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) 1-17, 19 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sjolund, et al (U.S. Patent Application Publication 2015/0367145 A1).
Regarding claim 1, Sjolund discloses a quality assurance method (paragraph 0047) comprising:
Obtaining status information of a medical device (paragraph 0047);
Obtaining a target plan of a target subject (paragraph 0047);
Determining a prediction result based on the status information of the medical device and the target plan (paragraph 0047); and determining whether a quality assurance test is passed based on the prediction result (paragraph 0047; “if OARs receive too much radiation under an initial dose plan, the initial dose plan may be rejected).
Regarding claim 2, Sjolund discloses wherein the state of the medical device includes at least one of beam information corresponding to a plurality of dose rates, positioning accuracy information of at least one component of the medical device, or operational error information of the at least one component of the medical device (paragraphs 0047-0052).
Regarding claim 3, Sjolund discloses wherein the obtaining a state of a medical device comprises:
Obtaining a data set, wherein the data set is determined based on at least one candidate plan, limit performance information of at least one component of the medical device, or a calculation limit of a dose model (paragraph 0021); and
Obtaining the state of the medical device by directing the medical device to execute the data set (paragraph 0030).
Regarding claim 4, Sjolund discloses determining the first data set based on at least one first candidate plan, wherein a value of a first candidate parameter in the at least one first candidate plan is within a first range (paragraph 0047).
Regarding claim 5, Sjolund discloses wherein the data set includes at least one sample parameter (DVH) , each of which corresponds to at least one sample parameter value; and
The obtaining the state of the medical device by directing the medical device to execute the data set comprises:
Determining an actual test result related to at least one of the target plan, the target subject or the medical device by directing the medical device to execute the data set, the target plan including at least one target parameter, each of which corresponds to at lest one target parameter value (paragraph 0047);
Determining a data set execution result based on the actual test result; and obtaining the state of the medical device based on the data set execution result (paragraph 0047).
Regarding claim 6, Sjolund discloses wherein determining a prediction result based on the state of the medical device and the target plan of the target subject comprises:
Determining at least one of feature information related to a complexity level of the target plan or a target fluence map based on the target plan of the target subject (paragraph 0056); and
Determining the prediction result based on the state of the medical device and the at least one of the feature information related to the complexity level of the target plan or the target fluence map (paragraph 0056).
Regarding claim 7, Sjolund discloses wherein
The target subject includes a plurality of regions of interest (paragraph 0045);
The prediction result includes dose distributions corresponding to the plurality of ROIs respectively (paragraphs 0045-0046); and
The determining whether a quality assurance test passes based on the prediction result comprises:
Determining a weight corresponding to each ROI of the plurality of ROIs (paragraphs 0045-0046); and
Determining whether the quality assurance passes based on the weights and the dose distributions corresponding to the plurality of ROIs respectively (paragraphs 0045-0047).
Regarding claim 8, Sjolund discloses wherein the determining a prediction result based on the state of the medical device and the target plan of the target subject comprises:
Determining the prediction result based on the state of the medical device and the target subject using a first model, wherein the first model is a machine learning model (paragraph 0109); and
The prediction result includes at least one of a predicted image of the target subject, a gamma passing rate, or a dose distribution (paragraph 0109).
Regarding claim 9, Sjolund discloses, in response to determining that the quality assurance test does not pass based on the prediction result, determining a reason that the quality assurance test does not pass based on the state of the medical device, the target plan of the target subject, and the prediction result using a second model (paragraph 0047).
Regarding claim 10, Sjolund discloses, adjusting, based on the reason that the quality assurance test does not pass, a value of a parameter associated with at least one of the medical device, the target plan, or a dose model (paragraph 0047).
Regarding claim 11, Sjolund discloses wherein the method further comprises:
Determining an updated prediction result based on an adjusted value of the parameter using the first model (paragraph 0030);
Determining whether the quality assurance test passes based on the updated prediction result (paragraph 0030); and
In response to determining that the quality assurance test passes, controlling the medical device to treat or scan the target subject according to an updated target plan, wherein the updated target plan is determined based on the adjusted value of the parameter (paragraph 0030).
Regarding claim 12, Sjolund discloses a method for quality assurance, which is implemented on a computing device including at least one processor and at least one storage device, the method comprising:
Obtaining a state of a medical device (paragraph 0047);
Obtaining a target plan of a target subject (paragraph 0047);
Determining a prediction result based on the state of the medical device and the target plan of the target subject using a quality assurance model (paragraph 0047), wherein the quality assurance model is a machine learning model (paragraph 0109).
Regarding claim 13, Sjolund discloses a method comprising:
Obtaining a data set for quality assurance, the data set including at least one sample parameter, each of which corresponds to at least one sample parameter value (paragraph 0047);
Determining an actual test result related to at least one of a target plan of a target subject or a medical device based on the data set, the target plan including at least one target parameter related to the at least one sample parameter, each of which corresponds to at least one target parameter value (paragraph 0047); and
Determining a quality assurance result related to the at least one of the target plan or the medical device based on the actual test result (paragraph 0047).
Regarding claim 14, Sjolund discloses wherein determining a quality assurance result related to the at least one of the target plan or the medical device based on the actual test result comprises:
Determining a predicted test result based on the data set using a test model; and
Determining the quality assurance result based on the predicted test result and the actual test result (paragraphs 0044-0047).
Regarding claim 15, Sjolund discloses wherein the actual test result includes a test image obtained by the medical device based on the data set (paragraph 0058);
The predicted test result includes a simulated image obtained based on the test model (paragraphs 0047 and 0058); and
The determining the quality assurance result based on the predicted test result and the actual test result comprises:
Determining the quality assurance result based on a difference between the test image and the simulated image (paragraph 0058).
Regarding claim 16, Sjolund discloses wherein the data set is determined based on a plurality of sample plans (paragraph 0047).
Regarding claim 17, Sjolund discloses wherein the plurality of sample plans comprise plans corresponding to different complexity levels (paragraph 0003).
Regarding claim 19, Sjolund discloses wherein the data set includes a plurality of data subsets configured to correspond to at least one of different quality assurance test frequencies or different types of plans (paragraph 0047).
Regarding claim 20, Sjolund discloses determining whether a quality assurance test passes based on the quality assurance result, and in response to determining that the quality assurance test does not pass, adjusting at least one of a parameter of the medical device of the sample parameter value of the data set (paragraph 0047).
Allowable Subject Matter
Claim 18 is 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: the prior art fails to teach determining whether a complexity level of the target plan is within a complexity range of the data set, the complexity range of the data set being determined based on the complexity level of the at least one of the plurality of sample plans corresponding to the data set; and in response to determining that the complexity level of the target plan is within the complexity range, controlling the medical device to perform a medical operation on the target subject based on the target plan, or in response to determining that the complexity level of the target plan is not within the complexity range, proceeding with at least one of adjusting the data set based on the target plan, or performing a quality assurance test on the target plan.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL P MASKELL whose telephone number is (571)270-3210. The examiner can normally be reached M-F 10A-6P.
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/MICHAEL MASKELL/Primary Examiner, Art Unit 2878 04 April 2026