DETAILED ACTION
Status of Claims
This is a Final Office Action in response to the arguments and/or amendments filed on 11 March 2026.
Claim(s) 1 and 8 is/are amended.
Claim(s) 1, 3-6, 8, and 11-14 is/are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-6, 8, and 11-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1, which is representative of claim 8, recites in part: a method for treating a subject with cancer, comprising:
processing the received set of medical records for the subject using a [created]
providing a curated cancer dictionary, the curated cancer dictionary comprising a plurality of cancer-related terms each associated with one or more types of cancer, wherein each of the plurality of cancer-related terms is associated with one or more of a plurality of categories comprising: histology, diagnosis test method, stage, grade, and invasiveness;
parsing, using the curated cancer dictionary
analyzing,
generating a table of parsed cancer-related terms and cancer determination date for each of the plurality of subjects;
[creating]
generating, based on the processing in (b), a cancer determination for the subject, wherein the cancer determination comprises both an identification of a cancer type and a cancer determination date, wherein the identification of the cancer type comprises distinguishing between a plurality of cancer types, wherein the cancer type is bladder cancer; and
responsive to the cancer determination generated in (c),
The preceding recitation of the claim has had strikethroughs applied to the additional elements beyond the abstract idea to more clearly demonstrate the limitations setting forth the abstract idea. The remaining limitations describe a concept of creating a model to evaluate the type and diagnosis date of a cancer and applying the model to medical records. This concept describes a mental process that a healthcare practitioner should follow to evaluate medical records, similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the methods of organizing human activity sub-grouping. As such, these limitation set forth a method of organizing human activity. Alternatively, the identified concept is analogous to the examples of “observation”, “evaluation”, “judgement”, and “opinion” given in MPEP 2106.04(a)(2)(III) and can be performed in the human mind. As such, these limitations set forth a mental process. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 8 recites the additional element of a processor. This additional element is recited at an extremely high level of generality, and is interpreted as an instruction to implement the abstract idea with a generic computing device. As such, this additional element does not integrate the abstract idea into a practical application.
The claims further recite the additional element of receiving data from an electronic database and receiving data. This additional element does not impose a meaningful limitation on the claim and instead this limitation may reasonably be considered necessary data gathering for the abstract idea. Thus this additional element is understood to be insignificant extra-solution activity. As such, this additional element does not integrate the abstract idea into a practical application.
The claims further recite the additional elements of using a natural language processing algorithm, and a trained model, using classifier, and training a model. These limitations are high level invocations of machine learning, and amount to instructions to implement the abstract idea with a computing device. As such, these additional element does not integrate the abstract idea into a practical application.
The claims further recite the additional element of storing the trained model. This limitation is interpreted as an instruction to implement the abstract idea with a computing device. As such, this additional element does not integrate the abstract idea into a practical application.
The claims further recite the additional element of administering cancer-specific radiation therapy to the bladder cancer of the subject. Per MPEP 2106.04(d)(2). The treatment of a “cancer-specific radiation therapy” is analogous to the treatment of “administering a suitable medication” identified in MPEP 2106.04(d)(2)(a) as not a particular treatment. Thus the additional element should not be considered a particular treatment. Instead, the generalized invocation of radiation therapy for a type of cancer having determined the presence of that cancer may reasonably be considered insignificant application of the abstract idea. As such, this additional element is understood to be insignificant extra-solution activity and does not integrate the abstract idea into a practical application.
There are no further additional elements. When considered as a combination, the additional elements do not 1) provide an improvement in the functioning of a computer or an improvement to another technical field, 2) do not apply the abstract idea to effect a particular treatment, 3) do not implement the abstract idea with a particular machine or manufacture, and 4) do not apply or use the abstract idea in a meaningful way beyond generally linking the abstract idea to a particular technological environment. Instead, when considered as a combination, the additional elements only generally link the abstract idea and insignificant extra-solution activity to a technological environment of computing devices. As such, the combination of additional elements does not integrate the abstract idea into a practical application. Therefore the claims are determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, the claims recite additional elements which may be interpreted as generic computing devices used to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not amount to significantly more than the abstract idea.
As previously noted, the claims recite an additional element of receiving data from an electronic database and receiving data. Per MPEP 2106.05(d)(2), retrieving information in memory is a well-understood, routine, and conventional computer function. As such, this additional element does not amount to significantly more than the abstract idea.
As previously noted, the claims recite additional elements of using a natural language processing algorithm, and a trained model, using classifier, and training a model. Tominaga (US 4958285) demonstrates the conventionality of natural language processing (Column 1, Lines 11-14) long before the priority date of the claimed invention. Keeler et al. (US 5479573) demonstrates the conventionality of trained machine learning models (Column 18, Line 59 through Column 19 Line 51) long before the priority date of the claimed invention. Thus these additional elements are well-known, routine, and conventional activity. As such, these additional elements do not amount to significantly more than the abstract idea.
As previously noted, the claims recite an additional element of storing the trained model. Per MPEP 2106.05(d)(2), storing information in memory is a well-understood, routine, and conventional computer function. As such, this additional element does not amount to significantly more than the abstract idea.
As previously noted, the claims recite an additional element of administering cancer-specific radiation therapy to the bladder cancer of the subject. Pos et al. (Radical radiotherapy for invasive bladder cancer: What dose and fractionation schedule to choose?) demonstrates (“The standard radiotherapy (RT) regimen for invasive bladder cancer is irradiation of the whole bladder and tumor with a 2- to 3-cm margin to a dose of 60–66 Gy (1).” Page 1) that such radiation therapy for bladder cancer was conventional long before the priority date of the claimed invention. Thus this additional element is well-known, routine, and conventional activity. As such, this additional element does not amount to significantly more than the abstract idea.
There are no further additional elements. When considered as a combination, the additional elements do not 1) provide an improvement in the functioning of a computer or an improvement to another technical field, 2) do not apply the abstract idea to effect a particular treatment, 3) do not implement the abstract idea with a particular machine or manufacture, and 4) do not apply or use the abstract idea in a meaningful way beyond generally linking the abstract idea to a particular technological environment. Instead, when considered as a combination, the additional elements only generally link the abstract idea and insignificant extra-solution activity to a technological environment of computing devices. As such, the combination of additional elements does not amount to significantly more than the abstract idea.
Therefore, when considered individually and as a combination, the additional elements of the independent claims do not amount to significantly more than the abstract idea. Thus the independent claims are not patent eligible.
Dependent claims 3, 4, 11, and 12 further narrow the abstract ideas, but the claims continue to recite an abstract idea, albeit a narrowed one. Claims 3, 4, 11, and 12 recite no further additional elements. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for the same reasons as explained above. Therefore these claims continue to be directed to an abstract idea. The previously identified additional elements, individually and as a combination, do not amount to significantly more than the narrowed abstract idea for the same reasons as explained above. Claims 5, 6, 13, and 14 recite a “gradient boosting classifier.” Current USPTO guidance indicates claims which use math (e.g., training a machine learning model) but do not expressly mention an algorithm should be understood as “merely involv[ing] a judicial exception” rather than “recit[ing] a judicial exception.” See August 4, 2025 Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101. Examiner notes that one of ordinary skill in the art would understand a “gradient boosting classifier” to have been trained by a gradient boosting algorithm. However, the claim does not specifically recite the use of the algorithm but rather references a model that is known to be trained by such an algorithm. Thus per current USPTO guidance, the limitation is considered to “merely involve” an abstract idea rather than reciting an abstract idea. Therefore these limitations are considered additional elements beyond the abstract idea. Kearny et al. (US 2020/0372301 A1) demonstrates (See at least [0174] and [0185]) that gradient boosting classifiers were conventional before the priority date of the claimed invention. Thus this additional element does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. When considered in combination with the previously identified additional elements, the combination of additional elements only generally links the abstract idea and insignificant extra-solution activity to a technological environment of computing devices. Thus the combination of additional elements does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Because the dependent claims remain directed to an abstract idea without reciting significantly more, the dependent claims are not patent eligible.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3-6, 8, and 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gippetti et al. (US 2021/0391087 A1) in view of Lucas et al. (US 2020/0126663 A1) and Frumkin et al. (US 9476100 B1).
Regarding Claim 1: Gippetti discloses a method for treating a subject with cancer, comprising:
(a) receiving, from an electronic health record database, a set of medical records for the subject (In step 710, process 700 includes accessing a database storing one or more medical records associated with a patient. For example, system 130 may access patient medical records from local database 132 or from an external data source, such as data sources 120. The medical record may comprise one or more electronic files, such as text files, image files, PDF files, XLM files, YAML files, or the like. See at least [0057]).
(b) processing the received set of medical records for the subject using a trained cancer determination model (The determination in step 720 may be made using a first machine learning model, such as trained model 420, described above. The determination in step 720 may be based on unstructured information included in the one or more medical records. For example, this may include unstructured data 210, as shown in FIG. 2 and described above. The unstructured information may include text written by a health care provider, a radiology report, a pathology report, or various other forms of text associated with the patient. In some embodiments the medical record may further include additional structured data 220. See at least [0058]. Also: the determination in step 740 may made using a second machine learning model, such as trained model 430. Alternatively, or additionally, a single model or more than two models may be used for steps 730 and 740. See at least [0060]).
wherein the trained cancer determination model is trained by: (ii) receiving a training dataset comprising a plurality of medical records for each of a plurality of subjects; (v) generating a table of parsed cancer-related terms and the cancer determination date for each of the plurality of subjects; (vi) training a cancer determination model to generate a cancer determination comprising an identification of a cancer type and a cancer determination date for each of the plurality of subjects based on the table of parsed cancer-related terms and the cancer determination date; and (vii) storing the trained cancer determination model (a training algorithm, such as an artificial neural network may receive training data in the form of unstructured data from medical records. The training data may be labeled to indicate particular conditions that patients associated with the unstructured data have been diagnosed with. As a result, a model may be trained to determine conditions, such as whether a patient has been diagnosed with brain metastasis, based on unstructured data in patient medical records. See at least [0042]. Also: may be trained based on a training data set of documents known to include diagnoses for a particular condition, along with diagnosis dates for the condition. As a result, the second model may be trained to indicate the dates at which a relevant diagnosis has been made. See at least [0044]).
(c) generating, based on the processing in (b), a cancer determination for the subject, wherein the cancer determination comprises both an identification of a cancer type and a cancer determination date, wherein the identification of the cancer type comprises distinguishing between a plurality of cancer types (In step 750, process 700 includes generating an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date. See at least [0062]. Also: generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date. See at least [0007]. Also: for a patient diagnosed with cancer, the condition may be a particular site of metastasis of the cancer (e.g., whether the patient has been diagnosed with metastasis in the brain, liver, bones, lungs, adrenal gland, peritoneum, or various other sites of metastases). See at least [0034]).
Gippetti does not clearly disclose wherein the trained cancer determination model is trained by: (i) providing a curated cancer dictionary, the curated cancer dictionary comprising a plurality of cancer-related terms each associated with one or more types of cancer, wherein each of the plurality of cancer-related terms is associated with one or more of a plurality of categories comprising: histology, diagnosis test method, stage, grade, and invasiveness; (iii) parsing, using the curated cancer dictionary and a natural language processing (NLP) algorithm, the training dataset to identify cancer-related terms in the plurality of medical records, wherein each of the identified cancer-related terms is associated with one or more of the plurality of categories; (iv) analyzing, using a classifier, the training dataset to identify a cancer determination date for each of the plurality of subjects;
However, Lucas teaches providing a curated cancer dictionary, the curated cancer dictionary comprising a plurality of cancer-related terms each associated with one or more types of cancer, wherein each of the plurality of cancer-related terms is associated with one or more of a plurality of categories comprising: histology, diagnosis test method, stage, grade, and invasiveness; (iii) parsing, using the curated cancer dictionary and a natural language processing (NLP) algorithm, the training dataset to identify cancer-related terms in the plurality of medical records, wherein each of the identified cancer-related terms is associated with one or more of the plurality of categories; (iv) analyzing, using a classifier, the training dataset to identify a cancer determination date for each of the plurality of subjects (UMLS is comprised of a number of independently maintained clinical dictionaries and ontologies (such as those for cancer diagnosis & treatment, dentistry, veterinarian medicine, etc.). See at least [0218], stage 66 for parsing may include NLP algorithms for sentence splitting and candidate extraction, stage 68 for dictionary lookups may include entity linking. See at least [0113], The processing may occur over a plurality of MLA processing steps and/or sub-steps, each of which may output certain features to the predefined model after each processing step as discussed in further detail below. Each of these features may additionally have a required or optional tag identifying whether the feature must be present or may be present. Furthermore, each feature may have a list of expected key health information types. For example, a header may expect a patient name, a patient date of birth, an institution name, a report date, a diagnosis, etc. The list of features and corresponding expected key health information may be encoded into the predefined model. Furthermore, a mask, natural language processing model, and other extraction guidelines may be stored in the predefined model. Extraction guidelines may include reliability checks to ensure that the information is correct. For example, a diagnosis date may not occur before birth. See at least [0056]).
Gippetti describes a machine learning model which determines a cancer type and an associated date based on training data in the form of labeled medical records, upon which the claimed invention’s techniques for extracting labeling information from medical records can be seen as an improvement. However, Lucas demonstrates that the prior art already knew of techniques for extracting labeling information from health records. One of ordinary skill in the art could have easily applied the techniques of Lucas to the system of Gippetti in order to generate the labeled medical records. One of ordinary skill in the art would have recognized that such an application of Lucas would have resulted in an improved system which could generate its own training data. As such, the application of Lucas would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Gippetti and the teachings of Lucas.
Gippetti does not expressly disclose wherein the cancer type is bladder cancer or (d) responsive to the cancer determination generated in (c), administering cancer-specific radiation therapy to the bladder cancer of the subject.
Frumkin teaches that bladder cancer is a type of cancer and responsive to a cancer determination, administering cancer-specific radiation therapy to the bladder cancer (identifying bladder cancer according to the method of the present invention, and administering to said subject anticancer therapy. … Methods for treating bladder cancer may include one or more of: … radiation therapy. See at least column 20, lines 46-51).
Gippetti and Lucas suggests a system which analyzes medical records to determine that a patient has cancer, which differs from the claimed invention which is specific to bladder cancer and which further requires the treatment of the bladder cancer with radiation. However, Frumkin demonstrates that bladder cancers were already known and that when they are present they can be treated with radiation therapy. One of ordinary skill in the art could have trivially substituted Frumkin’s bladder cancer into the system of Gippetti and Lucas so that the system recognizes the presence of bladder cancer specifically, and subsequent to that applied Frumkin’s treatment of bladder cancer when it is determined. One of ordinary skill in the art would have recognized that these modifications would have resulted in a system which would determine whether users have bladder cancer and then provide them with an appropriate treatment. As such, the incorporation of Frumkin would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Gippetti and the teachings of Lucas and Frumkin.
Regarding Claim 3 and 11: Gippetti in view of Lucas and Frumkin makes obvious the above limitations. Additionally, Lucas teaches wherein the curated cancer dictionary is generated at least in part by: (i) receiving a plurality of medical records for a plurality of patients; (ii) manually reviewing, by a clinician, the plurality of medical records for each of the plurality of patients; and (iii) generating, using the annotated medical records, the curated cancer dictionary (The UMLS is a list of medical concepts (described in more detail with respect to FIG. 3, below) and the UMLS CUI refers to the CUI field, which is UMLS' universal identifier. UMLS is comprised of a number of independently maintained clinical dictionaries and ontologies (such as those for cancer diagnosis & treatment, dentistry, veterinarian medicine, etc.). See at least [0110]). Lucas does not expressly teach wherein manually reviewing by the clinician comprises annotating the plurality of medical records with a diagnosed cancer and a date of diagnosis. However, Lucas separately teaches the clinician annotating the plurality of medical records with a diagnosed cancer and a date of diagnosis (the human takes into account the prediction as well as other information in the clinical documents before making their final annotation. This final annotation is utilized as the “gold standard” by which the MLA should operate and can immediately add the labeled documents and the corresponding annotation to the training data set to improve the results when the MLA is trained in the future. Each edge case that is corrected by a human, even each additional question that a human answers above and beyond the erroneous outputs may directly help the machine learning model answer the corresponding question correctly in the future. See at least [0228]).
Gippetti, Lucas, and Frumkin suggest techniques for analyzing a patient electronic medical records that is based on EMR training data sets, upon which the claimed invention’s inclusion of annotation to the dictionary generation process can be seen as an improvement. However, Lucas separately teaches EMR annotation for the purposes of generating training data. One of ordinary skill in the art could have easily applied Lucas’ annotation to data used to generate a dictionary. Further, one of ordinary skill in the art would have recognized that such an application of Lucas would have resulted in an improved system which would have additional training data for training the machine learning models. As such, the application of Lucas, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Gippetti and the teachings of Lucas and Frumkin.
Regarding Claim 4 and 12: Gippetti in view of Lucas and Frumkin makes obvious the above limitations. Additionally, Lucas teaches wherein the plurality of medical records or the plurality of subjects of the training dataset are curated by a clinician before training the cancer diagnosis model (the human takes into account the prediction as well as other information in the clinical documents before making their final annotation. This final annotation is utilized as the “gold standard” by which the MLA should operate and can immediately add the labeled documents and the corresponding annotation to the training data set to improve the results when the MLA is trained in the future. Each edge case that is corrected by a human, even each additional question that a human answers above and beyond the erroneous outputs may directly help the machine learning model answer the corresponding question correctly in the future. See at least [0228]).
Regarding Claim 5 and 13: Gippetti in view of Lucas and Frumkin makes obvious the above limitations. Additionally, Gippetti discloses wherein the cancer determination model comprises a gradient boosting classifier (Consistent with the present disclosure, various other machine learning algorithms may be used, including a logistic regression, a linear regression, a regression, a random forest, a K-Nearest Neighbor (KNN) model (for example as described above), a K-Means model, a decision tree, a cox proportional hazards regression model, a Naïve Bayes model, a Support Vector Machines (SVM) model, a gradient boosting algorithm, or any other form of machine learning model or algorithm. See at least [0042]).
Regarding Claim 6 and 14: Gippetti in view of Lucas and Frumkin makes obvious the above limitations. Additionally, Gippetti discloses wherein the classifier comprises a gradient boosting classifier (Consistent with the present disclosure, various other machine learning algorithms may be used, including a logistic regression, a linear regression, a regression, a random forest, a K-Nearest Neighbor (KNN) model (for example as described above), a K-Means model, a decision tree, a cox proportional hazards regression model, a Naïve Bayes model, a Support Vector Machines (SVM) model, a gradient boosting algorithm, or any other form of machine learning model or algorithm. See at least [0042]).
Response to Arguments
Applicant’s Argument Regarding 112(a) Rejection(s) of claim(s) 1, 3-6, 8, and 11-14: Applicant has amended claims 1 and 8 to clarify the claimed subject matter.
Examiner’s Response: Applicant's amendments filed 11 March 2026 have been fully considered and they resolve the 112(a) rejection(s). The rejection(s) of claim(s) 1, 3-6, 8, and 11-14 under 112(a) is/are withdrawn.
Applicant’s Argument Regarding 101 Rejections of claims 1, 3-6, 8, and 11-14:
Claims 1 and 8 are patent-eligible, for at least the reason that they encompass effecting a particular treatment—namely cancer-specific radiation therapy—administered to treat a particular disease: bladder cancer. Applicant respectfully submits that radiation therapy administered to a bladder cancer is a particular treatment.
The MPEP states that [e]xamples of ‘treatment’ and [‘]prophylaxis’ limitations encompass limitations that treat or prevent a disease or medical condition, including, e.g., acupuncture, administration of medication, dialysis, organ transplants, phototherapy, physiotherapy, radiation therapy, surgery, and the like. In particular Applicant respectfully notes that MPEP specifically enumerates “radiation therapy” as an example of a treatment to treat a disease.
Regarding the requirements of factor (A), each of claims 1 and 8 recites a treatment that is “particular” because it is “specifically identified so that it does not encompass all applications of” the alleged judicial exception of generating “a cancer determination for the subject.” Further, the treatment of claims 1 and 8 is cancer-specific radiation therapy administered to bladder cancer, and therefore does not encompass all applications of detecting bladder cancer. As such, Claim 1 and 8 recite a particular treatment under factor (a).
Regarding the requirements of factor (b), the treatment recited in claims 1 and 8 have more than a nominal or insignificant relationship between the cancer-specific radiation therapy administered to the bladder cancer and the cancer determination. Therefore, the treatment recited in claims 1 and 8 has a significant relationship with the detected bladder cancer, under the analysis of factor (b).
Regarding the requirements of factor (c), claims 1 and 8 impose meaningful limits on the cancer determination. Responsive to the cancer determination, claims 1 and 8 encompass administering not just any therapy, but cancer-specific radiation therapy to the bladder cancer. The subject does not merely receive a non-particular or generalized treatment for bladder cancer, but rather cancer-specific radiation therapy is being applied to the bladder cancer. Therefore claims 1 and 8 encompass a particular treatment under the analysis of factor (c).
Claim 1 and 8 parallel patent-eligible claim 2 of Example 43 of the Life Sciences & Data Processing Examples of Appendix 1 of the Subject Matter Eligibility Guidance, October 2019 Update. Indeed, claims 1 and 8 encompass a treatment that is even more particular than that in Example 43, claim 2.
Examiner’s Response: Applicant's arguments filed 11 March 2026 have been fully considered but they are not persuasive.
MPEP 2106.04(d)(2)(a) contains the most relevant example regarding the current claims. “For example, consider a claim that recites mentally analyzing information to identify if a patient has a genotype associated with poor metabolism of beta blocker medications. This falls within the mental process grouping of abstract ideas enumerated in MPEP § 2106.04(a). The claim also recites ‘administering a lower than normal dosage of a beta blocker medication to a patient identified as having the poor metabolizer genotype.’ This administration step is particular, and it integrates the mental analysis step into a practical application. Conversely, consider a claim that recites the same abstract idea and ‘administering a suitable medication to a patient.’ This administration step is not particular, and is instead merely instructions to ‘apply’ the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application.” Thus the MPEP clearly indicates that a claim which (1) mentions a condition, and (2) recites the administration of a general mode of treatment (here, “medication”) for that condition does not constitute a “particular treatment.” The present claim similar 1) mentions a condition and 2) recites the administration of a general mode of treatment (here, “radiation therapy”) suitable for the condition. With this guidance in mind, the additional element does not appear to be a particular treatment.
Examiner agrees that “radiation therapy” should be understood as a “treatment.” The issue at hand is whether a treatment described as “cancer-specific radiation therapy” qualifies as a “particular treatment” which is not informed by the MPEP’s identification of radiation therapy as a “treatment.”
Applicant’s suggested understanding of factor (A) appears contrary to the example provided in MPEP 2106.04(d)(2)(a). The abstract idea of “identify if a patient has a genotype associated with poor metabolism of beta blocker medications” could be addressed by administering an exercise regime to the patient. Such a treatment is a treatment other than “administering a suitable medication” meaning that “administering a suitable medication” does not actually encompass all applications of the abstract idea. However, the MPEP 2106.04(d)(2)(a) clearly indicates that “administering a suitable medication” does not constitute a particular treatment. Further, as noted above, the claimed treatment appears particularly similar to the example of “administering a suitable medication” which is specifically identified as insufficiently particular. Thus based on the example, factor (A) indicates that the treatment does not constitute a particular treatment.
Based on the blood glucose example of MPEP 2106.04(d)(2)(b), Examiner agrees that the additional element has “more than a nominal or significant relationship to the exception.” Examiner notes that the factors are described as “relevant” and that no single factor is dispositive.
Applicant’s argument regarding factor (c) appears to be essentially the same as regarding factor (A) addressed above. Examiner additional notes that MPEP 2106.04(d)(2)(c) references MPEP 2106.05(g) as helpful in determining whether a treatment is merely extra-solution activity. MPEP 2106.05(g) provides as an example of insignificant extra-solution activity “a printer that is used to output a report of fraudulent transactions.” Examiner notes that there are other means of outputting data beyond printers, and yet the printer was insignificant extra-solution activity. Further MPEP 2106.04(d)(2)(c) notes “Cutting hair after first determining the hair style” as an example of insignificant application. However, the claim at issue in In re Brown actually recited “using scissors to cut hair according to said assigned hair pattern in each of the said partial zones.” That abstract idea could be alternatively applied with hair clippers, and yet using scissors was an insignificant application. These examples indicate that the application of an abstract idea with a type of tool may still be insignificant application or insignificant extra-solution activity. The application of radiotherapy treatment after determining the presence of cancer seems particularly analogous to the utilization of scissors after determining a hair style. Thus the treatment appears to be mere extra-solution activity and factor (C) indicates that the treatment does not constitute a particular treatment.
Examiner disagrees regarding the comparative specificity between the claimed treatment and the treatment in example 43 claim 2. Example 43 notes that “glucocorticoids … are the conventional first-line treatment”. Thus in example 43 claim 2 there is a meaningful exclusion of a subset of the general mode of treatment (medicinal agents). In contrast, the treatment at issue is a general mode of treatment (radiation therapy) limited to examples which are “cancer-specific.” The broadest reasonable interpretation of a “cancer-specific radiation therapy” encompasses any radiation therapy which treats the relevant cancer. Thus the further limitation on the general mode of treatment simply excludes radiation therapies which do not treat the condition. That is not more specific than a limitation which actually excludes useful subsets of the general treatment modality.
Conclusion
Applicant's submission of an information disclosure statement under 37 CFR 1.97(c) with the timing fee set forth in 37 CFR 1.17(p) on 11 March 2026 prompted the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 609.04(b). 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 Bion A Shelden whose telephone number is (571)270-0515. The examiner can normally be reached M-F, 12pm-10pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached at (571) 272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Bion A Shelden/Primary Examiner, Art Unit 3685 2026-06-25
Prosecution Insights