Prosecution Insights
Last updated: April 19, 2026
Application No. 18/808,120

METHOD AND DEVICE FOR DETERMINING RADIATION DELIVERY PLAN, AND COMPUTER EQUIPMENT

Non-Final OA §102
Filed
Aug 19, 2024
Examiner
KIM, KIHO
Art Unit
2884
Tech Center
2800 — Semiconductors & Electrical Systems
Assignee
Shanghai United Imaging Healthcare Co. Ltd.
OA Round
1 (Non-Final)
85%
Grant Probability
Favorable
1-2
OA Rounds
2y 0m
To Grant
90%
With Interview

Examiner Intelligence

Grants 85% — above average
85%
Career Allow Rate
1419 granted / 1661 resolved
+17.4% vs TC avg
Minimal +4% lift
Without
With
+4.2%
Interview Lift
resolved cases with interview
Fast prosecutor
2y 0m
Avg Prosecution
27 currently pending
Career history
1688
Total Applications
across all art units

Statute-Specific Performance

§101
3.1%
-36.9% vs TC avg
§103
54.1%
+14.1% vs TC avg
§102
25.4%
-14.6% vs TC avg
§112
12.6%
-27.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1661 resolved cases

Office Action

§102
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 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. Claim(s) 1 – 2, 4, 6, 8 – 10, 13 – 14, and 16 – 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hartman (US 2014/0350863 A1). With respect to independent claim 1, Hartman teaches A method for determining a radiation delivery plan, comprising: obtaining an image of a target object Step 101 of Fig. 1, and determining a predicted dose distribution Step 107 of Fig. 1 of the target object according to the image of the target object; and determining an objective dose distribution see paragraph [0036] for the radiation delivery plan based on the predicted dose distribution and a plan-quality control strategy step 309 of Fig. 3. With respect to dependent claim 2, Hartman teaches in paragraph [ 0036] wherein the plan-quality control strategy step 309 comprises different control strategies. With respect to dependent claim 4, Hartman teaches wherein the plan-quality control strategy comprises at least one of: a priority control strategy, an off-target dose control strategy, a conformance control strategy, a hot spot control strategy, a target region dose control strategy in paragraph [0036], a uniformity control strategy, or an automatic normalization control strategy. With respect to dependent claim 6, Hartman teaches in paragraph [0036] and in Fig. 3 wherein, determining the objective dose distribution for the radiation delivery plan based on the predicted dose distribution and the plan-quality control strategy, comprises: constructing an initial dose-calculating function based on the predicted dose distribution; obtaining an objective dose-calculating function based on the initial dose-calculating function and the plan-quality control strategy; and computing the objective dose-calculating function to obtain the objective dose distribution. With respect to dependent claim 8, Hartman teaches in Fig. 1 wherein obtaining the image of the target object and determining the predicted dose distribution of the target object according to the image of the target object comprises: obtaining the image of the target object, inputting the image of the target object into a dose-distribution predicting model to obtain the predicted dose distribution. With respect to dependent claim 9, Hartman teaches in Fig. 1 wherein obtaining the image of the target object, and inputting the image of the target object into a dose-distribution predicting model to obtain the predicted dose distribution, comprise: obtaining the image of the target object, and inputting the image of the target object into the dose-distribution predicting model to obtain an initial dose distribution; and adjusting the initial dose distribution according to dose distribution reference information to obtain the predicted dose distribution, wherein the dose distribution reference information comprises at least one of an objective dose of a target region in the image, a dose limit of an organ-at-risk region in the image, or a priority of the organ-at-risk region. With respect to dependent claim 10, Hartman teaches in Fig. 1 wherein: the objective dose of the target region in the image refers to a prescription dose set for the target region in the image according to specificity of the image; the dose limit of the organ-at-risk region in the image refers to a lower limit of unacceptable dose set for the organ-at-risk region outside the target in the image; and the priority of the organ-at-risk region refers to the priority of the organ-at-risk region relative to the target region. With respect to independent claim 13, Hartman teaches a method for determining a radiation delivery plan, comprising: obtaining an image step 101 of Fig. 1 of a target object; determining an initial dose distribution step 103 of Fig. 1 based on the image of the target object; determining step 107 a predicted dose distribution of the target object by adjusting the initial dose distribution according to dose distribution reference information; and determining in paragraph [0036] an objective radiation delivery plan based on the predicted dose distribution. With respect to dependent claim 14, Hartman teaches wherein the dose distribution reference information comprises at least one of an objective dose of the target region in the image, a dose limit of the organ-at-risk region in the image in paragraph [0036], or a priority of the organ-at-risk region. With respect to dependent claim 16, Hartman teaches in Fig. 1 wherein determining the initial dose distribution based on the image of the target object comprises: inputting the image of the target object into a dose-distribution predicting model to obtain the initial dose distribution steps 101 - 105. With respect to dependent claim 17, as discussed in the rejection justification to claim 1, Hartman teaches wherein determining the objective radiation delivery plan based on the predicted dose distribution, comprising: determining an objective dose distribution for the radiation delivery plan based on the predicted dose distribution and a plan-quality control strategy; and determining the objective radiation delivery plan based on the objective dose distribution. With respect to dependent claim 18, Hartman teaches wherein: the plan-quality control strategy comprises different control strategies of a priority control strategy, an off-target dose control strategy, a conformance control strategy, a hot spot control strategy, a target region dose control strategy in paragraph [0036], a uniformity control strategy, and an automatic normalization control strategy; and determining the objective dose distribution for the radiation delivery plan based on the predicted dose distribution and the plan-quality control strategy comprises: determining the objective dose distribution for the radiation delivery plan by stages, based on one of or a combination from the different control strategies and the predicted dose distribution per stage as discussed above. With respect to independent claim 19, Hartman teaches in Fig. 1 and Fig. 3 a device for determining a radiation delivery plan, comprising: circuitry of a predicted dose distribution determination module, configured to obtain an image of a target object, and determine a predicted dose distribution of the target object according to the image of the target object; and circuitry of an objective dose distribution determination module, configured to determine an objective dose distribution for the radiation delivery plan based on the predicted dose distribution and a plan-quality control strategy. With respect to dependent claim 20, Hartman should have a computer equipment in paragraph [0022], comprising a memory and a processor, wherein the memory having a computer program stored thereon, and the processor, when executing the computer program, performs steps of the method of claim 1 as discussed above. Allowable Subject Matter Claims 3, 5, 7, 11 – 12, and 15 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. The following is a statement of reasons for the indication of allowable subject matter: With respect to dependent claim 3 and its dependent claim 5, the prior art of record fails to teach or reasonably suggest: wherein: determining the objective dose distribution for the radiation delivery plan based on the predicted dose distribution and the plan-quality control strategy comprises: determining the objective dose distribution for the radiation delivery plan by stages, based on one of or a combination from the different control strategies and the predicted dose distribution per stage. With respect to dependent claim 7, the prior art of record fails to teach or reasonably suggest: wherein, determining the objective dose distribution for the radiation delivery plan based on the predicted dose distribution and the plan-quality control strategy, comprises: selecting any control strategy from the plan-quality control strategy as a current control strategy; optimizing a current dose distribution according to the current control strategy to obtain an optimized dose distribution; and using the optimized dose distribution as an updated current dose distribution, and using any remaining control strategy in the plan-quality control strategy as an updated current control strategy, returning to execute the step of optimizing the current dose distribution according to the current control strategy to obtain the optimized dose distribution, until all control strategies of the plan-quality control strategy are traversed, and using the optimized dose distribution obtained finally as the objective dose distribution. With respect to dependent claim 11, the prior art of record fails to teach or reasonably suggest: wherein: the priority of the organ-at-risk region comprises a high priority, a medium priority, and a low priority; and adjusting the initial dose distribution according to dose distribution reference information to obtain the predicted dose distribution, comprising: selecting the minimum value between the dose limit of the organ-at-risk region and the initial dose distribution to be the predicted dose distribution of the organ-at-risk region with the high priority; and determining the predicted dose distribution of the organ-at-risk region with the medium priority or the low priority according to the initial dose distribution. With respect to dependent claim 12, the prior art of record fails to teach or reasonably suggest: wherein before inputting the image of the target object into the dose-distribution predicting model to obtain the predicted dose distribution, the method further comprises: obtaining a training sample image set, contouring a target region in the training sample image set, and obtaining gold standard dose information of a dose distribution in the target region and outside the target region; and inputting the contoured training sample image set and the gold standard dose information into an initial neural network, and performing an iterative training according to a preset loss function, until a preset iteration condition is met to obtain the dose-distribution predicting model. With respect to dependent claim 15, the prior art of record fails to teach or reasonably suggest: wherein: the target object comprises a target region and an organ-at-risk region; the priority of the organ-at-risk region comprises a high priority, a medium priority, and a low priority; and determining the predicted dose distribution of the target object by adjusting the initial dose distribution according to the dose distribution reference information comprises: selecting the minimum value between the dose limit of the organ-at-risk region and the initial dose distribution to be the predicted dose distribution of the organ-at-risk region with the high priority; and determining the predicted dose distribution of the organ-at-risk region with the medium priority or the low priority according to the initial dose distribution. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIHO KIM, Ph.D. whose telephone number is (571)270-1628. The examiner can normally be reached M-F: 8-5 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, David Makiya can be reached at (571)272-2273. 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. KIHO KIM, Ph.D. Primary Examiner Art Unit 2884 /Kiho Kim/Primary Examiner, Art Unit 2884
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Prosecution Timeline

Aug 19, 2024
Application Filed
Feb 24, 2026
Non-Final Rejection — §102 (current)

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Prosecution Projections

1-2
Expected OA Rounds
85%
Grant Probability
90%
With Interview (+4.2%)
2y 0m
Median Time to Grant
Low
PTA Risk
Based on 1661 resolved cases by this examiner. Grant probability derived from career allow rate.

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