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 .
Claim Rejections - 35 USC § 102
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 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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Saito et al. (US 2022/0020496 A1).
With respect to claim 1, Saito discloses a method for detecting a bowel of a patient comprising: performing an intestinal ultrasound on the patient; determining, using an artificial intelligence/machine learning (AI/ML) model (see paragraph 0220 disclosing the use of an ultrasound device between other imaging modalities in conjunction with CNN as a machine learning model or AI according to paragraph 0033), whether a bowel is detected in data of the intestinal ultrasound (see paragraphs 0058); and categorizing the data corresponding to the intestinal ultrasound based on the determination (see paragraphs 0042-0045).
With respect to claims 2 and 12, Saito discloses scoring a frame of the data corresponding to the intestinal ultrasound (see paragraphs 0070, 0109-0110 and 0175).
With respect to claims 3 ad 13, Saito discloses scoring the frame of the data corresponding to the intestinal ultrasound comprises giving the frame a score of one of: 0, when the bowel is not detected in the frame; 1, when the bowel is partially detected in the frame; 2, when the bowel is detected in the frame; and 3, when the bowel is detected in the frame and in one or more adjacent frames to the frame (see paragraph 0175).
With respect to claims 4 and 14, Saito discloses scoring a frame of the data corresponding to the intestinal ultrasound comprises giving the frame a confidence score corresponding to a detection of the bowel (see paragraphs 0276-0278 and 0325).
With respect to claims 5 and 15, Saito discloses aggregating the scoring of the frame of the data corresponding to the intestinal ultrasound (see paragraphs 0070, 0109-0110 and 0175).
With respect to claims 6 and 16, Saito discloses aggregating the scoring is based on a severity score (see Abstract and paragraphs 0051-0055).
With respect to claims 7 and 17, Saito discloses aggregating the scoring is based on a time-frame score (see paragraphs 0166, 0276-0278 and 0325).
With respect to claims 8 and 19, Saito discloses determining whether the bowel detected in the data corresponding to the intestinal ultrasound comprises a disease (see Abstract and paragraph 0045).
With respect to claim 9, Saito discloses presenting, to a user, the data corresponding to the intestinal ultrasound on a graphical user interface on a display (see paragraph 0070 as seen in Figures 1A and 5).
With respect to claims 10 and 20, Saito discloses providing an indication to a user of whether the bowel is detected in the data corresponding to the intestinal ultrasound (see paragraphs 0058).
With respect to claim 11, Saito discloses a computing apparatus comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processor, configure the computing apparatus to: determine, using an artificial intelligence/machine learning (AI/ML) model (see paragraph 0220 disclosing the use of an ultrasound device between other imaging modalities in conjunction with CNN as a machine learning model or AI according to paragraph 0033), whether a bowel is detected in data of an intestinal ultrasound of a patient (see paragraphs 0058); and categorize the data corresponding to the intestinal ultrasound based on the determination (see paragraphs 0042-0045).
With respect to claim 18, Saito discloses a non-transitory computer-readable storage medium including instructions that, when executed by one or more processors of a computer, cause the computer to: determine, using an artificial intelligence/machine learning (AI/ML) model (see paragraph 0220 disclosing the use of an ultrasound device between other imaging modalities in conjunction with CNN as a machine learning model or AI according to paragraph 0033), whether a bowel is detected in data of an intestinal ultrasound of a patient (see paragraphs 0058); and categorize the data corresponding to the intestinal ultrasound based on the determination (see paragraphs 0042-0045).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The additional prior art cited in the PTO 892 not relied upon discloses imaging devices to discover diseases in the digestive tract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIXOMARA VARGAS whose telephone number is (571)272-2252. The examiner can normally be reached Monday-Friday 8am-5pm.
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/DIXOMARA VARGAS/Primary Examiner, Art Unit 3798
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