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 .
Status of claims: claims 21-40 are pending below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/15/2024 and 6/23/2026 was filed and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-10 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 11,574,140. Although the claims at issue are not identical, they are not patentably distinct from each other because e invention defined by the claims of the instant application is anticipated by the invention stipulated by the claims of U.S. Patent No. 11,574,140.
Claims 21-10 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,148,532. Although the claims at issue are not identical, they are not patentably distinct from each other because e invention defined by the claims of the instant application is anticipated by the invention stipulated by the claims of U.S. Patent No. 12,148,532.
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 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over Mark Herrmann et al (“Implementing the DICOM Standard for Digital Pathology”) in view of Duggirala et al (US 2004/0147840) and Duggirala et al (US 2010/0184093).
Mark Herrmann et al (“Implementing the DICOM Standard for Digital Pathology”) in view of Duggirala et al (US 2004/0147840).
Claim 21, similar claims 33 and 40:
Mark Herrmann et al teach:
A computer-implemented method for identifying a diagnostic feature of a digitized pathology image, the method comprising:
receiving one or more digitized images of a pathology specimen, and medical metadata comprising related case and/or patient information (page 1 Abstract teaches file and format of digitized pathology with DICOM/metadata with pixel-related metadata (pixel is digitized) integrated patient and specimen-related metadata, populated and encoded as well as stored DICOM files/database, which also include department and different laboratory information);
applying a machine learning model to generate one or more predictions based on a presence of one or more pathological conditions in the one or more digitized images (page 6 DICOM enables modeling of image pixel data: teaches integration of machine learning for image related information/diagnostic features for tumor stage, biopsy sampling approach; page 8 right column first paragraph teaches machine learning for processing of detail in DICOM attributes in figure 3);
providing, by the machine learning model (page 9 right column title DICOM enables efficient frame-level data access from local files teach the use of machine-learning), the at least one relevant diagnostic features for output to a display (page 1 Introduction: teaches display multiple magnification for diagnosis and research view in real-time; page 6 DICOM enables modeling of image pixel data: teaches uniquely assign a diagnosis to a given patient; page 12 figure 7 teaches display of matches/relevant diagnostic feature and metadata with trigger search to the user with a navigated resolution image), the at least one relevant diagnostic feature indicating a region harboring cancer (page 3 left column Selection and encoding DICOM attribute teaches information object definition (IOD) with set of attributes grouped; page 6 right column second paragraph teaches set of IOD with classes and attributes for description of encoded dataset represent in unnormalize form (irregular/cancer)).
Mark Herrmann et al teaches the following subject matter above, but not the following which is taught by Duggirala et al (US 2004/0147840):
generating, by the machine learning model, at least one relevant diagnostic feature of the relevant diagnostic features for output to a display, the at least one relevant diagnostic feature being based on the presence of a region having the one or more pathological conditions beyond a statistical likelihood (0068 teaches selection of image due to similarity with anatomical position (region of interest) with diagnoses with a probability exceeding a threshold amount (beyond a predetermined probability threshold) with condition of similarity by percentage, feature measurement with information highlighted (region of interest) for the user (display for user), 0065 teaches stage of disease (grade of cancer), detection of abnormal and normal tissue (non-cancerous feature)).
Mark Herrmann et al and Duggirala et al are both in the field of image analysis especially the use of computer aid detection for diagnose of target features such that the combine outcome is predictable.
It would have been obvious to one skill in the art at the time of the invention to modify Mark Herrmann et al by Duggirala et al to generate predictions such would provide a diagnostic decision base on statistical information with a predicted probability of accuracy of the diagnosis as disclosed by Duggirala et al (US 2004/0147840) in 0065.
Regarding claim 33, Mark Herrmann et al teach system in page 1 right column first paragraph teaches slide imaging system.
Regarding claim 40, Mark Herrmann et al teaches non-transitory computer-readable medium in page 3 right column second paragraph teaches database/storage, data and processor to carry out the method.
Claim 22, similar claim 34:
Duggirala et al teach:
The computer-implemented method of claim 21, wherein providing, by the machine learning model, the at least one relevant diagnostic feature for output to a display including: displaying a location that indicates a region with a highest statistical likelihood for harboring cancer (figure 4 and 0070 teaches highest probability to be display; 0035 teaches higher probability diagnosis maybe provided from measurement of other feature to confirm diagnosis).
Claim 23, similar claim 35:
Duggirala et al teach:
The computer-implemented method of claim 21, wherein providing, by the machine learning model, the at least one relevant diagnostic feature for output to a display including: displaying a set of top locations that indicate a set of regions with a highest statistical likelihood for harboring cancer (0068 teaches selection of image due to similarity with anatomical position (region of interest) with diagnoses with a probability exceeding a threshold amount (beyond a predetermined probability threshold) with condition of similarity by percentage, feature measurement with information highlighted (region of interest) for the user (display for user), 0065 teaches stage of disease (grade of cancer), detection of abnormal and normal tissue).
Claim 24, similar claim 36:
Duggirala et al teach:
The computer-implemented method of claim 21, wherein providing, by the machine learning model, the at least one relevant diagnostic feature for output to a display including: displaying one or more locations for a region with values around a decision boundary for determining if the at least one relevant diagnostic feature is cancer or not (0024 teaches identify minimal number of measurement, where identify is predetermined; 0070 and figure 4 teaches output screen with highest probability is display; above teaches the diagnostic relevance, plurality features).
Claim 25, similar claim 37:
Duggirala et al teach:
The computer-implemented method of claim 21, wherein providing, by the machine learning model, the at least one relevant diagnostic feature for output to a display including: displaying a prediction for each piece of tissue on the one or more digitized images (0065 teaches classifiers with proactive predictions of early stage disease (pathological condition) based on historical data (past digital image with pathological condition) indicating both abnormal and normal tissue with and predict diagnostic decision).
Claim 26, similar claim 38:
Mark Herrmann et al teach:
The computer-implemented method of claim 21, wherein providing, by the machine learning model, the at least one relevant diagnostic feature for output to a display including: displaying a descriptor, the descriptor including a statistical likelihood that an identified region is cancerous, on the one or more digitized images (page 15 item 9 acknowledge histological images; page 8 left third paragraph teaches DICOM attributes with histological description of parts/features).
Claim 27:
Mark Herrmann et al teach:
The computer-implemented method of claim 21, further including: logging the at least one relevant diagnostic and the display as part of a case history within a clinical reporting system (page 1 Abstract teaches file and format of digitized pathology with DICOM/metadata with pixel-related metadata (pixel is digitized) integrated patient and specimen-related metadata, populated and encoded as well as stored DICOM files/database, which also include department and different laboratory information).
Claim 28, similar claim 39:
Mark Herrmann et al teach:
he computer-implemented method of claim 26, wherein the at least one relevant diagnostic feature is indicated by an outline, a set of crosshairs, or a text descriptor (page 10 figure 5 teaches DICOM header/metadata contain information for field of interest; page 12 figure 7 teaches the text descriptor in the metadata such as patient name, medical record number, accession number which user can read and select from the list; page 6 left column first paragraph teaches annotation/text of the slide).
Claim 29:
Mark Herrmann et al teach:
The computer-implemented method of claim 21, wherein the method further comprises storing a collection of data into a digital storage device (title teaches digital pathology; above teaches storing of collection data by DICOM format; where page 3 right column second paragraph teaches values/data are populated in DICOM and further in database/digital storage device).
Claim 30:
Mark Herrmann et al teach:
The computer-implemented method of claim 21, wherein the method further comprises generating a probability for cancer on all points of a whole slide image (abstract teaches whole side image encoded together in metadata of DICOM; page 5 Effort tracking and data analysis: teaches whole slide image in DICOM format with define statistic/probability; page 15 item 24 teaches acknowledge of biomarker; page 15 item 10 and 12 take cancer is consideration; page 15 item 9 acknowledge histological images; page 8 left third paragraph teaches DICOM attributes with histological description of parts/features).
Claim 31:
Duggirala et al teach:
The computer-implemented method of claim 21, wherein the method further comprises generating a binary output to indicate whether or not a target feature is present in a selected region (0192 teaches binary output within meaningful timeframe of potential cut/target points/regions; 0100 teaches where consideration of characterizing and distinguishing/target features of select region by red/blue channel intensity from H&E stained images).
Claim 32:
Duggirala et al teach:
The computer-implemented method of claim 31, wherein the method further comprises computing an overall score for each pathological condition (0100 teaches stained feature/target with threshold and computed ratio/score; 0192 teaches output/computing score between 0 and 100 for each potential cut points/regions the sensitivity and specificity are evaluated).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TSUNG-YIN TSAI whose telephone number is (571)270-1671. The examiner can normally be reached 7am-4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached at (571) 272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656
Prosecution Insights