding indications of the condition, wherein the indication of the condition is determined based on the machine learning model (Paragraphs 0032-0034, 0051-0052).
18. As to claim 15, Halmann discloses determining, based on inputting the one or more values of the one or more morphological features to a machine learning model, the indication of the condition (Paragraphs 0032-0034, 0051-0052).
19. As to claim 16, Halmann discloses applying weights to each of the one or more values of the one or more morphological features, wherein the weights are applied equally or based on a machine learning model; and averaging the weighted values to determine a value of the indication of the condition (irregularity score) (Paragraphs 0032-0034, 0051-0052).
20. As to claims 17-20, the combination of the Halmann, Mad, and Casciaro references discloses all claimed subject matter as explained with respect to the above comments/citations of claims 1 and 5.
21. Claim 9 is rejected under 35 U.S.C 103 as being unpatentable by Halmann (US PGPub 2022/0061813) [hereafter Halmann] and Madabhushi (US PGPub 2018/0353149) [hereafter Mad] and Casciaro (US PGPub 2024/0252140) [hereafter Casciaro], as applied to claim 1, and in further view of Xu (US PGPub 2020/0359991) [hereafter Xu].
22. As to claim 9, it is noted that Halmann, Mad, and Casciaro fails to particularly disclose determining the at least one pleural line region in the imaging data comprises receiving, based on user input, an indication of a location of the at least one pleural line region and segmenting, based on the indication of the location, the pleural line region.
On the other hand, Xu discloses determining the at least one pleural line region (as shown in Figures 3 and 10) in the imaging data comprises receiving, based on user input, an indication of a location of the at least one pleural line region and segmenting, based on the indication of the location, the pleural line region (Paragraphs 0036-0037, 0042-0044, 0046, 0065-0071).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to include determining the at least one pleural line region in the imaging data comprises receiving, based on user input, an indication of a location of the at least one pleural line region and segmenting, based on the indication of the location, the pleural line region as taught by Xu with the operational method of Halmann, Mad, and Casciaro because the cited prior art are directed towards ultrasound imaging methods and systems that identify pleural line regions of lung images in order to diagnose irregularities/diseases present within the images and because each of the claimed limitations are fully disclosed within the cited prior art reference and would yield predictable results of enabling a certified user/expert to manually identify the area including the pleural line to be used for the identification of the lung condition.
Conclusion
23. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL S OSINSKI whose telephone number is (571) 270-3949. The examiner can normally be reached on Monday - Thursday, 10:00am - 6:00pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oneal Mistry can be reached on (313) 446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MO
/MICHAEL S OSINSKI/Primary Examiner, Art Unit 2674
9/11/2026
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