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 .
Election/Restrictions
Claims 6, 8, 15, and 17 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 03/29/2024.
Response to Amendment
This action is in response to the amendment filed on 01/13/2026. Claims1-2, 11-12, and 18-19 have been amended. Claim 10 has been previously cancelled, and claims 6, 8, 15, and 17 were previously withdrawn. Claims 1-5, 7, 9, 11-14, 16, and 18-20 are examined below.
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, 11-14, 16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 11 recites (additional elements crossed out):
A system for training a model using machine learning for automatically distributing medical imaging studies to radiologists, the system comprising:
receive one or more medical images included in a medical study, each of the one or more medical images including image metadata defining characteristics of the corresponding medical image,
receive radiologist metadata for each one of the plurality of radiologists,
generate a state representation of the image metadata and the radiologist metadata,
provide the state representation to the model,
assign, based on the action output via the neural network, at least one of the one or more medical images to one of the plurality of radiologists,
calculate feedback based on a change in the state representation after the at least one of the one or more medical images is assigned to one of the plurality of radiologists, by providing the action output via the neural network to an environment block, wherein the environment block represents an updated state representation following assignment of the at least one of the one or more medical images to one of the plurality of radiologists and calculating a reward based on changes between the state representation and the updated state representation, and
adjust the
wherein calculating the reward includes:
determining whether a value of the reward is positive or negative based on a specialty of the radiologist assigned to the medical image of the one or more medical images and a body part identifier for the medical image of the one or more medical images, wherein the reward is positive when the specialty of the radiologist assigned to the medical image aligns with the body part identifier for the medical image of the one or more medical images.
The above limitations as drafted, is a process that, under its broadest reasonable interpretation covers managing personal behavior or relationships or interactions between people, and mental processes. That is, other than reciting the steps as being performed by an “electronic processor”, “model”, and “neural network”, nothing in the claim precludes the steps as being described as managing personal behavior or relationships or interactions between people, and mental processes. For example, but for the “electronic processor”, “model”, and “neural network” language, the limitations describe a system for assigning images to radiologists based upon information about the images and radiologists, and updating the system based upon feedback. An analog example would be a manager assigning tasks to employees based upon their skill level, receiving information regarding the speed at which tasks due by a certain time were performed, and then updating their method for assigning tasks based on the speed at which the tasks were performed. The limitations describe the management of personal behavior, as well as actions that can be performed mentally or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Claims 1 and 18 feature limitations similar to those of claim 11, and are therefore also found to be directed to the same abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of an “electronic processor” to perform the steps. This additional element is recited at a high level of generality (see at least Para [0024]) such that it amounts to no more than mere instructions to apply the exception using generic computing components. Furthermore, the claims recite the additional elements of “wherein providing the state representation to the model includes inputting the state representation to a neural network and wherein the neural network outputs an action based on the state representation” and “adjust the neural network based on the reward”. However, the functions of inputting the state representation to a neural network, and adjusting the neural network appears to be based on very rudimentary constraints (e.g., image metadata, radiologist metadata, reward). Without some prohibition in the claims regarding scalability, computation load, etc., these additional limitations could reasonably be considered an additional abstract idea in the “mental process” category, but for which is simply automated (i.e., “apply it”).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), particularly as it relates to the recited “electronic processor”, “model”, and “neural network” elements. The claims are therefore still directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using an “electronic processor”, “model”, and “neural network” to perform the steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Claims 2-5, 7, and 9 are dependent on claim 1 and include all the limitations of claim 1. Claims 12-14, and 16 are dependent on claim 11 and include all the limitations of claim 11. Claims 19-20 are dependent on claim 18 and include all the limitations of claim 18. Therefore, they are also directed to the same abstract idea. The remaining dependent claims have not been found to integrate the judicial exception into a practical application, or provide significantly more than the abstract idea since they merely further narrow the abstract idea. Therefore, the dependent claims are found to be directed to an abstract idea without significantly more.
Examiner Notes
No prior art could be found that taught each limitation of the independent claims. In particular, no art could be found regarding determining a value of the reward “a specialty of the radiologist assigned to the medical image of the one or more medical images and a body part identifier for the medical image”. The closest prior art found was Jester (US 2015/0149193) which featured matching medical exam characteristics with profiles of radiologists that included specialties (Para. [0105]). However, Jester was silent in regards to determining a reward for a neural network. While the overall concept of calculating a reward in reinforcement learning is arguably a fundamental aspect of machine learning, the particular data used in the reward calculation was found to be novel.
Response to Arguments
Applicant's arguments regarding claims rejected under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that the human mind could not practically “adjust the neural network based on the reward”. This is not persuasive. As stated in the body of the 101 rejection above, the additional limitation of adjust the neural network based on the reward “appears to be based on very rudimentary constraints (e.g., image metadata, radiologist metadata, reward). Without some prohibition in the claims regarding scalability, computation load, etc., these additional limitations could reasonably be considered an additional abstract idea in the “mental process” category, but for which is simply automated (i.e., “apply it”).” (emphasis added)
Applicant argues that the application “describes problems with existing automated image study assignment technology (e.g., rule-based systems) and provides a technological solution to these problems through the use of online learning to perform reinforcement learning” (emphasis added). The Examiner respectfully disagrees. As stated in the previous Action, the assignment of images to radiologists is not a technological problem. Further, the claims do not provide an improvement to the concept of reinforcement learning. Other than the particular inputs used, the claims offer no discernable difference from the manner in which reinforcement learning was performed prior to filing. If anything, the claims merely utilize reinforcement learning (i.e., “apply it”) to the abstract concept of assigning images to radiologists (i.e., workflow management). This is further bolstered by the Applicant’s arguments that the claims “provide[s] a technological solution to these problems through the use of online learning to perform reinforcement learning”. Even if the claims feature a particular manner in which a reward used to train the model is calculated, the calculation of rewards to train a model is arguably a fundamental aspect of reinforcement learning, and the training/adjustment of a model is simply an adjustment to set of instructions, which is not an improvement to a computer or technical field.
The Examiner notes that the claims are analogous to Recentive Analytics, Inc. v. Fox Corp. in that the claims merely apply conventional machine learning to new data environments, without specific, non-generic improvements to the technology itself. The Applicant’s very own specification states “Accordingly, embodiments described herein provide methods and system for training and implementing a model for worklist assignment. The methods and systems can use online learning, wherein data obtained over a period of time is used to update the model for future data (e.g., as compared to batch learning techniques). In particular, embodiments described herein can use a reinforcement learning model. Reinforcement learning is an area of machine learning directed to learning an action to take in an environment to maximize a reward.” (Para. [0004]), and “Accordingly, embodiments described herein use online leaning models, such as reinforcement-based learning models, to automatically assign medical image studies to radiologists. For example, one embodiment provides a computer-implemented method of training a model using machine learning for automatically distributing medical imaging studies to radiologists.” (Para. [0008]) (emphasis added). In other words, the invention merely relies on the use of generic machine learning technology in carrying out the steps of assigning medical image studies to radiologists. The steps of training the reinforcement-based learning model do not represent a technological improvement as they merely describe the well-known routine functions of reinforcement learning (i.e., updating policy based on calculated rewards), but within a particular environment (i.e., medical image assignment).
There is nothing in the claims, whether considered individually or in their ordered combination, that would transform the invention into something “significantly more” than the abstract idea of distributing medical imaging studies to a plurality of radiologists through the application of machine learning.
Based on at least the above, the 101 rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Backhaus (US 2011/0010192) teaches the transmission of orders to appropriate radiologists (Para. [0059]).
Jester (US 2015/0149193) teaches assigning exams to radiologists based in part on exam attributes, radiologist availability/profile, and radiologist work queue (Fig. 10).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 KYLE G ROBINSON whose telephone number is (571)272-9261. The examiner can normally be reached Monday - Thursday, 7:00 - 4:30 EST; Friday 7:00-11:00 EST.
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/KYLE G ROBINSON/Examiner, Art Unit 3626
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685