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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 5, 2026 has been entered.
Status of Claims
In the response filed February 5, 2026, Applicant amended claims 1, 3, and 14. Claims 6, 7, and 12 were canceled, and claim 10 was previously canceled. Claims 1-5, 8, 9, 11, and 13-22 are pending in the current application.
Information Disclosure Statement
The information disclosure statements (IDS) received on February 5, 2026 have been considered by examiner.
Response to Arguments
Applicant's arguments with respect to the rejection under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant asserts that the claims are subject matter eligible because the claims recite the use of Automatic Exposure Control (AEC) and machine learning model. Examiner respectfully disagrees. The identified limitations, under its broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. For example, but for the processor, memory, imaging device, and machine learning model (interpreted as computer environment), the context of the claim encompasses a radiologist collecting sets of data to determine a set of exposure technique parameters to generate an image. The claim limitations fall within the Mental Processes groupings of abstract ideas. The performance of the claim limitations using generic computing components does not preclude the claim limitations from being in the Mental Processes grouping. Moreover, the claims recited conventional machine learning models without specific improvements to the technology itself. In Recentive Analytics, the court noted that "iterative training," a claimed feature, was inherent to all machine learning models and thus did not confer eligibility. Additionally, applying machine learning to event scheduling, an activity predating computers, did not transform the abstract idea into a patent-eligible invention. In this case, simply applying generic machine learning techniques to mammography without improving the underlying technology is insufficient for patent eligibility. The rejection is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 8, 9, 11, and 13-22 are rejected under 35 U.S.C. 101 because the claims are not directed to patent eligible subject matter.
Claims 1-5, 8, 9, 11, and 13-22 do fall within at least one of the four categories of patent eligible subject matter because the claims recite a machine (i.e., non-transitory storage medium and system) and process (i.e., a method).
Although claims 1-5, 8, 9, 11, and 13-22 fall under at least one of the four statutory categories, it should be determined whether the claim wholly embraces a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception (See MPEP 2106 I and II).
Claims 1-5, 8, 9, 11, and 13-22 are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Part I: Step 2A, Prong One: Identify the Abstract Idea
Under step 2A, Prong One of the Alice framework, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). The determination consists of a) identifying the specific limitations in the claim that recite an abstract idea; and b) determining whether the identified limitations fall within at least one of the three subject matter groupings of abstract ideas (i.e., mathematical concepts, mental processes, and certain methods of organizing human activity).
The identified limitations of independent claim 1 (representative of independent claim 14) recite:
a processor; and
memory coupled to the processor, the memory comprising computer executable instructions that, when executed, perform a method for setting automated exposure parameters for the X-ray imaging device, the method comprising:
a processor; and
memory coupled to the processor,
the memory comprising computer executable instructions that, when executed, perform a method for setting automated exposure parameters for the X-ray imaging device, the method comprising:
receiving a first data set comprising patient-specific data of a patient, the patient- specific data including at least one of: breast thickness data and a foreign object indicator identifying presence of implants or foreign objects in a breast of the patient;
receiving a second data set comprising an equipment-specific information, the equipment-specific information indicating attributes or accessories of the X-ray imaging device including at least one of: compression paddle type, compression paddle shape, compression paddle size, imaging mode, anode material, or imaging filter type, wherein the equipment-specific information indicating attributes affect required radiation exposure levels;
receiving a third data set comprising prior image data of the patient collected during one or more previous patient visits, the prior image data including at least one of: pixel data, breast density values, or exposure technique parameters used in acquiring the prior images;
providing the first data set, the second data set, and the third data set as input to an exposure control model trained to output patient-specific exposure technique parameters that account for patient anatomical characteristics, equipment-specific radiation transmission properties, and historical patient imaging data;
receiving a set of exposure technique parameters as output from the exposure control model, the set of exposure technique parameters comprising at least one of: a kilovoltage peak (kVp) value, a milliamp-seconds (mAs) value, or an X-ray filter combination, wherein the set of exposure technique parameters are determined prior to acquiring an image of the patient during a current patient visit; and
acquiring the image of the patient, using the X-ray imaging device, based on the set of exposure technique parameters configured to minimize radiation exposure to the patient, wherein acquiring the image is performed without acquiring a scout image.
The identified limitations, under its broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. For example, but for the processor, memory, imaging device, and machine learning model (interpreted as computer environment), the context of the claim encompasses a radiologist collecting sets of data to determine a set of exposure technique parameters to generate an image. The claim limitations fall within the Mental Processes groupings of abstract ideas. The performance of the claim limitations using generic computing components does not preclude the claim limitations from being in the Mental Processes grouping. Thus, the claim recites an abstract idea.
Part I: Step 2A, prong two: additional elements that integrate the judicial exception into a practical application
Under step 2A, Prong Two of the Alice framework, the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application. In particular, the claims are evaluated to determine if there are additional elements or a combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the judicial exception.
As a whole, the additional elements recite using the processor, memory, imaging device, and machine learning model (interpreted as computer environment) to implement the abstract idea. The processor, memory, imaging device, and machine learning model (interpreted as computer environment) in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Dependent claims 2-5, 8, 9, 11,13, and 15-22, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Since these claims are directed to an abstract idea, the Office must determine whether the remaining limitations “do significantly more” than describe the abstract idea.
Part II. Determine whether any Element, or Combination, Amounts to“Significantly More” than the Abstract Idea itself
Under Part II, the steps of claims, when considered individually and as an ordered combination, do not improve another technology or technical field, do not improve the functioning of the computer itself, and are not enough to qualify as "significantly more". For example, the steps require no more than a conventional computer to perform generic computer functions. As stated above in Prong Two, The processor, memory, imaging device, and machine learning model (interpreted as computer environment) in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Therefore, based on the two-part Mayo analysis, there are no meaningful limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself. Claims 1-5, 8, 9, 11, and 13-22, when considered individually and as an ordered combination, are rejected as ineligible subject matter under 35 U.S.C. 101.
Dependent claims 2-5, 8, 9, 11,13, and 15-22 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional claims do no recite significantly more than an abstract idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Yin et al. (US 2022/0133258 A1), SYSTEMS, METHODS, DEVICES AND STORAGE MEDIUMS FOR OBTAINING A RADIOGRAPHIC IMAGE
Hao et al. (US 2020/0381125 A1), SYSTEMS AND METHODS FOR SCAN PREPARATION
Bhatia et al. (US 2015/0199478 A1), SYSTEMS AND METHODS FOR IDENTIFYING MEDICAL IMAGE ACQUISITION PARAMETERS
Reiner (US 2011/0257919 A1), Method And Apparatus For Automated Quality Assurance In Medical Imaging
Reiner (US 2008/0103834 A1), Method And Apparatus Of Providing A Radiation Scorecard
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The aforementioned references teach or suggest receiving a first data set comprising patient-specific data of a patient (Bhatia Paragraph [0029], also described in Yin, Hao, and Reiner); receiving a second data set comprising an equipment-specific information, the equipment-specific information indicating attributes or accessories of an imaging device (Bhatia Paragraph [0028]); receiving a third data set comprising prior image data of the patient (Bhatia Paragraph [0026]). Moreover, the references describe optimizing medical imaging by identifying and associating various imaging parameters. Bhatia, Hao, and Yin describe using machine learning or machine learning models. However, the aforementioned references do not teach or suggest providing the first data set, the second data set, and the third data set as input to a machine learning model; receiving a set of exposure technique parameters as output from the machine learning model, the set of exposure technique parameters comprising at least one of: a kilovoltage peak (kVp) value, a milliamp-seconds (mAs) value, or an X-ray filter combination; and acquiring an image of the patient based on the set of exposure technique parameters because the machine learning model is not inputted with all three data sets prior to acquisition of the image (see dependent claim 2), and the output of the machine learning model does not include a set of exposure techniques such as a kilovoltage peak (kVp) value, a milliamp-seconds (mAs) value, or an X-ray filter combination.
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/CHINYERE MPAMUGO/Primary Examiner, Art Unit 3685