Prosecution Insights
Last updated: October 04, 2026
Application No. 19/006,058

METHOD FOR DISPLAYING REGION OF INTEREST BY COMPARING MEDICAL DATA

Non-Final OA §103
Filed
Dec 30, 2024
Priority
Dec 28, 2023 — RE 10-2023-0193847 +1 more
Examiner
CRADDOCK, ROBERT J
Art Unit
Tech Center
Assignee
Irm Inc.
OA Round
1 (Non-Final)
84%
Grant Probability
Favorable
1-2
OA Rounds
7m
Est. Remaining
98%
With Interview

Examiner Intelligence

Grants 84% — above average
84%
Career Allowance Rate
545 granted / 649 resolved
+24.0% vs TC avg
Moderate +14% lift
Without
With
+14.4%
Interview Lift
resolved cases with interview
Typical timeline
2y 5m
Avg Prosecution
14 currently pending
Career history
661
Total Applications
across all art units

Statute-Specific Performance

§101
11.5%
-28.5% vs TC avg
§103
43.7%
+3.7% vs TC avg
§102
22.1%
-17.9% vs TC avg
§112
12.6%
-27.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 649 resolved cases

Office Action

§103
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 . 35 USC § 101 Claim 1 – 7 are consider to be patent eligible under 101. Specification The disclosure is objected to because of the following informalities: Page 19 line 4-7, “A non-transitory computer readable medium is a medium that can store data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Examples of non-transitory computer readable media may include a compact disc (CD), a digital versatile disc (DVD), a hard disk, a Blu-ray disc, a Universal Serial Bus (USB), a memory card, and a read-only” appears to have a typo. The examiner notes “a Universal Serial Bus (USB),” appears to be a typo, as a bus itself is not a non-transitory computer medium, rather it appears the applicant intended to say language like “a Universal Serial Bus (USB) based flash drive” or something similar, rather than a bus itself. Appropriate correction is required. Allowable Subject Matter Claim 3 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. 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. Claim(s) 1, 2, 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over Shibuya et al. (US 20230253083 A1) Regarding claim 1, Shibuya teaches a method (¶46: Method) of displaying a region of interest (ROI) of target data based on a comparison of medical data, the method comprising (¶21, “In general, according to one embodiment, a report creation support device includes an identifying module and a report creating module. The identifying module is configured to, when receiving an input selecting a reading order related to a first medical image in which a patient's site is captured by a first modality, specify a predetermined region of the first medical image as a region of interest, and identify a second medical image corresponding to the specified region of interest from among second medical images in which the patient's site is captured by a second modality which is different from the first modality. The report creating module is configured to attach the first medical image and the identified second medical image to a predetermined region of a reading report created for the reading order.”): producing a comparison value based on a difference between comparison data and the target data (¶41-46); identifying an ROI in an object of interest included in the target data based on the comparison value and the target data (¶40-46. See ¶40, “The region narrowing function 66d, based on a value of the lesion candidate or a specific value, narrows down a lesion candidate region corresponding to the value of the lesion candidate from the target region. Here, the specific value may be a preset value or a value instructed by the user, and the value may be a pixel value or a range of pixel values. As shown in FIG. 4, for example, such a region narrowing function 66d narrows down a lesion candidate region 122 from the target region 121 of the MLO image 110R based on the value of the lesion candidate 1 of the CC image 100R. Also, for example, as shown in FIG. 5, the region narrowing function 66d narrows down the lesion candidate region 122 from the target region 121 of the CC image 100R based on the value of the lesion candidate 1 of the MLO image 110R.”); but the main embodiment doesn’t explicitly disclose displaying the identified ROI in a different display method from remaining regions excluding the ROI. An alternate embodiment of Shibuya teaches displaying the identified ROI in a different display method from remaining regions excluding the ROI (¶72, Fig 9a-b). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the main embodiment of Shibuya in view of the alternate embodiment of Shibuya can prevent lesion candidates from being overlooked (¶72). Regarding claim 2, Shibuya teaches the method of claim 1, wherein the identifying of the ROI includes: selecting an object included in the target data as the object of interest when the comparison value is greater than or equal to a reference value that varies by object (¶40, “The region narrowing function 66d, based on a value of the lesion candidate or a specific value, narrows down a lesion candidate region corresponding to the value of the lesion candidate from the target region. Here, the specific value may be a preset value or a value instructed by the user, and the value may be a pixel value or a range of pixel values. As shown in FIG. 4, for example, such a region narrowing function 66d narrows down a lesion candidate region 122 from the target region 121 of the MLO image 110R based on the value of the lesion candidate 1 of the CC image 100R. Also, for example, as shown in FIG. 5, the region narrowing function 66d narrows down the lesion candidate region 122 from the target region 121 of the CC image 100R based on the value of the lesion candidate 1 of the MLO image 110R.”). Regarding claim 4, Shibuya teaches the method of claim 2, wherein the selecting of the object of interest includes: when the comparison data is a previous medical image for the target data, setting the reference value based on a normal numerical range for an object identified in the comparison data and the target data and a numerical value of the comparison data (¶40). Regarding claim 5, Shibuya teaches the method of claim 1, wherein the producing of the comparison value includes: when the target data is a medical report, extracting object information included in the medical report( ¶40-46, the examiner notes the medical data being described is interpreted as medical report. The extraction can be any of the analysis of the medical report ); extracting a numerical value corresponding to the object information from the medical report (See ¶40-46, (a)-(d) are extracting numerical value when applying any of (a)-(d)); and producing a difference between a numerical value included in the comparison data and the numerical value included in the target data as the comparison value (See ¶40-46. The difference is the visual result). Regarding claim 6, Shibuya teaches the method of claim 1, wherein the producing of the comparison value includes: when the target data is a medical image, identifying an object of the medical image; measuring a size of the object using a measuring tool for each type of medical image; and producing the comparison value based on the size of the measured object (¶48, “From among operations on the mammogram images clearly showing the lesion candidate regions, the operation restriction function 68a accepts operations on the lesion candidate regions while ignoring operations on regions different from the lesion candidate regions. In other words, the operation restriction function 68a limits operations on mammogram images including lesion candidate regions to only the operations on the lesion candidate region. Here, the operation on the lesion candidate region may be an operation for reading the lesion candidate region. For example, the operation for reading may be one of the following operations: changing the tone of the lesion candidate region, changing the size of the lesion candidate region, adding information on the lesion candidate region, clicking on the lesion candidate region, and cutting out the lesion candidate region. The operation for adding the information may be, for example, an operation for selecting a lesion candidate region in the first mammogram image, then selecting the lesion candidate region in the second mammogram image, and adding the information to the lesion candidate region. The information to be added may include, for example, at least one of the results of size measurement of the lesion candidate, annotation, description of findings, and analysis results. The click operation may also serve as the operation for changing the tone or size, the operation for adding the information, or the operation for the cutting out. The operation for the cutting out may referred to as a trimming operation or an operation for mask processing. The mask processing displays only the lesion candidate regions and does not display other regions.”). Regarding claim 7 The method of claim 1, wherein the identifying of the ROI includes: when an object of the comparison data is an object having a smaller size than an object of the target data, setting a portion of the target data corresponding to the object of the comparison data as an object of interest (¶44, ¶48). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT J CRADDOCK whose telephone number is (571)270-7502. The examiner can normally be reached Monday - Friday 10:00 AM - 6 PM EST. 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, Devona E Faulk can be reached at 571-272-7515. 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. /ROBERT J CRADDOCK/Primary Examiner, Art Unit 2618
Read full office action

Prosecution Timeline

Dec 30, 2024
Application Filed
Aug 11, 2026
Non-Final Rejection mailed — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
84%
Grant Probability
98%
With Interview (+14.4%)
2y 5m (~7m remaining)
Median Time to Grant
Low
PTA Risk
Based on 649 resolved cases by this examiner. Grant probability derived from career allowance rate.

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