DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/15/25 has been entered.
Status of Claims
Claims 1, 11 and 20 have been amended.
No claims have been canceled or added.
Claims 1-20 are currently pending and have been fully examined.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claim 1), a machine (claim 11), and an article of manufacture (claim 20) which is recited as methods, systems, and non-transitory computer readable media that performs the steps and/or functions of:
acquiring pre-treatment features of one or more target lesions associated with a pre-treatment scan of a target lesion prior to treating the target lesion according to a treatment plan;
determining a set of features indicative of a change in the one or more target lesions using the pre-treatment features;
providing the set of features to one or more predictive models trained to predict, based on features of target lesions, therapeutic agent responses for a Programmed Death-1 (PD-1) agent as a monotherapy and a PD-1 agent in combination with chemotherapy; and
generating, by a processing device, a predicted treatment response score for the treatment plan based on the set of features and the one or more predictive models prior to treating the target lesion according to the treatment plan, wherein the treatment plan recommends treating the target lesion with either the PD-1 agent as the monotherapy or the PD-1 agent in combination with the chemotherapy,
wherein the predicted treatment response score is further based on an assessment of variation in lesion-specific features across a plurality of lesions within the same patient; and
treating the target lesion according to the treatment plan
Step 2A: Prong 1
When taken individually and as a whole, the steps corresponds to concepts identified as abstract ideas by the courts, such as “certain methods of organizing human activity”, which are interactions between individuals that can include: fundamental economic principles or practices; commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
The claim is directed to a system to perform the process of determining a treatment plan for a patient, which is performed by the system determining a set of features indicative of a change in the one or more target lesions, predicting therapeutic agent responses, and generating a predicted treatment response score for the treatment plan based on the set of features, wherein the predicted treatment response score is based on assessment of variation in lesion-specific features across a plurality of lesions within the same patient. This is certain methods of organizing human activity because it is managing the behavior of the patient and the provider by providing rules or instructions regarding which treatment plans would be best-suited to treat the patient.
Step 2A: Prong 2
The claims do not include additional elements that are sufficient to be considered a practical application because the additional elements amount to: insignificant extra-solution activity (MPEP 2106.05(g)), generally linking the application of the abstract idea to a particular field of use or technological environment (2106.05(h)), or mere instructions to apply it with a computer (MPEP 2106.05(f)), as discussed below.
Insignificant Extra-Solution Activity
The steps of: acquiring pre-treatment features of one or more target lesions associated with a pre-treatment scan of a target subject prior to treating the target subject according to a treatment plan is an example of mere data gathering, which is an insignificant extra-solution activity (MPEP 2106.5(g)).
The steps specifying the data to be pre-treatment features, features of one or more target lesions associated with a pre-treatment scan, the data being of a target subject prior to treating the target subject according to a treatment plan, and describing the therapeutic agent responses as being for “Programmed Death-1 (PD-1) agent as a monotherapy and PD-1 agent in combination with chemotherapy” are examples of selecting by type or source the data to be manipulated, which is an extra-solution activity (MPEP 2106.05(g)).
Insignificant extra-solution activities are not sufficient to integrate the abstract idea into a practical application or cause the claim to amount to significantly more than the abstract idea (MPEP 2106.05(g))
Mere Instructions to Apply the Abstract Idea
The steps reciting the use of computer components, such as providing the set of features to one or more predictive models trained to predict therapeutic agent responses based on features of target lesions and generating, by a processing device, a predicted treatment response score based on the set of features and the one or more predictive models prior to treating the target subject, serve as mere instructions to apply the abstract idea using a computer. Providing features to a predictive model and generating a score based on that model are simply instructions to apply the analysis of the abstract idea using a computer running a generically recited predictive model. Mere instructions to apply the abstract idea using a computer are not sufficient to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (MPEP 2106.05(f)).
The limitation reciting “treating the target lesion according to the treatment plan” is considered mere instructions to apply the abstract idea and not a particular treatment or prophylaxis because it is not particular and is instead merely instructions to “apply” the exception in a generic way. Per MPEP 2106.04(d)(2), several factors are relevant when determining whether a claim applies or uses a recited judicial exception to effect a particular treatment or prophylaxis for a disease or medical conditions. In a claim that recites an abstract idea and “administering a suitable medication to a patient”, the administration step is not particular, and is merely instructions to “apply” the exception in a generic way.
Step 2B
The claims also do not include additional elements that are sufficient to be considered a significantly more than the abstract idea because the additional elements amount to: insignificant extra-solution activity (MPEP 2106.05(g)), mere instructions to apply it with a computer (MPEP 2106.05(f)), generally linking the application of the abstract idea to a particular field of use or technological environment (MPEP 2106.05(h)), or a well-understood, routine, and conventional limitation (MPEP 2106.05(d)), as discussed below.
The steps addressed above in Step 2A: Prong 2, when considered again under Step 2B are not considered to make the claims amount to significantly more than the abstract idea because those steps, when considered additionally with regards to Step 2B, are still considered to be either insignificant extra-solution activity, mere instructions to apply an abstract idea with a computer, or generally linking the application of the abstract idea to a particular field of use or technological environment, which are types of limitations that are not sufficient to make the claims amount to significantly more than the abstract idea (MPEP 2106.05.I.A).
The steps recited as either being part of the abstract idea or insignificant extra-solution activity are all examples of at least one of: storing and retrieving data from a memory (determining the features and providing the features to the predictive models), sending and receiving data over a network (acquiring the pre-treatment features if the features are acquired from an external source), electronic recordkeeping, or performing repetitive calculations. All of those functions have been identified as well-understood, routine, and conventional functions of a generic computer that are not significantly more than the abstract idea when claimed broadly or as an extra-solution activity (MPEP 2106.05(d).II).
The recited computer components (e.g., the memory and processing device) are all generically recited components (see specification, par. [0021]). Commercially available components, generic computer components, and specially-programmed computer components performing the functions of a generic computer are not considered to be amount to significantly more than the abstract idea (MPEP 2106.05(b)).
When considered as a whole, the components do not provide anything that is not present when the component parts are considered individually. Using the broadest reasonable interpretation, the system as a whole is a system of generic computer components receiving data, analyzing that data, and generating a predicted effect of a treatment plan for a patient. This is a system of general purpose computer components performing the abstract idea and insignificant extra-solution activities through these generically described devices performing well-understood, routine, and conventional functions of a generic computer (MPEP 2106.05(d).II).
Dependent Claim Analysis
Claims 2-10 are ultimately dependent from Claim(s) 1 and includes all the limitations of Claim(s) 1. Therefore, claim(s) 2-10 recite the same abstract idea of certain methods of organizing human activity of claim 1.
Claims 2 and 9 recite additional limitations that amount to mere instructions to apply the abstract idea using a computer (MPEP 2106.05(f)). Mere instructions to apply the abstract idea using a computer is not sufficient to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claims 3-6 and 10 recite additional limitations that serve to select by type or source the data to be manipulated. Selecting by type or source the data to be manipulated is an insignificant extra-solution activity that is not sufficient to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (MPEP 2106.05(g)).
Claims 7 and 8 recite additional limitations that amount to performing calculations, which would be considered abstract ideas falling within the enumerated grouping of “mathematical concepts” (e.g., mathematical formulas, equations, calculations, and mathematical relationships). Additional limitations that recite an abstract idea are not considered sufficient to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (MPEP 2106.04.I.A.2). Further, these steps are recited as the system performing a series of repetitive calculations (performing repetitive calculations is a well-understood, routine, and conventional function of a generic computer (MPEP 2106.05(d).II).
Claims 12-19 are ultimately dependent from Claim(s) 11 and includes all the limitations of Claim(s) 11. Therefore, claim(s) 12-19 recite the same abstract idea of certain methods of organizing human activity of claim 11.
Claims 12-19 all recite limitations that are the same or substantially similar to the limitations of claims 2-10, respectively (wherein claim 17 recites limitations of both claim 7 and claim 8). Therefore, claims 12-19 are rejected under 101 for the same reasons as claims 2-10.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jordan (US PG Pub. 2022/0028551) in view of Jiang (US PG Pub. 2022/0015698).
Note to Applicant
Though the cited reference is owned by the same assignee and has inventors in common with the present application, the publication date of the Jordan reference, January 27, 2022, is more than one year prior to the effective filing date of the present application, which is the filing date of the provisional application 63/465,460 on May 10, 2023. Therefore, it qualifies as prior art under 35 USC 102(a)(1), and is not subject to any of the exceptions under 102(b)(2).
Further, because the filing date of the non-provisional application of the present application, May 7, 2024, is after the issuance date of the patent issued for application 17/383,649 (i.e., the Jordan reference), January 31, 2023, the two applications were never co-pending, so the present application is not able to be reclassified as a continuation or continuation-in-part of application 17/383,649 (MPEP 201.07, MPEP 201.08).
Therefore, the Jordan reference qualifies as prior art under 102, is not subject to any exceptions to prior art under 102, and the present application cannot be afforded the priority date of the Jordan reference as a continuation or continuation-in-part.
Claim 1
Regarding claim 1, Jordan discloses
A method, comprising:
Abstract, “A method comprises providing a pre-treatment image of a target subject to at least one deep learning model uniquely trained to predict immunotherapy treatment responses.”
Acquiring pre-treatment features of one or more target lesions associated with a pre-treatment scan of a target subject prior to treating the target subject according to a treatment plan and determining a set of features indicative of a change in the one or more target lesions using the pre-treatment features
Par. [0057], “FIG. 3B is an illustration of an example of a follow-up image 350 of a target, in accordance with embodiments of the disclosure. As previously described, embodiments of the disclosure may utilize one or more follow-up images, such as follow-up image 350, of the target that were captured after treatment. The follow-up image 350 includes lung lesion 352, which may correspond to lung lesion 302 after receiving treatment. In embodiments, the follow-up image 350 may be provided to the machine learning architecture 127 and may be used to determine whether the current treatment plan is effective and should continue, whether there is a more effective treatment option, and/or whether the treatment should be discontinued based on an analysis of the follow-up image 350 relative to pre-treatment image 300. In embodiments, the follow-up image 350 may correspond to a CT image. In some embodiments, the follow-up image 350 may correspond to a PET image. In an embodiment, the follow-up image 350 may correspond to an MM image. In some embodiments, other types of follow-up images may be used.”
In this example, there are a plurality of scans throughout the patient’s care and the efficacy of the treatment plan is evaluated at each of the times the model is run. Therefore, a scan that was taken for the intent of determining whether a new treatment plan should be considered would be a pre-treatment scan, and an analysis of the change in features between the scan before the initial treatment and the follow-up treatment would be a change in pre-treatment values.
Providing the set of features to one or more predictive models trained to predict, based on features of target lesions, therapeutic agent responses for a Programmed Death-1 (PD-1) agent as a monotherapy and a PD-1 agent in combination with chemotherapy
Par. [0026], “To predict treatment response of a single lesion, a model is trained using multiparametric optimization techniques, such as stochastic gradient descent (SGD), RMSprop, or adaptive momentum (Adam) algorithms, to maximize the agreement between model-predicted lesion response and lesion response determined by human expert (e.g. radiologist).”
Par. [0033], “At block 203, processing logic generates (e.g., by a processing device) a predicted treatment response score (e.g., on a scale representing least likely to have a positive of negative effect to most likely to have a positive or negative effect) to an immunotherapy treatment based on the deep learning models. In some embodiments, the predicted treatment response score may be a numerical value. In one embodiment, processing logic generates the predicted treatment response score based on the single pre-treatment image and the at least one deep learning model. For example, in one embodiment, results from the different models may be combined (e.g., averaged, or combined in any other way) to generate a single response score. In one embodiment, one or more non-imaging features (e.g., genomic tests, electronic medical record information, PD-L1 immunohistochemistry assays, etc.) may be used to generate the predicted response score. In another embodiment, the one or more non-imaging features may be combined with one or more imaging features to generate the predicted response score.”
Par. [0036-0038], “The processing logic may receive an intra-treatment follow-up image… The processing logic may provide the intra-treatment follow-up image to the machine learning model…. The processing logic may generate an updated predicted treatment response score
Par. [0020], “In one embodiment, system 100 includes server 101, network 106, and client device 150. Server 100 may include various components, which may allow for predicting responses to PD-1 checkpoint blockades (and other immunotherapy treatments) using deep learning analysis and of imaging and clinical data on a server device or client device.”
This shows the system’s ability to predict the response to a PD-1 agent as monotherapy.
Par. [0035], “In another embodiment, the recommended treatment plan for a patient with a model-predicted high risk of progression may be to add chemotherapy or CTLA-4 immunotherapy in combination with PD-[L]1 immunotherapy to maximize treatment response likelihood.”
This shows the system’s ability to predict PD-1 agent in combination with chemotherapy.
Generating, by a processing device, a predicted treatment response score for the treatment plan based on the set of features and the one or more predictive models prior to treating the target subject according to the treatment plan, wherein the predicted treatment response score is further based on… a plurality of lesions within the same patient
Par. [0027], “Examples of lesion response may include, numerical assessment (e.g. change in lesion volume, change in one or more primary dimensions of the lesion, change in image intensity within the lesions), tumor growth rate (TGR), or categorical assessment (e.g. responding lesion, stable lesion, progressing lesion, new lesion). Predicting treatment response at patient level is performed by aggregating one or more lesion-level model predictions. In one embodiment, aggregation from lesion to patient level response prediction is performed by a set of rules and logical operations.”
Par. [0028], “In embodiments, a per-lesion response score may be calculated for multiple lesions in a single patient, followed by a mathematical operation, such as maximum score, minimum score, and/or mean score to transform the multiple per-lesion response predictions into a single, patient-level response prediction. In an embodiment, aggregation from lesion to patient level response prediction is performed by a second model, which takes predictions from one or more lesion-level models as an input and is trained specifically to perform patient-level response prediction. In some embodiments, to account for variable numbers of lesions (e.g., the model inputs), the inputs into the model may be the lesion-level prediction statistics (e.g., mean, median, standard deviation, etc.). In another embodiment, the model may be a recurrent neural network (RNN) model in which multiple lesion predictions are represented as an input sequence of variable length.”
Par. [0035], “At block 205, processing logic provides, based on the predicted treatment response, a recommended treatment plan. For example, based on the predicted treatment response, a recommended treatment plan may include an indication of whether a specific pharmaceutical product should be used, a dosage of such product, a timing associated with administering such a product, etc. In embodiments, the indication may identify whether or not a patient is likely to respond to the specific pharmaceutical product. In one embodiment, the per-lesion immunotherapy and/or chemotherapy response predictions are used to generate a lesion-specific therapy plan to enhance the therapeutic effect in high-risk lesions by combining ongoing systemic therapy with localized therapy.”
Wherein the treatment plan recommends treating the target lesion with either the PD-1 agent as the monotherapy or the PD-1 agent in combination with the chemotherapy
Par. [0020], “In one embodiment, system 100 includes server 101, network 106, and client device 150. Server 100 may include various components, which may allow for predicting responses to PD-1 checkpoint blockades (and other immunotherapy treatments) using deep learning analysis and of imaging and clinical data on a server device or client device.”
This shows the system’s ability to predict the response to a PD-1 agent as monotherapy.
Par. [0035], “In another embodiment, the recommended treatment plan for a patient with a model-predicted high risk of progression may be to add chemotherapy or CTLA-4 immunotherapy in combination with PD-[L]1 immunotherapy to maximize treatment response likelihood.”
This shows the system’s ability to predict PD-1 agent in combination with chemotherapy.
However, Jordan does not explicitly teach
Acquiring pre-treatment features of one or more target lesions associated with a pre-treatment scan of a target subject prior to treating the target subject according to a treatment plan and determining a set of features indicative of a change in the one or more target lesions using the pre-treatment features
Wherein the predicted treatment response score is further based on an assessment of variation in lesion-specific features across a plurality of lesions within the same patient;
Treating the target lesion according to the treatment plan
Jiang teaches
Acquiring pre-treatment features of one or more target lesions associated with a pre-treatment scan of a target lesion prior to treating the target lesion according to a treatment plan and determining a set of features indicative of a change in the one or more target lesions using the pre-treatment features
Par. [0144], “FIG. 16 illustrates a block diagram of a further example method for precision cancer treatment by identifying drug resistance (“the method”) 1600 according to various embodiments described herein. The method 1600 may start 1601 and a first measurement of oxygenated perfusion percentage (OPP %) data and volume change ratio (Vt %) data as baseline of a tumor of a patient before administering the assuming first therapy modality C to the patient may be determined in step 1602.”
Par. [0072], “As used herein, the term “volume change ratio” or “Vt %” refers to the varication of tumor volume comparing with the tumor volume of before first treatment (reference volume), the negative value means tumor shrinkage, the positive value means tumor volume increase.”
Par. [0103]-[0104] describes volume change ratio as being calculated as the percent change in the volume of a lesion captured during imaging at two different times.
It would have been obvious to one having ordinary skill in the art before the effective filing date of this application to add to the system of Jordan the ability to capture a set of features indicative of a target lesion prior to treating the target lesion, as taught by Jiang, because “The Vt % parameter directly correlates to cancer response to previous treatment.” (Jiang, par. [0125]).
Jiang further teaches
Wherein the predicted treatment response score is further based on an assessment of variation in lesion-specific features across a plurality of lesions within the same patient;
Par. [0011], Two parameters of the previous response (tumor volume change) and future possible response (tumor drug distribution) are very important therapeutic information for evaluating tumor response to treatment. Changes in tumor volume are used to assess treatment outcomes objectively…. The tumor volume change and ability of drug distribution can be integrated a tumor response point in a two - dimensional coordinate system that represents previous treatment outcomes and possible future treatment outcomes. Visualization of multiple tumor response points (two-dimensional tumor response information) can be used to monitor treatment progress and identify different treatment resistances, thereby optimizing In treatment strategies and reducing ineffective treatment.
Par. [0019], calculating tumor region of interest (ROI) volume (V) based on intensity threshold of the dynamic contrast enhanced T2 - weighted MR imaging data with the processor; computing a tumor volume change ratio (Vt %) based on a reference volume Ve of the particular solid tumor with the processor
Treating the target lesion according to the treatment plan
Par. [0111], at least two consecutive measurements are needed to confirm the type of resistance (to treatment). In this case, the clinicians can continue the systemic treatment but must immediately change the treatment drugs/agents.
Par. [0148-0149], “the patient may continue to be treated with the cancer therapeutic modality C so that their therapy is unchanged…. The patient may continue to be treated with the cancer therapeutic modality C while having the dosage and/or frequency of administration changed, such as by being increased or decreased”.
It would have been obvious to one having ordinary skill in the art before the effective filing date of this application to add to the system of Jordan the ability to consider the variation in lesion-specific features across a plurality of lesions within the same patient, and treat the target lesion according to the treatment plan, as taught by Jiang, because doing so evaluates tumor response to prior treatment using an agent or drug, and whether or not resistance merits changing the treatment drugs/agents being administered to the patient, where timely monitoring and identifying the type of drug resistance of tumor will greatly benefit to developing or adjusting the optimal treatment plan during treatment course, and reducing ineffective treatment (Jiang, par. [0003-0004, 0111]).
Claim 2
Regarding claim 2, the combination of Jordan and Jiang teaches all the limitations of claim 1. Jordan further teaches
The one or more predictive models being trained using training data comprising a plurality of imaging and non-imaging features associated with a plurality of target lesions of a plurality of target subjects
Par. [0031], “The deep learning models may utilize a variety of suitable training methods. For example, in one embodiment, the deep learning models use a population of training subjects and a plurality of images associated with each of a plurality of training subjects as training data. In another embodiment, the deep learning models use calculated subject-specific models as training data. In yet another embodiment, the deep learning models use a combination of the two methods described above.”
Par. [0033], “For example, in one embodiment, results from the different models may be combined (e.g., averaged, or combined in any other way) to generate a single response score. In one embodiment, one or more non-imaging features (e.g., genomic tests, electronic medical record information, PD-L1 immunohistochemistry assays, etc.) may be used to generate the predicted response score. In another embodiment, the one or more non-imaging features may be combined with one or more imaging features to generate the predicted response score.”
Claim 3
Regarding claim 3, the combination of Jordan and Jiang teaches all the limitations of claim 1. Jordan further teaches
Wherein generating the predicted treatment response score is further based on pre-treatment information indicative of at least one of: a change in blood lab values, or a change in urine lab values, or a change in imaging features
Par. [0015], “In one embodiment, the classifier is developed from training data that includes diagnostic imaging scans at baseline and follow-up intervals, along with existing biomarkers, relevant clinical, molecular, demographic, response and survival data. Examples of existing biomarkers used in clinical practice include: PD-L1 expression immunohistochemistry, tumor mutation burden (TMB), mutation mismatch repair (MMR), microsatellite instability (MSI), and neutrophil-to-lympocyte ratio (NLR). Furthermore, there is early evidence suggesting that laboratory tests, such as Lactate Dehydrogenase (LDH), S100 proteins and related blood serum proteins are predictive of immunotherapy response and pseudoprogression, specifically. In the near future, features and biomarkers extracted from the microbiome are expected to play a significant role as well.”
Par. [0033], “For example, in one embodiment, results from the different models may be combined (e.g., averaged, or combined in any other way) to generate a single response score. In one embodiment, one or more non-imaging features (e.g., genomic tests, electronic medical record information, PD-L1 immunohistochemistry assays, etc.) may be used to generate the predicted response score. In another embodiment, the one or more non-imaging features may be combined with one or more imaging features to generate the predicted response score.”
Claim 4
Regarding claim 4, the combination of Jordan and Jiang teaches all the limitations of claim 1. Jordan further teaches
The one or more predictive models are further trained to predict the therapeutic agent responses based on a change in lesion volume
Par. [0027], “Examples of lesion response may include, numerical assessment (e.g. change in lesion volume, change in one or more primary dimensions of the lesion, change in image intensity within the lesions), tumor growth rate (TGR), or categorical assessment (e.g. responding lesion, stable lesion, progressing lesion, new lesion). Predicting treatment response at patient level is performed by aggregating one or more lesion-level model predictions. In one embodiment, aggregation from lesion to patient level response prediction is performed by a set of rules and logical operations.”
Claim 5
Regarding claim 5, the combination of Jordan and Jiang teaches all the limitations of claim 1. Jordan further teaches
Improving a prediction accuracy of the one or more predictive models by training the one or more predictive models with a plurality of pre-treatment scans associated with a plurality of target subjects
Par. [0031], “The deep learning models may utilize a variety of suitable training methods. For example, in one embodiment, the deep learning models use a population of training subjects and a plurality of images associated with each of a plurality of training subjects as training data. In another embodiment, the deep learning models use calculated subject-specific models as training data. In yet another embodiment, the deep learning models use a combination of the two methods described above.”
Claim 6
Regarding claim 6, the combination of Jordan and Jiang teaches all the limitations of claim 5. Jordan further teaches
Wherein acquiring the pre-treatment features of the one or more target lesions associated with the pre-treatment scan of the target subject further comprises:
Acquiring baseline features of one or more target lesions associated with a baseline scan of the target subject prior to administering the treatment plan to the target subject
Par. [0036]-[0038], “At block 207, the processing logic may receive an intra-treatment follow-up image. At block 209, the processing logic may provide the intra-treatment follow-up image to the machine learning model. At block 211, the processing logic may generate an updated predicted treatment response score.”
See Fig. 4, which shows that the system has the ability to process patient data at a plurality of time points and change the treatment plan according to those changes between the time points. In this specific example, one treatment plan was tried, then an intra-treatment image was taken. Based on that intra-treatment image, a new treatment plan was generated, and the intra-treatment image was a scan of the target subject prior to administering the updated treatment plan to the target subject.
Acquiring pre-baseline features of one or more corresponding target lesions associated with a pre-baseline scan of the target subject prior to administering the treatment plan to the target subject
Par. [0034], “In one embodiment, the predicted treatment response score includes a prediction of patient progression on a predefined pharmaceutical product. In another embodiment, the predicted treatment response score indicates a prediction of one or more immune-related adverse events associated with the immunotherapy treatment. In one embodiment, the predicted treatment response score may include a predicted likelihood (e.g., a confidence level) of a specific type of response and/or adverse event occurring. In another embodiment, the response score may also include an indication of pseudo-progression, which is characterized by short-term and temporary increase in tumor volume due to natural swelling and/or inflammation (e.g., in response to treatment), rather than progression of disease.”
Also referring to fig. 4, the initial patient data, prediction, and patient scans would be the pre-baseline features for the patient, and the intra-treatment follow-up data would be the baseline features.
Claim 7
Regarding claim 7, the combination of Jordan and Jiang teaches all the limitations of claim 6. Jordan further teaches
Calculating a difference between the pre-baseline features and the baseline features
Par. [0027], “Examples of lesion response may include, numerical assessment (e.g. change in lesion volume, change in one or more primary dimensions of the lesion, change in image intensity within the lesions), tumor growth rate (TGR), or categorical assessment (e.g. responding lesion, stable lesion, progressing lesion, new lesion). Predicting treatment response at patient level is performed by aggregating one or more lesion-level model predictions. In one embodiment, aggregation from lesion to patient level response prediction is performed by a set of rules and logical operations.”
Claim 8
Regarding claim 8, the combination of Jordan and Jiang teaches all the limitations of claim 7. Jordan further teaches
Normalizing the difference to produce a normalized difference by dividing the difference in imaging and non-imaging features by a reference time period between the baseline scan and the pre-baseline scan
Par. [0027], “Examples of lesion response may include, numerical assessment (e.g. change in lesion volume, change in one or more primary dimensions of the lesion, change in image intensity within the lesions), tumor growth rate (TGR), or categorical assessment (e.g. responding lesion, stable lesion, progressing lesion, new lesion). Predicting treatment response at patient level is performed by aggregating one or more lesion-level model predictions. In one embodiment, aggregation from lesion to patient level response prediction is performed by a set of rules and logical operations.”
A rate is an amount of change over time. So tumor growth rate would be an measurement of the difference between the tumor size at the two time points over the time period.
However, Jordan does not explicitly disclose
The reference time period being a total number of days
The following limitations would have been obvious in light of Jordan
The reference time period being a total number of days
See Fig. 4, which shows a timeline between scans taken for the patient, which includes a timeline on the scale of months.
Although it does not explicitly show the use of days as the basis for the growth rate, it would be obvious to try to use days as the basis for the growth rate because there are a finite number of identified, predictable potential solutions. Because rate is a change of a value over time, the base for the rate calculation would be any of the known standard units of time (e.g., years, months, weeks, days, hours, minutes, seconds).
Therefore, it would have been obvious to try, by one of ordinary skill in the art at the time of the invention was made, to use days as the base for the tumor growth rate calculation since there are a finite number of identified, predictable potential solutions (i.e. standard units of time) to the recognized need (a temporal reference for a change in tumor volume) and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success (the rate calculations could be adjusted for any time period to reflect the user’s needs (i.e., when analyzing two patient scans days apart, either using a relatively large unit like years or a relatively small unit like seconds would give numbers that are either unnecessarily small or unnecessarily small) (MPEP 2143.I.E).
Claim 9
Regarding claim 9, the combination of Jordan and Jiang teaches all the limitations of claim 1. Jordan further teaches
Acquiring post-treatment features of the one or more target lesions associated with a post-treatment scan of the target subject after treating the target subject according to the treatment plan
Par. [0036], “At block 207, the processing logic may receive an intra-treatment follow-up image.”
Determining a second set of features indicative of a second change in the one or more target lesions using the post-treatment features and at least one of the baseline features or the pre-treatment features
Par. [0051], “In one embodiment, a treatment response model is trained to predict patient's likelihood of disease progression, pseudo-progression, or hyper-progression using baseline and first intra-treatment follow-up scan.”
See par. [0057], “As previously described, embodiments of the disclosure may utilize one or more follow-up images, such as follow-up image 350, of the target that were captured after treatment.”
Intra-treatment features are considered to be post-treatment features because these are features that have been recorded after the patient has started receiving treatment.
Providing the second set of features to the one or more predictive models
Par. [0037], “At block 209, the processing logic may provide the intra-treatment follow-up image to the machine learning model.”
Generating a second predicted treatment response score to a second treatment plan for the target subject based on the second set of features and the one or more predictive models after treating the target subject according to the treatment plan
Par. [0038], “At block 211, the processing logic may generate an updated predicted treatment response score.”
Par. [0039], “At block 213, the processing logic may provide, based on the updated predicted treatment response score, an updated recommended treatment plan.”
Claim 10
Regarding claim 10, the combination of Jordan and Jiang teaches all the limitations of claim 1. Jordan further teaches
Wherein the predicted treatment response score comprises: an indication of pseudo-progression associated with the one or more target lesions, an indication of hyper-progression associated with the one or more target lesions, or an indication of overall target subject survival
Par. [0051], “In one embodiment, a treatment response model is trained to predict patient's likelihood of disease progression, pseudo-progression, or hyper-progression using baseline and first intra-treatment follow-up scan.”
Claim 11
Claim 11 is a system claim that recites a system configured to perform functions that are the same or substantially similar to the steps of the method claim of claim 1. Jordan discloses the following limitations not directly addressed by the rejection of claim 1:
A treatment analysis system comprising:
Par. [0020], “In one embodiment, system 100 includes server 101, network 106, and client device 150.”
A memory to store a pre-treatment scan of a target subject, and a processing device, operatively coupled to the memory, the processing device to: perform functions that are the same or substantially similar to the steps of the method of claim 1.
Par. [0022], “Each component may include hardware such as processing devices (e.g., processors, central processing units (CPUs), graphics processing units (GPUs), memory (e.g., random access memory (RAM), storage devices (e.g., hard-disk drive (HDD), solid-state drive (SSD), etc.), and other hardware devices (e.g., sound card, video card, etc.). The server 100 may comprise any suitable type of computing device or machine that has a programmable processor including, for example, server computers, desktop computers, laptop computers, tablet computers, smartphones, set-top boxes, etc. In some examples, the server 101 may comprise a single machine or may include multiple interconnected machines (e.g., multiple servers configured in a cluster)… The OS of a server may manage the execution of other components (e.g., software, applications, etc.) and/or may manage access to the hardware (e.g., processors, memory, storage devices etc.) of the computing device.”
Par. [0062], “he exemplary computer system 500 includes a processing device 502, a main memory 504 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), a static memory 506 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 518, which communicate with each other via a bus 530. Any of the signals provided over various buses described herein may be time multiplexed with other signals and provided over one or more common buses.”
Par. [0065], “The machine-readable storage medium 528 may also be used to store instructions to perform the methods and operations described herein. While the machine-readable storage medium 528 is shown in an exemplary embodiment to be a single medium, the term “machine-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) that store the one or more sets of instructions. A machine-readable medium includes any mechanism for storing information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). The machine-readable medium may include, but is not limited to, magnetic storage medium (e.g., floppy diskette); optical storage medium (e.g., CD-ROM); magneto-optical storage medium; read-only memory (ROM); random-access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or another type of medium suitable for storing electronic instructions.”
Please refer to the rejection of claim 1 for additional limitations.
Claims 12-19
Claims 12-19 are system claims that are ultimately dependent from claim 11 and recite limitations that are the same or substantially similar to the limitations of claims 2-10. Please refer to the rejections of claims 11 and 2-10.
Claim 20
Claim 11 is a computer program product claim that recites a non-transitory computer-readable storage medium comprising instructions, which when executed by a processing device, cause the processing device to perform functions that are the same or substantially similar to the steps of the method claim of claim 1. Jordan discloses the following limitations not directly addressed by the rejection of claim 1:
A non-transitory computer-readable storage medium comprising instructions, which when executed by a processing device, cause the processing device to: perform functions that are the same or substantially similar to the steps of the method of claim 1.
Par. [0065], “The machine-readable storage medium 528 may also be used to store instructions to perform the methods and operations described herein. While the machine-readable storage medium 528 is shown in an exemplary embodiment to be a single medium, the term “machine-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) that store the one or more sets of instructions. A machine-readable medium includes any mechanism for storing information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). The machine-readable medium may include, but is not limited to, magnetic storage medium (e.g., floppy diskette); optical storage medium (e.g., CD-ROM); magneto-optical storage medium; read-only memory (ROM); random-access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or another type of medium suitable for storing electronic instructions.”
Please refer to the rejection of claim 1 for additional limitations.
Response to Arguments
101 Rejections
Applicant's arguments filed July 15, 2025, have been fully considered but they are not persuasive.
Applicant argues that amended claim 1 of the pending application is directed to patentable eligible subject matter for at least the same reasons as claim 2 in Example 49. Specifically, Applicant argues that both claim 2 of Example 49 and amended claim 1 of the pending application are directed to identifying a particular treatment for a patient. Applicant argues that amended claim 1 requires recommending, from a group of two particular treatments, a single treatment for treating the target lesion of the target subject instead of simply recommending any common treatment and then treating the target lesion according to the treatment plan.
The Examiner disagrees. Claim 2 of Example 49 is not eligible solely because of the administration of a treatment. Compound X is particular to a specific patient population (glaucoma patients at a high risk of post-implantation inflammation) and reduces scarring without the undesirable side effects of other drugs, providing a technical solution to a technical problem. Amended claim 1 of the pending application is not similar; the claim does not specify when or why the PD-1 agent would be used as a monotherapy versus in combination with chemotherapy, and so the treatment is not “particular” or “specific”. Amended claim 1 of the pending application is more similar to Claim 1 of Example 49, which is held to be ineligible. The treatment plan recommending treating the target lesion with either the PD-1 agent as the monotherapy, or the PD-1 agent in combination with chemotherapy is deemed to be similar to “administering an appropriate treatment”.
Applicant argues that neither Jordan nor Jiang teach the amended limitations of “the predicted treatment response score is further based on an assessment of variation in lesion-specific features across a plurality of lesions within the same patient”. Applicant argues that Jordan summarizes lesion responses, not contrasting them. Applicant also argues that while Jiang may involve multiple lesions in a patient, it does not disclose that its system assesses the variations in lesion-specific features across a plurality of lesions within the same patient.
The examiner disagrees. Jordan teaches utilizing a follow-up image of a lung lesion captured after a (previous) treatment and a pre-treatment image of a target, the follow-up image being provided to the machine learning architecture to determine the effectiveness of a current treatment plan and should continue whether there is a more effective treatment option, and/or whether the treatment should be discontinued based on analysis of the follow-up image relative to pre-treatment image (e.g., assessment of variation in lesions within the same patient) [para. 0056-0057]. Jordan further teaches that the processing logic receives an intra-treatment follow-up image to be provided to the machine learning model to generate an updated predicted treatment response score (e.g., the predicted treatment response score is further based on an assessment of variation in lesions within the same patient) [para. 0036-0038]
Jiang teaches evaluating tumor volume change, changes in tumor volume, tumor volume change ratio (e.g., assessment of variation in lesion-specific features across a plurality of lesions within the same patient) and incorporating such in a two-dimensional coordinate system that represents previous treatment outcomes and possible future treatment outcomes [para. 0011, 0019].
Jiang also teaches treating the target lesion according to the treatment plan. As a result of evaluating tumor treatment resistance of a drug modality, the patient may be treated such that the therapy is unchanged, or continue to be treated with the same modality while having the dosage and/or frequency of administration changed, or changing the drugs/agents (e.g., treating the target lesion according to the treatment plan) [para. 0111, 0148-0149].
Thus, when viewed in combination, Jordan and Jiang teach “wherein the predicted treatment response score is further based on an assessment of variation in lesion-specific features across a plurality of lesions within the same patient”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following references each predict the therapeutic response of various therapies on tumors/lesions:
Kinsey (US PG Pub 20210350935)
Jain (US PG Pub 20210249101)
Colley (US PG Pub 20210090694)
Yu (US PG Pub 20200191792 and 20200126636)
Vladimirova (US PG Pub 20200105413)
Cummings (US PG Pub 20190025308)
Bagaev (US PG Pub 20180358132)
Hafez (US Patent 11,145,416)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER H CHOI whose telephone number is (469)295-9171. The examiner can normally be reached M-Th 9am-7pm.
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/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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