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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites the limitation "the S2D image". There is insufficient antecedent basis for this limitation in the claim. It appears that this claims was intended to depend from 4.
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.
Claims 1-5, 7, 9-11, 13 and 17-19 are rejected under 35 U.S.C. 102(1)(1) as being anticipated by Daughton et al (WO 2022/221712 A1)
Referring to claim 1:
Daughton et al disclose a protocol generation system for a digital mammography system (par. 229-231: inclusion for review of images within a larger set, or a particular slice (slab) within imagery), the protocol generation system comprising:
a processor, and a memory storing instructions (par. 116-121: networked computer system implementing software / computing devices with processor executing computer code stored in memory) that when executed cause the processor to:
receive digital breast tomosynthesis (DBT) case images of a patient, the DBT case images including at least one projection set of images and/or image volume of a breast of the patient reconstructed from data acquired using the digital mammography system (par. 97, 110, 132, 138-139: receiving medical images of the breast and by 3D Tomosynthesis, i.e., DBT);
generate or receive as an input a case score for the DBT case images (par. 108, 112: generating a “score” that quantifies any tissue anomalies or indications of cancer);
select a minimum dataset of the DBT case images to be read, based on the case score (par. 113, 137, 140-142: by using the system to pre-screen and score the mammograms based on anomalies to detect the presence or absence of cancer, the number of images the radiologist needs to review is reduced to a minimum); and
send the minimum dataset and/or a full dataset with a reading protocol specifying the minimum dataset to a radiology workstation (RWS) to be read and/or store the minimum dataset in an archive (par. 229-231: worklist can highlight images or studies (datasets) within a listing of all pending reviews (Fig. 8), can sort the listing of all pending reviews in order of suspicious and not suspicious, or can list only the suspicious images or studies for priority review (protocol for review), the images or studies within a workflow may also be highlighted with an indication that the image or study comprises a suspicion code).
Referring to claim 2:
Daughton et al disclose the protocol generation system is integrated into or electronically coupled to the digital mammography system or installed at a radiology workstation (RWS) communicatively coupled to the digital mammography system via a network (Figs. 1, 26).
Referring to claim 3:
Daughton et al disclose the received DBT case images are analyzed by one or more artificial intelligence (AI) models, and the case score is generated using outputs from the one or more AI models (par. 244-281: neural network developed for image analysis and object detection include the combination of features described for optimal performance on anomaly detection, including in medical imaging).
Referring to claim 4:
Daughton et al disclose the one or more AI models include one of a risk assessment system (par. 219: event risk), a negative triage system (par. 113: pre-screening identifies benign mammogram / par. 116: triage a workflow / Fig. 26 triage system), a breast density assessment system (par. 89), an anomaly detection system used to generate a set of planes, a set of slabs, and/or a Synthetic2D (S2D) image from the received DBT case images (par. 116: generate synthetic 2D images), and a different anomaly detection system (par. 115, 129, 134: anomaly detection).
Referring to claim 5:
Daughton et al disclose the output of the AI model is used to generate the set of planes, slabs and/or the S2D image includes at least one of: a location of an anomaly in a received case image; a classification of the anomaly; an assessment of a seriousness of the anomaly; and a confidence score indicating a degree of confidence of the anomaly detection system in assessing the seriousness or presence of the anomaly (par. 466-469: determination of the uncertainty or confidence level).
Referring to claim 7:
Daughton et al disclose a full-field digital mammography (FFDM) image of the breast is an input to the AI models, and the case score is generated at least partially based on the FFDM image (par. 229, 244-281: among the numerous examples of abnormalities within anatomical features which can be detected and categorized using the systems and methods of the present invention, it will be appreciated by those of skill in the art that the suspicion codes may also be forwarded to other healthcare functional units, such as the Full-Field Digital Mammography (“FFDM”) system and to use the disclosed AI models).
Referring to claim 9:
Daughton et al disclose the reading protocol and/or an output of an anomaly detection system is encoded into a Digital Imaging and Communications in Medicine (DICOM) tag of the S2D image (par. 15, 96, 138, 146, 239, 369, 414, 472).
Referring to claim 11:
The same reasons for rejection that apply to claims 1, 3, 4 and 5 above applies mutatis mutandis to claims 11.
Referring to claim 13:
The same reasons for rejection that apply to claims 1, 3, 4, 5, and 9 above applies mutatis mutandis to claims 11 and 13.
Referring to claim 17:
The same reasons for rejection that apply to claims 1, 3, and 4 or 11 above applies mutatis mutandis to claims 13 except for the added limitation of displaying the minimum dataset on a display device in accordance with the reading protocol, which Daughton et al disclose (par. 117, 126-129, 146, 239: computing devices having a display for displaying transmitted medical images according to a review priority, i.e., reading protocol).
Referring to claim 18:
The same reasons for rejection that apply to claims 1, 3, 4, and 9, or 11 and 13, above applies mutatis mutandis to claims 17 and 18.
Referring to claim 19:
The same reasons for rejection that apply to claims 1, 3, 4, and 9, or 11 and 13, above applies mutatis mutandis to claims 17-19 except for the added limitation of the minimum dataset specified in the extracted reading protocol is retrieved from an archive of the digital mammography system at a first time, and remaining images of the DBT study not included in the minimum dataset is retrieved at a second time, the second time being later than the first time, which Daughton et al disclose (par. 229-231: this is implied by the radiologist’s review priority or reading protocol where the pre-screened medical images are sorted and reviewed based on the score, the dataset of suspicious images being reviewed first, and if necessary, the dataset of remaining unsuspicious images being reviewed later).
Claim Rejections - 35 USC § 103
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.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Daughton et al as applied to claim 1 above, and further in view of well-known prior art (MPEP 2144.03).
Referring to claim 10:
While not disclosed in Daughton et al, it is well-known in the prior art to use lossless compression for certain images (e.g., images that are of higher importance or priority such as an image including or focused on a target subject or object, where image detail can’t be lost) and lossy compression for other images (e.g. images that of less importance or priority such as a background or non-subject image).
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Daughton et al in view of such well-known in the prior art, to have included instructions executed to cause the processor to compress the minimum dataset using a lossless compression, compress a remaining portion of the case images not included in the minimum dataset using lossy compression, and send both of the minimum dataset and the remaining portion to the archive. Such a modification achieves data reduction of the case images for improved transmission efficiency, and reduced memory capacity for storage, while not loosing any detail in the minimum dataset of case images requiring critical review, and thereby enabling the best result possible when transmitting and/or storing the case images.
Allowable Subject Matter
Claim 6, 8, 12, and 14-16 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. Referring to these claims, the prior art searched and of record neither anticipates nor suggests the limitations added in the claimed combinations.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 15 November 2024 and 10 June 2026 were filed in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the statements have been considered by the examiner.
The relevance of the cited documents cited in the second IDS, in addition how applied above, can be found in the European Search Report and/or Written Opinion from the EPO dated 21 October 2025 for EP application no. 25209980.9 (of record).
Applicant has not provided an explanation of relevance of the cited document in the first IDS summarized below.
Gkanatsios et al (US 11372534 B2) disclose a method and system for acquiring, processing, storing, and displaying x-ray mammograms Mp tomosynthesis images Tr representative of breast slices, and x-ray tomosynthesis projection images Tp taken at different angles to a breast, where the Tr images are reconstructed from Tp images.
Cited Art
The prior art and other references made of record and not relied upon are considered pertinent to applicant's disclosure.
Daughton et al (US 11430113 B2) disclose receiving a piece of medical information, e.g., a medical image of tissue, for processing and analysis on a computing device or system. A region of the medical image may be analyzed to determine a presence of one or more contours in the region. One or more properties of the one or more contours may be extracted, where the one or more properties are inputted into a first algorithm to determine an indication of cancer for the region. The indication of cancer may be inputted into a second algorithm to generate a cancer score for the region.
St. Pierre et al (US 12505645 B2) disclose an AI model that analyzes new DBT and ultrasound images to determine if one lesion correlates to another.
Jing et al (US 20230351600 A1) discloses use of a real-time artificial intelligence system analyzes digital breast tomosynthesis images as they are recorded to identify potential lesions.
Su et al (US 20260112028 A1) disclose synthetic 2D mammography (SM) images derived from digital breast tomosynthesis (DBT) exams (Figs. 12A-12D) and training/using Deep learning (DL) models for estimating breast density in digital breast tomosynthesis (DBT) exams, and synthetic 2D mammography (SM) images from digital breast tomosynthesis (DBT) exams.
Park (EP 4502830 B1) discloses analyzing input medical images, such as digital breast tomosynthesis (DBT) images, using an artificial intelligence model that has learned a difficult task of distinguishing abnormalities. This may include adding the analysis result to a reading worklist, and the input image whose abnormality score is less than or equal to the cut-off score may be not added to the reading worklist.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott Rogers whose telephone number is 571-272-7467. The examiner can normally be reached 8 am to 7 pm flex.
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/Scott A Rogers/
Primary Examiner, Art Unit 2683
27 June 2026
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