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 § 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, 7-9, 11-13, 15-27 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to A system configured to make a medical prediction without significantly more. The claim(s)
recite(s) a trained neural network in communication with the image storage and configured to generate
a quantitative prediction for the patient's expected benefit from the use of a pharmaceutical, based on
the stored non-invasive images. This judicial exception is not integrated into a practical application
because the recitation of "using a trained neural network" merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element "using a trained neural network" limits the identified judicial exceptions "generate a quantitative prediction using the trained neural network" this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an
inventive concept to the claims. See MPEP 2106.05(h)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because Additional elements (image analysis logic); (user interface), (feedback logic) and (image storage) were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
As discussed above, the broadest reasonable interpretation of steps (b) is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
Specifically, step (b) recites "generate a quantitative prediction for the patient's expected benefit from the use of a pharmaceutical" which may be practically performed in the human mind using observation, evaluation, judgment, and opinion. Under its broadest reasonable interpretation when read in light of the specification, the "generating" encompasses mental observations or evaluations that are practically performed in the human mind.
The Step of "dividing the set of non-invasive images" using the trained network to generate predictive; encompasses performing evaluation, judgment, and opinion to make a determination about detected anomalies. Under its broadest reasonable interpretation when read in light of the specification, the "comparing" encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
As discussed above, the broadest reasonable interpretation of dividing also encompasses mathematical concepts that can be performed mentally. The limitations "(a) receiving, at a computer" and "comparing images from the trained ANN" are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) ("whether the limitation is significant"). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP
Further, limitations (a), (b), and (c) are recited as being performed by a microprocessor. The microprocessor is recited at a high level of generality. The microprocessor is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). In limitations (b) and (c), the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f)
These limitations "using the trained neural network" provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f).
Claims 2-5, 7-8, 11-12, 15-26, 28-29 are rejected for the same reasons.
Response to Arguments
Applicant's arguments filed 5/29/25 have been fully considered but they are not persuasive.
Applicant argues
Independent claim 1 recites a system comprising an image generator, image storage, trained neural-network-based image-analysis logic, user interface, and microprocessor. Independent claim 9 recites a method comprising receiving non-invasive images of the body parts of a patient, dividing the images into a training set and a validation set, providing the images to a neural network, and using the neural network to make a prediction. Independent claim 13 recites generating non-invasive images, providing such images to a neural network that is trained to predict a quantitative benefit from exposure to a pharmaceutical. Independent claim 27 is likewise directed to a method.
Each claim is therefore either a "machine" or "process" under 35 U.S.C. § 101 and satisfies Step 1 (MPEP § 2106.03).
The examiner disagrees because These limitations "using the trained neural network" provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f).
III. Step 2A, Prong 1 The Claims Are Not "Directed To" an Abstract Idea
The Office Action characterizes the claims as directed to "a system configured to make a medical prediction" and asserts that step (b) of claim 1-"generate a quantitative prediction for the patient's expected benefit from the use of a pharmaceutical"-"may be practically performed in the human mind," falling within the "mental process" grouping of abstract ideas. That characterization is inconsistent with the actual claim language and the specification and what is possible to do "in the human mind".
When the claims are read as a whole, they are directed not to a disembodied concept of predicting drug benefit," but to a specific technical system and training pipeline for generating and using medical predictions from non-invasive images. The specification explains that conventional imaging systems and statistical models are unable to determine patient specific drug response from non-invasive images; they focus on disease detection or anatomical assessment rather than on predicting the expected benefit from a pharmaceutical. The invention addresses this technical problem in a technical field-AI based medical imaging. See for example:
"[U] sing non-invasive medical imaging techniques and advanced machine learning algorithms to determine the expected effect of a patient's medication use to enhance drug effectiveness and safety in healthcare and related sectors." ( " [0002]-[0004] of the application published as Furthermore, the claimed neural-network operations are applied to medical image data to generate quantitative treatment-benefit predictions, not to merely abstract mathematical computation results or generic mental steps.
The Examiner's "mental process" characterization is inconsistent with the claim language The rejection treats the Applicant's claimed requirement to "generate a quantitative prediction for the patient's expected benefit from the use of a pharmaceutical" as a mental step that "may be practically performed in the human mind using "observation, evaluation, judgment, and opinion". The rejection also extends that reasoning to other limitations such as dividing image sets. Under the broadest reasonable interpretation, however, these steps are expressly implemented by a computer system that processes non-invasive medical images using a trained neural network.
The examiner disagrees because these limitations are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) ("whether the limitation is significant"). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting.
Thus, the recited "generating" is not untethered human reasoning; it is the output of a particular neural-network-based image-analysis module operating under control of a microprocessor and consuming digital image data acquired by specialized imaging hardware.
Under MPEP § 2106.04(a)(2), the "mental process" category covers only acts that can be performed in the human mind (or with pen and paper). It is not reasonable to ignore the express limitations requiring a trained neural network, digital image data, and a microprocessor-implemented system.
The examiner disagrees because These limitations "using the trained neural network" provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f).
Applicant argues:
Processing non-invasive image data with a trained neural network is not a mental process The specification describes that the image-analysis logic processes high-dimensional image data (e.g., ultrasound images of anatomical structures) using a trained neural-network architecture employing techniques such as pretraining, data augmentation, quantile regression, and transformer aggregation. See of the published application. These are large-scale numerical operations over millions and millions of pixels and neural network parameters - they are not mere mental observations or opinions.
Human beings cannot practically perform such matrix and tensor computations in their minds for each image and each neural network layer. As the Appeals Review Panel explained in Ex parte Desjardins, the mere fact that one may conceptually reason about a prediction problem does not render claims "directed to" a mental process when the claim actually requires machine-learning operations on high-dimensional data. The same is true here: the claims require a neural network operating on the non-invasive digital medical image data sets to output a quantitative prediction of drug benefit.
The examiner disagrees because these limitations are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) ("whether the limitation is significant"). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting.
Applicant argues
Dependent claim 8 (and the new dependent claims discussed below) recites feedback behavior in which results of the neural-network inference are used to request additional non-invasive images when prediction precision is inadequate. The specification explains that Feedback Logic 170 may, for example, instruct an operator to "obtain images showing entire kidney" when additional ultrasound images are desirable. 19[003]-[0039]. This feedback loop controls imaging hardware and operator actions; it is not a mental step.
The examiner disagrees because the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because Additional elements (image analysis logic); (user interface), (feedback logic) and (image storage) were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Applicant argues:
New independent claim 27 similarly provides feedback to direct the user in selecting additional images known to improve the quality of the quantitative prediction.
Method claims 9 and 13 recite concrete steps of receiving non-invasive images, dividing them into training and validation sets, providing them to a neural network, training the neural network with associated labels, and using the trained network to generate a quantitative prediction of drug benefit. These are specific machine-learning training and inference pipelines, not mental processes.
The examiner disagrees because The Step of "dividing the set of non-invasive images" using the trained network to generate predictive; encompasses performing evaluation, judgment, and opinion to make a determination about detected anomalies. Under its broadest reasonable interpretation when read in light of the specification, the "comparing" encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
Applicant argues
Dependent claim 8 and new claim 27 (and the corresponding disclosure at 11[0037]-[0039]) further illustrates this practical application where feedback logic 170 dynamically guides the imaging device in real time.
"If analysis results in determinations having inadequate precision, Feedback Logic 170 may inform a user that additional ultrasound images are desirable e.g., 'obtain images showing entire kidney.'
This feedback loop controls physical hardware and improves image-acquisition quality-a hallmark of integration into a practical application (MPEP § 2106.04(d)). The computations directly affect how the imaging machine operates.
At least the claims reciting this feedback behavior (e.g., claim 8 and new independent claim 27) therefore satisfy Step 2A, Prong 2 by improving the operation of the imaging device itself
The examiner disagrees because the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because Additional elements (image analysis logic); (user interface), (feedback logic) and (image storage) were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Applicant argues:
Quantitative Technical Benefits
The specification also describes measurable performance gains over other approaches, including at least reduced overfitting and improved generalization (1 [0043]), improved model accuracy and positive predictive value in validation studies (11 [0089]-[0091]), and decreased operator dependence and improved imaging efficiency (II [0092]-[0093]). Such empirical improvements demonstrate that the invention yields a practical technological effect, not a merely abstract result. See McRO Inc. V. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016)
The U.S. Patent Office's own memo dated August 5, 2025 titled "Reminders on Evaluating Subject Matter Eligibility of Claims Under 35 U.S.C. 101" is also instructive, as it contains a reminder that examiners can conclude that claims are eligible in Step 2A Prong 2 by finding that a claim reflects an improvement to the functioning of a computer or to another technology or technical field.
VII. The Claims are Directed to Patentable Subject Matter
When properly analyzed under the Alice/Mayo framework and the USPTO's eligibility guidance. The claims recite a specific technological improvement in image-based medical prediction systems. They integrate any mathematical computations or mental steps into a practical, hardware-implemented medical application that provides technical performance benefits such as reduced imaging error, improved model accuracy, and efficient guided acquisition.
The examiner disagrees because these limitations are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) ("whether the limitation is significant"). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOEL F BRUTUS whose telephone number is (571)270-3847. The examiner can normally be reached Mon-Sat, 11:00 AM to 7:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Kozak can be reached at 571-270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOEL F BRUTUS/ Primary Examiner, Art Unit 3797