DETAILED ACTION
This Office action is responsive to communications filed on 06/23/2026. Presently, Claims 1-8 remain pending and are hereinafter examined on the merits.
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 .
Response to Arguments
Previous rejections under 35 USC § 112(b) are withdrawn in view of the amendments filed on 06/23/2026.
Previous claim objections are withdrawn in view of the amendments filed on 06/23/2026.
The Applicant’s arguments with respect to rejections under 35 USC § 112(a) have been fully, considered, but are not persuasive.
The rejection is not based on whether the specification mentions a first model, a second model, their general inputs and outputs, or the surrounding imaging workflow. Rather, the rejection is based on the absence of disclosure showing how the model performs the claimed computer-implementation functions.
Paragraphs, ¶0044, ¶0045, ¶0051, ¶0056 identify categories of data provided to the first model, identifying a timing prediction and confidence levels as desired outputs, and provide a high level of generality to describe the training of the data and loss function. These paragraphs do not disclose the operations by which the model transforms the patient information and clinical task into the first estimated time or calculates the first confidence level. Similarly, paragraphs ¶0048, ¶0059, ¶0060, & ¶0062 identify information supplied to the second model and describe the generic workflow followed when the first confidence level is below the threshold, but it does not disclose how the second model combines the updated patient information, first estimated time, first confidence level, and monitoring scan data to calculate the second estimate time and second confidence level. These paragraphs merely identify the model types, inputs, outputs, and the training data used, and the desired result to be produced, but does not reasonably “demonstrate” procession of the particular manner of producing the desired result (i.e., the specification does not reasonably provide the operational computation procedure.).
The Applicant’s reliance on FIG. 4 and the computing device does not obviate this deficiency. FIG. 4 is merely a high-level flow chart for the scanning procedure, not an algorithmic flow chart of the deep learning model. FIG. 4 just demonstrates that the patient information is input into the model and an estimated time and confidence level are received from the model. At best, it’s a functional block diagram, without disclosing of the intervening computational operations that generate the claimed outputs. Indeed, the specification characterizes a confidence level both as a probability and as a plus-or-minus time interval, but doesn’t explain how either value is derived.
The Applicant’s assertion that a person of ordinary skill could write a program to perform the claimed functions does not establish written description support and instead confirms that the missing implementation is being supplied by the skilled artisan rather than the original disclosure. This is further supported by the Applicants own admission, “there are multiple ways to program a learning algorithm to achieve the same claimed technological workflow”-pg. 9, which confirms there is indeed missing information within the disclosure. The Applicant further states “our claims are directed to a use of a machine learning algorithm in a technological workflow and not a specific algorithm”-pg. 10. This argument underscores the deficiency because the claims encompass deep learning implementations, while the specification does not reasonably provide the operational computation procedure.
The Applicant’s reliance on the rejection as requiring the claims and/or specification themselves to recite a specific algorithm (i.e., source code), is a mischaracterization of the rejection made. The issue is not that the Specification fails to provide source code. The issue is whether the Specification reasonably conveys possession of the claimed subject matter showing how the inventor intended the claimed estimates and confidence levels to be generated/determined via the deep learning models. Ultimately, the Specification describes the idea of these steps using a first model, a second model, their general inputs and outputs, in a high level of generality; however, at its core, it stops short of reasonably provided proper written description of how the computing device actually performs these identified steps.
Accordingly, the 35 USC § 112(a) rejection is maintained, See MPEP 2161.01.
The Applicant’s arguments with respect to rejections under 35 USC § 101 have been fully, considered, but are not persuasive.
Applicant argues that the recited limitations of a first and second deep learning models prevents the claimed generating and determining limitation from being characterized as a mental process. However, claim 1 does not require any particular model, architecture, training procedure, any specific operation, or processing technique in general to obviate the abstract idea from amounting to a mental process. The models are recited only by the results they produce. Under the broadest reasonable interpretation, the recited operations of the deep learning models amount to evaluating patient and task information, estimating an appropriate scan time, assigning confidence levels to that estimate, comparing the confidence levels with a threshold, and revising the estimate based on updated information. These particular limitations indeed, as its generically recited in the claims, amount to evaluations and judgments that can practically be performed mentally. Merely assigning those operation to a generically recited deep learning models does not change the nature of the analysis. Applicant’s assertion that a person could not perform the analysis “with any accuracy”-pg. 12 is not commensurate with the scope of the claim, which recites no required degree of accuracy and/or complexity.
The Applicant’s reliance concerning reduced radiation exposure, fewer diagnostic or monitoring scans, improved image quality, reduced energy consumption, reduced wear, and improved patient care, and use of contrast agent is not commensurate with claim 1. The claim 1 does not require these asserted advantages. The claim 1 does not require that the selected time be the best time for obtaining an image. These alleged advantages therefore do not establish that the claim integrates the judicial exception into a practical application.
The claim has been considered as a whole, including the limitation of “control, via an x-ray source controller and a gantry motor controller, the x-ray source and the detector to perform the diagnostic scan of the subject at the first estimated time in response to the first confidence level of the first estimated time being above a threshold;” The limitation merely uses conventional imaging components and hardware to perform their ordinary imaging functions once the estimated confidence satisfies the threshold. The claim does not recite any improvement to the operation of the x-ray source, detector, gantry, data acquisition, computing device, or the imagine acquisition. Rather, the imaging system provides the technological environment in which the abstract determinations and generating are applied. Performing a conventional diagnostic scan at the time selected through the recited analysis does not, without significantly more, transform the analysis into a improvement to the technology itself.
Applicant’s reliance on BASCOM, is also not persuasive. Claim 1 does not recite any specific nonconventional arrangement of conventional components. It just broadly recites using a first deep learning model to produce an estimate and confidence level, and when the confidence level is below a threshold, using a second deep learning model to produce another estimate and confidence level. No particular technological arrangement or interaction between the models and the imaging components is claimed. Applicant’s assertation that the generating and determining steps “are critical in achieving the purpose of the invention”-pg. 13, does not establish an inventive concept. The additional imaging and computing components, individually and in combination, merely implement and apply the recited abstract idea using conventionally employed components and hardware and therefore do not amount to significantly more than the judicial exception.
Accordingly, the 35 USC § 101 rejection is maintained.
Examiners Notes
Claims 1-8, though rejected under 35 USC § 112(a) & 35 U.S.C § 101 are not rejected under the prior arts. The claims are statutorily ineligible for indication of allowable subject matter. Note; a change in scope in view of the requested corrections will require further search and consideration.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1: recites:
“generate, via a first deep learning model, a first estimated time to perform a diagnostic scan of the subject based on patient information and clinical task;
determine, via the first deep learning model, a first confidence level of the first estimated time;
[...]
generate, via a second deep learning model, a second estimated time to perform the diagnostic scan and a second confidence level of the second estimated time in response to the first confidence level of the first estimated time being below the threshold,”
An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340 (Fed. Cir. 2008) (internal citation omitted). This can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015), see MPEP § 2161(I).
The claim is rejected under 35 USC § 112(a) for a lack of written description. Proper written description cannot be identified in the specification, claims, and drawings directed to the computer implemented steps for how the first estimated time to perform a diagnostic scan of the subject based on the patent information and clinical task is generated via first deep learning model, how the first confidence level of the first estimated time is determined via the first deep learning model, & how a second estimated time to perform the diagnostic scan and a second confidence level of the second estimated time in response to the first confidence level of the first estimated time being below is generated via a second deep learning model. Specifically, the specification does not provide and lacks detailed a step-by-step description, any algorithmic or flowchart-based disclosure, specific functions, and/or weights for the machine learning techniques or the specifics of convolutional networks or recurrent neural networks used for these steps.
These limitations are computer/processor-implemented functional claim limitation as it is directed to a processor-controlled algorithm configured to determine a location. Yet the specification does not disclose the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed functions, i.e., ““generate, via a first deep learning model, a first estimated time to perform a diagnostic scan of the subject based on patient information and clinical task; determine, via the first deep learning model, a first confidence level of the first estimated time; [...] generate, via a second deep learning model, a second estimated time to perform the diagnostic scan and a second confidence level of the second estimated time in response to the first confidence level of the first estimated time being below a threshold,” in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. It is not enough to disclose that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015). As the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention, these claims are rejected for lack of written description. For more information regarding the written description requirement, see MPEP §§ 2161, 2162-2163.07(b).
The specification does not provide proper written description describing the specific weights, internal mathematical operations, layer-by-layer parameters, or detailed algorithmic step-by-step charts of the neural network/deep learning models. Without the level of detail regarding the weights used, parameters used, and operations using machine learning models, the written description requirement is not satisfied. The specification focuses on clinical application, system workflow, and high-level architecture of the models.
Regarding algorithms, the specification provides the following:
¶0047, ‘To that end, the first model 301 may comprise a supervised or a partially supervised deep machine learning algorithm or algorithms. For example, the first model 301 may comprise a combination of random forest(s) and support vector machine(s). As another example, the first model 301 may comprise a combination of recurrent neural networks and convolutional neural networks. In some examples, the first model 301 may comprise a long short-term memory network (LSTM). Additionally or alternatively, the first model 301 may comprise an unsupervised deep learning algorithm or algorithms. For example, the first model 301 may include a neural network that undergoes unsupervised training to identify monitoring locations 323 based on the scout data 313 and other inputs of the plurality of inputs 310. The monitoring locations 323 comprise one or more regions of interest (ROIs) that may be imaged to evaluate the contrast enhancement at a given time. In some examples, the monitoring locations 323 may be different than a scan field of view or ROI. For example, the monitoring location may be different than the scan location because the enhancement may be better in a location that is not part of the region of interested (e.g., scan location). Additionally, the model may output multiple monitoring locations. It should be appreciated that the first model 301 may include a neural network that instead undergoes supervised training to identify the monitoring locations 323.’
The specification broadly lists the types of machine learning algorithms that might be used, stating the models may comprise supervised, partially supervised, or unsupervised algorithms. Specifically, it mentions, combinations of random forest and support vector machines, RNN, and CNN, or LSTMs.
Regarding inputs and outputs, the specification, heavily generalizes on what data goes into the models (i.e., patent demographics, EMR, scout data, monitoring data) and what comes out (i.e., estimated time, confidence levels, recommended scanning parameters), see ¶0045-0049.
Regarding the training, the specification describes that models are trained using curated database of previous patient scans, filtering the data against baseline image quality metrics like image noise, texture, and x-ray dose, ¶0046, ¶0051. There is mention of a loss function determined based on the difference from the desired Hounsfield unit enhancement level, ¶0051, but not actual mathematical formulas or specific parameter tuning steps are provided.
While the instant application references FIG. 4, this chart outlines the overall clinical workflow of the imaging system, not the algorithmic details of the deep learning models, ¶0053. In fact, no image provides algorithmic details of the deep learning models. The flow charts detail steps like performing a scout scan, inputting data into the first model, evaluating the confidence threshold, triggering a monitor scan if the confidence is low, and using the second model to refine the estimate, ¶0054-0063.
Therefore, the specification and drawings are directed to mere examples of generalized machine learning models tantamount to a black box, rather than showing procession of a particular implementation. In other words, the models are described as functional blocks within a larger diagnostic imaging system, defining what the models must learn and the data they process, but omit the granular, step-by-step mathematical and architectural details of how the deep learning models compute their predictions. One of ordinary skill in the art would not be able to implement the described process without disclosure of said weights, parameters, and/or operations of the machine learning techniques in a step-by-step manner.
In addition, an assertion that could be derived using simulations or test (i.e., prophetic examples) does not demonstrate that the inventors’ actual did so or had possession of the specific functional relationships and constraints to obviate the lack of written description requirement.
Consequently, one of ordinary skill in the art would not deem the instant specification having sufficient detail so that they could understand how the inventor intended to achieve the aforementioned step. Since the instant specification fails to provide a finite sequence of steps for performing step, the aforementioned claim fails to meet the written description requirement under 35 U.S.C. 112(a).
Dependent claims are rejected by virtue of their dependency to abovementioned claims.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 1-8 are directed to an “apparatus” which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One:
Claim 1 recites (“sets forth” or “describes”) the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), substantially as follows:
“ generate, via a first deep learning model, a first estimated time to perform a diagnostic scan of the subject based on patient information and clinical task;
determine, via the first deep learning model, a first confidence level of the first estimated time;
[...]
and; generate, via a second deep learning model, a second estimated time to perform the diagnostic scan and a second confidence level of the second estimated time in response to the first confidence level of the first estimated time being below the threshold, wherein the second estimated time is based on updated patient information, the first estimated time, the first confidence level, and data acquired during one or more monitoring scans. ”
In claim 1, the above recited steps can be practically performed in the human mind, (i.e., by a medical professional) mentally using reference tables of information. A technician could review available patient information together with clinical task to be performed and consult a reference sheet (i.e., a datasheet) containing previously complied examples of scan durations for similar situations. In this context, the first deep learning model can be understood as a datasheet or table that correlates patient characteristics and clinical task with typical scan durations derived from prior cases. By mentally comparing the current patient information to entries in the table, the person can estimate an appropriate time to perform the diagnostic scan. The same datasheet may also include an associated values or indicators reflecting consistent time which have worked in past situations, thereby allowing the person to correspond confidence levels for the estimated time. The individual can then mentally compare that confidence level to an estimated mentally noted predetermined threshold and decide whether these estimate is sufficient with the scan time.
If the confidence level appears low, the person can update their prior assessment using additional information gathered from observations of scans, such as initial imaging feedback or new patient details. The person could then consult another reference data sheet (i.e., a second deep learning model) that list revised scan times based on inputs such as updated patient information, and estimated times, and previously determined confidence levels. By mentally comparing these inputs to the entries in this second table, the persons can derive mental estimated scan times and determine a corresponding confidence level for that estimate. The processed steps involve merely estimating known information comparing it against stored tabulated examples, and forming judgements about timing and confidence, all of which can be carried out mentally by a human reviewing and reasoning referenced data. There is nothing recited in the claim to suggest an undue level of complexity in the generating and determining steps are conducted.
Prong Two: Claim 1 does not include additional elements that integrate the mental process into a practical application.
This judicial exception is not integrated into a practical application. In particular, the claims recites (1) additional steps of “an x-ray source that emits a beam of x-rays towards a subject to be imaged; a detector that receives the x-rays attenuated by the subject; a data acquisition system (DAS) operably connected to the detector; and a computing device operably connected to the DAS and configured with executable instructions in non-transitory memory that when executed cause the computing device to:”- (claim 1); and (2) further an additional step of “control, via an x-ray source controller and a gantry motor controller, the x-ray source and the detector to perform the diagnostic scan of the subject at the first estimated time in response to the first confidence level of the first estimated time being above a threshold;”
The steps in (1) represent merely data gathering or pre-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality with conventionally used tools (see below Step IIB for further details). Data gathering and mere instructions to implement an abstract idea on a computer do not integrate a judicial exception into a practical application (MPEP 2106.05 (f and g)).
Regarding the processor language “data acquisition system” & “computing device” written at such a high level of generality of structural limitations, the processor language amounts to a generic computer component with mere instructions to implement the abstract idea on a computer.
The step in (2) represents merely post-solution activity and is recited at a high level of generality, not a practical application, nor a technological solution that solves a technological application in a meaningful way.
Regarding the limitations of claim 1, directed to the “computing device” for the generating and determining steps” is treated as a generic computer implementation, which falls under mere instructions to apply the abstract idea on a computer and therefore does not place the abstract idea into a practical application that solves a technological solution in a meaningful way or improve the functionality of the technology or generic computer “itself”. Simply, it’s a generic computer implementation of a mental process rather than a meaningful limitation. Regarding the processor language written at such a high level of generality of structural limitations, the processor language amounts to a generic computer component with mere instructions to implement the abstract idea on a computer.
As a whole, the additional elements merely serve to gather and feed information to the abstract idea and to output a notification based on the abstract idea, while generically implementing it on conventionally used tools. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the estimated diagnostic information is not outputted in any way such that a practical benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, there is no evidence of record that would support the assertion that this step is an improvement to a computer or technological solution to a technological problem. Ultimately, the Applicant’s describe improvement in the process of using diagnostic techniques, but this is not an improvement in the function of a computer or other technology (See MPEP 2106.05(a)(ii); “the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology”; See MPEP 2106.04(d)(1); 2106.05(a); and 2106.05(f)). The claims are directed to the abstract idea. Also, there does not appear to be any particular structure or machine, treatment or prophylaxis, transformation, or any other meaningful application that would render the claim eligible at step 2A, prong 2.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claim 1 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims recite additional steps of an x-ray source emitting x-rays, a detector receiving the x-rays, a data acquisition system (DAS) and a computing device configured to receive instructions, with control of a x-ray source and gantry motor controller. These steps represents mere data gathering, data outputting or pre/post/extra-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality. Furthermore, as discussed above, limitations with respect to the processor languages/terms, respectively, amount to mere instructions to implement the abstract idea on a computer. As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than insignificant extra solution activity and mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept. The data gathering steps that were considered insignificant extra-solution activity in Step 2A Prong Two, have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field.
As an evidence, Thienphrapa et al (US 20200275982 A1) discloses:
¶0034, ‘As known in the art of the present disclosure, X-ray modality 10 generally5 includes an X-ray generator 11, an image intensifier 12 and a collar 13 for rotating X-ray modality 10. In operation as known in the art, an X-ray controller 14 controls a generation by X-ray modality 10 of X-ray imaging data 15 informative of a X-ray imaging of the anatomical region of patient P (e.g., a heart of patient P during a minimally invasive aortic valve replacement).’
¶0040, ‘Encoder(s) 32 are any type of encoder as known in the art of the present disclosure for generating robot pose data 34 informative of a location and/or orientation of each arm/link of interventional robot 30 relative to a reference to thereby facilitate a determination by an interventional controller 70 of a pose of intervention tool 31 as held by interventional robot 30 within the anatomical region of patient P.’
¶0045, ‘Each processor may be any hardware device, as known in the art of the present disclosure or hereinafter conceived, capable of executing instructions stored in memory or storage or otherwise processing data. In a non-limiting example, the processor may include a microprocessor, field programmable gate array (FPGA), application-specific integrated circuit (ASIC), or other similar devices.’
¶0049, ‘The storage may include one or more machine-readable storage media, as known in the art of the present disclosure or hereinafter conceived, including, but not limited to, read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash-memory devices, or similar storage media. In various non-limiting embodiments, the storage may store instructions for execution by the processor or data upon with the processor may operate. For example, the storage may store a base operating system for controlling various basic operations of the hardware. The storage stores one or more application modules in the form of executable software/firmware for implementing the various functions of monitor controller 60 and interventional controller 70 as further described in the present disclosure.’
¶0051, ‘Still referring to FIG. 1, monitor controller 60 processes X-ray image data 15 to generate an X-ray image 61 and controls a display of X-ray image 61 on monitor 41 as known in the art of the present disclosure. Monitor controller 60 further controls a display on monitor 41of an overlay or a fusion 86 of a roadmap 82 onto X-ray image 74.’
For these reasons, there is no inventive concept. The claim is not patent eligible. Even when viewed as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent Claims
The following dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons:
defining wherein the first estimated time comprises a timing prediction of peak contrast enhancement in a region of interest (ROI) of the subject. – (claim 6)
defining wherein the first deep learning model further outputs recommended scan parameters and/or reconstruction parameters. (claim 7).
defining wherein the second deep learning model further outputs recommended scan parameters and/or reconstructions parameters. (claim 8)
The following dependent claims merely further describe the extra-solution activities and therefore, do not amount to significantly more than the judicial exception or integrate the abstract idea into a practical application for similar reasons:
describing wherein the computing device is further configured with the executable instructions in non-transitory memory that when executed cause the computing device to :control the x-ray source and the detector to perform the one or more monitoring scans of a monitoring location of the subject in response to the first confidence level being below the threshold, the one or more monitoring scans comprising a low-dose, short-duration scan relative to the diagnostic scan; and control the x-ray source and the detector to perform the diagnostic scan of the subject at the second estimated time responsive to the second confidence level of the second estimated time above the threshold.– (claim 2)
describing wherein the system includes a patient monitoring sensor to obtain at least a portion of the patient information. (claim 3);
describing further wherein the patient monitoring sensor provides real-time data related to at least one of heart rate, breathing rate, or ejection fraction. –(claims 4);
describing wherein the computing device is further configured with the executable instructions in the non-transitory memory that when executed cause the computing device to: reconstruct an image from data acquired during the diagnostic scan; receive, via an operator console communicatively coupled to the computing device, an indication of image quality for the image; and update one or more of the first deep learning model and the second deep learning model based on the indication of image quality. (claim 5);
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Conclusion
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.
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/N.A.R./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798
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