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 .
This communication is in response to the amendment received on 10/09/2025. Claims 1-26 remain pending in this application.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-13 are drawn to a system which is within the four statutory categories (i.e. machine). Claims 14-26 are drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
Claim 1 has been amended to recite:
“receiving multi-parametric input data representing data associated with a patient;
receiving an indication of a disease associated with the patient;
applying a machine learning technique comprising a deep convolutional neural network (DCNN) to the multi-parametric input data to generate one or more metrics corresponding to a plurality of different modalities for treating the disease associated with the patient, the DCNN being trained based on training data to establish a relationship between a plurality of characteristics of pre-treatment planning associated with known patients associated with the disease, the modality of the plurality of different modalities used to treat the disease for each of the known patients, and a treatment result of each of the known patients, a first of the plurality of different modalities comprising radiotherapy treatment of the disease and a second of the plurality of modalities comprising an alternate treatment modality including at least one of a surgical modality, a chemotherapy modality, an immunotherapy modality, a hormone therapy modality, a stem cell transplant modality, or a precision medicine modality;
generating based on a result of applying the machine learning technique to the multiparametric input data, a first score for the first of the plurality of different modalities and a second score for the second of the plurality of different modalities comprising the alternate treatment modality;
ranking the plurality of different modalities based on the first score and the second score;
selecting, based on the one or more metrics and the ranking of the plurality of different modalities, a given modality from the plurality of different modalities to treat the disease associated with the patient;
configuring parameters of the given modality based on a portion of the multi-parametric input data, the configuring comprising:
obtaining a template for the given modality;
populating the template based on the portion of the multi- parametric input data;
transmitting the populated template for performing radiotherapy to a radiotherapy planning system; and
automatically generating a radiotherapy treatment plan based on the populated template;
receiving outcome information from a modality execution module associated with application of the given modality to treat the disease; and
updating the training data to generate new training data to update the DCNN based on the outcome information”,
claim 14 has been amended to recite:
“receiving a portion of training data corresponding to a given known patient of the known patients, the portion of the training data comprising characteristics of pre-treatment planning associated with the given known patient, a modality used to treat the disease for the given known patient, and a treatment result of the given known patient, a first of the plurality of different modalities comprising radiotherapy treatment of the disease and a second of the plurality of modalities comprising an alternate treatment modality including at least one of a surgical modality, a chemotherapy modality, an immunotherapy modality, a hormone therapy modality, a stem cell transplant modality, or a precision medicine modality;
applying the DCNN to the characteristics of pre-treatment planning associated with the given known patient to estimate a set of modalities for treating the disease of the given known patient, the machine learning technique being trained to establish a relationship between a plurality of characteristics of pre-treatment planning associated with known patients associated with the disease, the modality of the plurality of different modalities used to treat the disease for each of the known patients, and a treatment result of each of the known patients;
comparing the modality used to treat the disease for the given known patient with the estimated set of modalities for treating the disease of the given known patient;
computing a loss function based on a deviation parameter and a treatment result parameter, wherein the deviation parameter is determined based on a result of comparing the modality used to treat the disease for the given known patient with the estimated set of modalities, and wherein the treatment result parameter is determined based on the treatment result of the given known patient; and
updating one or more parameters of the machine learning technique based on the computed loss, the machine learning technique being used to ranking the plurality of different modalities based on first and second scores associated with respective modalities, outcome information being retrieved from a modality execution module associated with application of the given modality to treat the disease, and the training data being updated to generate new training data to update the DCNN based on the outcome information” and
claim 20 has been amended to recite:
“receiving multi-parametric input data representing data associated with a patient;
receiving an indication of a disease associated with the patient;
applying a machine learning technique to the multi-parametric input data to generate one or more metrics corresponding to a plurality of different modalities for treating the disease associated with the patient, the machine learning technique being trained based on training data to establish a relationship between a plurality of characteristics of pre-treatment planning associated with known patients associated with the disease, the modality of the plurality of different modalities used to treat the disease for each of the known patients, and a treatment result of each of the known patients, a first of the plurality of different modalities comprising radiotherapy treatment of the disease and a second of the plurality of modalities comprising an alternate treatment modality including at least one of a surgical modality, a chemotherapy modality, an immunotherapy modality, a hormone therapy modality, a stem cell transplant modality, or a precision medicine modality;
generating based on a result of applying the machine learning technique to the multiparametric input data, a first score for the first of the plurality of different modalities and a second score for the second of the plurality of different modalities comprising the alternate treatment modality;
ranking the plurality of different modalities based on the first score and the second score;
selecting, based on the one or more metrics and the ranking of the plurality of different modalities, a given modality from the plurality of different modalities to treat the disease associated with the patient;
configuring parameters of the given modality based on a portion of the multi-parametric input data, the configuring comprising:
obtaining a template for the given modality; and
populating the template based on the portion of the multi- parametric input data;
receiving outcome information from a modality execution module associated with application of the given modality to treat the disease; and
updating the training data to generate new training data to update the DCNN based on the outcome information”.
The limitations of “receiving multi-parametric input data representing data associated with a patient; receiving an indication of a disease associated with the patient;… generating…a first score…and a second score…; ranking the plurality of different modalities based on the first score and the second score; selecting, based on the one or more metrics and the ranking of the plurality of different modalities, a given modality from the plurality of different modalities to treat the disease associated with the patient” correspond to “certain methods of organizing human activity”. This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic processor and generic memory devices does not take the claims out of the methods of organizing human interactions grouping.
The limitations of “applying a machine learning technique comprising a deep convolutional neural network (DCNN) to the multi-parametric input data to generate one or more metrics corresponding to a plurality of different modalities for treating the disease associated with the patient, the DCNN being trained based on training data to establish a relationship between a plurality of characteristics of pre-treatment planning associated with known patients associated with the disease, the modality of the plurality of different modalities used to treat the disease for each of the known patients, and a treatment result of each of the known patients;…” corresponds to mathematical relationships and performing mathematical calculations, which falls within the “mathematical concepts” grouping of abstract ideas.
Accordingly, claims recite an abstract idea.
Dependent claims also correspond to mathematical relationships/calculations, such as claim 7 recites “computing a quality score associated with a medical facility based on the given modality selected to treat the disease associated with the patient and a treatment result associated with treating the patient with the given modality”, claim 8 recites “receiving a portion of training data corresponding to a given known patient of the known patients, the portion of the training data comprising characteristics of pre-treatment planning associated with the given known patient, the modality used to treat the disease for the given known patient, and the treatment result of the given known patient; applying the machine learning technique to the characteristics of pre- treatment planning associated with the given known patient to estimate a set of modalities for treating the disease of the given known patient; comparing the modality used to treat the disease for the given known patient with the estimated set of modalities for treating the disease of the given known patient; computing a loss function based on a deviation parameter and a treatment result parameter, wherein the deviation parameter is determined based on a result of comparing the modality used to treat the disease for the given known patient with the estimated set of modalities, and wherein the treatment result parameter is determined based on the treatment result of the given known patient; and updating one or more parameters of the machine learning technique based on the computed loss”, claim 9 recites “the machine learning technique is configured to generate a weight for each of the set of modalities for treating the disease of the given known patient, the weight being generated based on the computed loss function”, claim 10 recites “training the machine learning technique to generate a set of modalities for treating a second disease based on additional training data comprising a plurality of characteristics of pre-treatment planning associated with known patients associated with the second disease, a modality of a plurality of different modalities used to treat the second disease for each of the known patients, and a treatment result of each of the known patients associated with the second disease”, claim 11 recites “ranking the plurality of different modalities for treating the disease associated with the patient based on the one or more metrics; determining that the given modality selected to treat the disease associated with the patient is associated with a lower rank than a second modality of the plurality of different modalities; and comparing a treatment result associated with treating the patient with the given modality with an estimated result of the second modality”, claim 16 recites “training one or more sub- networks of the DCNN separately and independently in sequence by minimizing a set of cost functions associated with each particular sub-network, wherein a first of the one or more sub-networks is configured to estimate a first set of modalities for treating a first disease, and a second of the one or more sub- networks is configured to estimate a second set of modalities for treating a second disease”, claim 17 recites “the loss function further includes a reimbursement parameter specifying a level of reimbursement of each of the modalities for treating the disease and a government or professional body regulations parameter specifying a modality for treating the disease given a set of characteristics, further comprising: repeating the applying, comparing, computing and updating operations for another portion of training data in response to determining that a stopping criterion has not been satisfied, the stopping criterion comprising a difference between the modality used to treat the disease for the given known patient and the estimated set of modalities falling below a threshold”, claim 18 recites “training one or more sub- networks of the DCNN simultaneously based on a same batch of training data by minimizing a set of cost functions associated with each particular sub-network, wherein a first of the one or more sub-networks is configured to estimate a first set of modalities for treating a first disease, and a second of the one or more sub- networks is configured to estimate a second set of modalities for treating a second disease”, claim 19 recites “training the machine learning technique to generate a set of modalities for treating a second disease based on additional training data comprising a plurality of characteristics of pre-treatment planning associated with known patients associated with the second disease, a modality of a plurality of different modalities used to treat the second disease for each of the known patients, and a treatment result of each of the known patients associated with the second disease, wherein the plurality of characteristics of pre-treatment planning comprises medical images of the known patients including images of an anatomy, CT images, PET images or MRI images and wherein the treatment result comprises survival or toxicity information”, and claim 26 recites “training the machine learning technique by performing a series of training steps comprising: receiving a portion of training data corresponding to a given known patient of the known patients, the portion of the training data comprising characteristics of pre-treatment planning associated with the given known patient, the modality used to treat the disease for the given known patient, and the treatment result of the given known patient; applying the machine learning technique to the characteristics of pre- treatment planning associated with the given known patient to estimate a set of modalities for treating the disease of the given known patient; comparing the modality used to treat the disease for the given known patient with the estimated set of modalities for treating the disease of the given known patient; computing a loss function based on a deviation parameter and a treatment result parameter, wherein the deviation parameter is determined based on a result of comparing the modality used to treat the disease for the given known patient with the estimated set of modalities, and wherein the treatment result parameter is determined based on the treatment result of the given known patient; and updating one or more parameters of the machine learning technique based on the computed loss”.
These limitations correspond to mathematical relationships and performing mathematical calculations, which falls within the “mathematical concept” grouping of abstract ideas.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Claims 2-6, 12-13, 15, 21-25 are ultimately dependent from claims 1, 14, 20 and include all the limitations of claim 1. Therefore, claims 2-6, 12-13, 15, 21-25 recite the same abstract idea. Claims 2-6, 12-13, 15, 21-25 describe a further limitation regarding the basis for generating a radiotherapy treatment plan. These are all just further describing the abstract idea recited in claims 1, 14, 20, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of “a memory”, “one or more processors”, using one or more processors to perform receiving data, applying a machine learning technique to establish a relationships between a plurality of characteristics of pre-treatment planning associated with known patients associated with disease, “obtaining a template for the given modality; populating the template based on the portion of the multi- parametric input data; transmitting the populated template for performing radiotherapy to a radiotherapy planning system; and automatically generating a radiotherapy treatment plan based on the populated template; receiving outcome information from a modality execution module associated with application of the given modality to treat the disease; and updating the training data to generate new training data to update the DCNN based on the outcome information” which are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Accordingly, these 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. The claims are directed to an abstract idea.
Step 2B:
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 element of using a processor to perform receiving data, selecting modalities, populating template and automatically generating a disease treatment plan steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 10/09/2025 have been fully considered. Applicant’s arguments will be addressed below in the order in which they appear.
Arguments about 35 USC 101 rejection:
Applicant argues that claims recite "generating, based on a result of applying the machine learning technique to the multi-parametric input data, a first score for the first of the plurality of different modalities and a second score for the second of the plurality of different modalities comprising the alternate treatment modality; ranking the plurality of different modalities based on the first score and the second score; selecting, based on the one or more metrics and the ranking of the plurality of different modalities, a given modality from the plurality of different modalities to treat the disease associated with the patient; ... receiving outcome information from a modality execution module associated with application of the given modality to treat the disease; and updating the training data to generate new training data to update the DCNN based on the outcome information" ad these limitations are not directed to an abstract idea of certain methods of organizing human activity or mathematical concepts.
In response, Examiner submits that the limitations of “receiving multi-parametric input data representing data associated with a patient; receiving an indication of a disease associated with the patient;… generating…a first score…and a second score…; ranking the plurality of different modalities based on the first score and the second score; selecting, based on the one or more metrics and the ranking of the plurality of different modalities, a given modality from the plurality of different modalities to treat the disease associated with the patient” correspond to “certain methods of organizing human activity”. This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic processor and generic memory devices does not take the claims out of the methods of organizing human interactions grouping. MPEP recites “…the sub-groupings encompass both activity of a single person…and an activity involves multiple people…and thus, certain activity between a person and a computer…may fall within the “certain methods of organizing human activity” grouping” (MPEP 2106.04(a)(2) II).
The limitations of “applying a machine learning technique comprising a deep convolutional neural network (DCNN) to the multi-parametric input data to generate one or more metrics corresponding to a plurality of different modalities for treating the disease associated with the patient, the DCNN being trained based on training data to establish a relationship between a plurality of characteristics of pre-treatment planning associated with known patients associated with the disease, the modality of the plurality of different modalities used to treat the disease for each of the known patients, and a treatment result of each of the known patients;…” corresponds to mathematical relationships and performing mathematical calculations, which falls within the “mathematical concepts” grouping of abstract ideas. The CNN architecture is usually trained through backpropagation, the current specification also mentions “The errors or result of computing the loss function can be used during a procedure called backpropagation to update the parameters of the deep learning network…, in order to reduce or minimize errors…” in [0094]. In particular, the backpropagation algorithms are mathematical calculations, therefore these features are directed to “mathematical concepts”.
Applicant argues that the claims limitations are directed to improving treatment selection consistency through automated multi-parametric analysis and standardized template-based configuration, and the claims provide concrete technological improvements that transform conventional medical treatment planning systems and provide a practical application.
In response, Examiner submits that the claim limitations may provide improved outcomes (disease treatments), however, they are not directed to any improvement to the technology, since using a processor to perform receiving data, selecting modalities, populating template and automatically generating a disease treatment plan steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Arguments about 35 USC 102 rejection:
Applicant’s arguments, see Remarks, filed 10/09/2025, with respect to 35 USC 102 rejection of the claims have been fully considered and are persuasive. The 35 USC 102 rejection of claims 1-26 has been withdrawn.
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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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687
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