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 .
DETAILED ACTION
The present Office Action is in response to the Request for Continued Examination dated 02/27/2026
In the amendment dated 02/27/2026, the following occurred: Claims 52, 54-55, 58, 60, 76 and 78 were amended. Claims 1-51, 57 and 79 were canceled. Claim 81-88 are new.
Claims 52-56, 58-78 and 80-88 are currently pending.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/27/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/27/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 52-56, 58-78 and 80-88 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.
Claim 52 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recite a method for performing a virtual clinical trial.
Regarding claim 52, the limitation of (a) obtaining a clinical summary for a first subject having or suspected of having cancer, wherein the clinical summary comprises a set of treatment rationales for the first subject, wherein the one or more treatment rationales are in controlled natural language data format; (b) publishing the clinical summary; (c) collecting peer review feedback scores pertaining to one or more treatment rationales of the clinical summary; (d) storing one or more peer-reviewed scores treatment rationales and clinical outcomes associated with the one or more peer-reviewed scored treatment rationales, wherein the one or more peer-reviewed scored treatment rationales are converted into a computer-readable data format prior to storing; (e) selecting, using a decision model, a machine learning model from a plurality of generated machine learning models to process information for a second subject having or suspected of having the cancer, wherein the decision model selects the machine learning model based at least in part on the cancer or a characteristic of the second subject; (f) querying to determine a predicted outcome of a treatment protocol for treating the second subject for the cancer, wherein the predicted outcome is determined based at least in part on analysis of the stored one or more peer-review scored treatment rationales and the clinical outcomes associated with the one or more peer-review scored treatment rationales; and (g) selecting the second subject to receive the treatment protocol, based at least in part on the predicted outcome as drafted, is a process that, under the broadest reasonable interpretation, covers a method organizing human but for the recitation of generic computer components. The Examiner notes that Claim 52 is not tied to any particular technological environment. That is, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). For example the claims encompass a method for performing a virtual clinical trial in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claim further recites “selecting, using a decision model, a machine learning model.” When given the broadest reasonable interpretation in light of the disclosure, using the decision model to select a machine learning model to process information, determine predicted outcome of a treatment protocol, query a knowledge base and select a subject to receive treatment as described in the specification represent the creation of mathematical interrelationships between data, see Spec. Para. [0020], [0055] and [0066]-[0081]. Thus given the broadest reasonable interpretation, the Examiner interprets the decision model and the machine learning model to be implemented using existing, known mathematical techniques and interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
This judicial exception is not integrated into a practical application. Claim 52 is purely directed to an abstract idea and is not tied to any particular technological environment.
Claim 52 recite the additional element of to an expert clinician network and a knowledge base. These additional element are recited at a high level of generality (i.e. a general means to output/receive/transmit data) and amount to extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Claim 52 also recites the additional element of a decision model and a machine learning model to process information, determine predicted outcome of a treatment protocol, query a knowledge base and select a subject to receive treatment. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to process information, determine predicted outcome of a treatment protocol, query a knowledge base and select a subject to receive treatment merely confines the use of the abstract idea (i.e., the machine learning model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
Claim 52 further recites the additional element of administering the treatment protocol to the second subject, wherein the treatment protocol comprises chemotherapy, radiation therapy, surgery, targeted therapy, hormone therapy, stem cell transplant, or immunotherapy. This additional element is (“apply it”) to the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of an expert clinician network and a knowledge base were considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional activity in the field. MPEP 2016.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine and conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Also as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model to process information, determine predicted outcome of a treatment protocol, query a knowledge base and select a subject to receive treatment was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the machine learning model) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Also as discussed above with respect to integration of the abstract idea into a practical application, the additional element of administering the treatment protocol to the second subject, wherein the treatment protocol comprises chemotherapy, radiation therapy, surgery, targeted therapy, hormone therapy, stem cell transplant, or immunotherapy was interpreted as (“apply it”). This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 53-56, 58-78 and 80-88 are similarly rejected because they either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Dependent claim 53 further defines the clinical outcomes, which further defines the abstract idea. Dependent claims 54-56 further define the peer-reviewed one or more treatment rationales, which further defines the abstract idea. Dependent claims 58 and 59 further defines the cancer, which further defines the abstract idea. Dependent claim 60 further defines the treatment protocol, which further defines the abstract idea. Dependent claim 61 further defines prioritizing a set of ranked treatment protocols, which further defines the abstract idea. Dependent claims 62-63 further define the prioritizing, which further defines the abstract idea. Dependent claims 64-67 further define the Bayesian decision process, which further defines the abstract idea. Dependent claim 68 further defines collecting outcome data, which further defines the abstract idea. Dependent claim 69 further defines the outcome data, which further defines the abstract idea. Dependent claim 70 further defines training a machine learning algorithm, which further defines the abstract idea. Dependent claim 71 further defines presenting a plurality of selectable clinical case templates and generating the clinical summary, which further defines the abstract idea. Dependent claims 72-73 further define capturing the clinical case, which further defines the abstract idea. Dependent claim 74 further defines conducting an adaptive Delphi survey process, which further defines the abstract idea. Dependent claim 75 further defines the computer-readable data format, which further defines the abstract idea. Dependent claim 76 further defines receiving user input, which further defines the abstract idea. Dependent claim 77 further defines the cohort, which further defines the abstract idea. Dependent claim 78 further defines using the plurality of peer-reviewed treatment rationales, which further defines the abstract idea. Dependent claim 80 further defines the plurality of peer-reviewed treatment rationales and the associated clinical outcomes, which further defines the abstract idea. Dependent claim 81 further defines the characteristic of the second subject, which further defines the abstract idea. Dependent claim 82 and 84 further defines what the selection of the machine learning model is based on, which further defines the abstract idea. Dependent claim 83 further defines the plurality of generated machine learning models, which further defines the abstract idea. Dependent claim 85 further defines the predictive ability of the selected machine learning model, which further defines the abstract idea. Dependent claim 86 further defines the decision model evaluates the predictive ability, which further defines the abstract idea. Dependent claim 87 further defines determining by the decision model a risk assessment weight and a benefit assessment weight, which further defines the abstract idea. Dependent claim 88 further defines selecting the machine learning model based at least in part on the risk assessment weight and the benefit assessment weight, which further defines the abstract idea. As a result, claims 53-56, 58-78 and 80-88 are rejected for the same reason presented above with respect to claim 52.
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.
Claims 52-56, 68-72, 75-77, 80-82 and 84-88 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes (US 2017/0076046) and in further view of Peták (US 2016/0224760).
REGARDING CLAIM 52
Barnes discloses a method for performing a virtual clinical trial, comprising:(a) obtaining a clinical summary for a first subject having or suspected of having cancer, wherein the clinical summary comprises one or more treatment rationales for the first subject, wherein the one or more treatment rationales are in controlled natural language data format ([0005] teaches group defined treatment plan for a patient and [0077] teaches the preparation, presentation and archiving of information associated with cancer patient treatment plans. [0114] teaches a summary section that includes information on treatment plans (interpreted by examiner as obtaining a clinical summary for a first subject having or suspected of having a disease, wherein the clinical summary comprises one or more treatment rationales for the first subject) [0012] teaches the use of structured reporting functionality (interpreted by examiner as controlled natural language data format)); (b) publishing the clinical summary to an expert clinician network (([0081] teaches a virtual PinBoard that is saved and documented as supporting evidence for the treatment decision made by the board. [0187] teaches the virtual PinBoard can provide the user with the clinical information selected by the board members as the most relevant information in formulating a treatment plan for a patient. [0125] teaches the “pinning” of information for the virtual PinBoard can often be performed by medical personnel from the department corresponding to the information's category, where examples of medical categories can include treatments [0121] teaches the use of a graphical user interface and displaying treatments (interpreted by examiner as publishing the clinical summary to an expert clinician network) [0122] teaches plots of treatment); (c) collecting, from the expert clinical network, peer review feedback pertaining to one or more treatment rationale of the clinical summary ([0272] teaches at tool that allows clinicians to enter treatment response data and [0079] teaches recommendation forms that provides the medical personnel the ability to document patient contextual information, the relevant lab reports and clinical tests results that are presented along with structured board recommendations that can be mined for analyses and future disease patterns and [0086] teaches an interactive recommendation form that can provide the clinical context for the patient and an easy way to document, through structured reporting, the treatment plans for the patient (interpreted by examiner as collecting peer review feedback. The peer review feedback is interpreted by examiner as the peer-review feedback of Peták below). [0131] teaches preparing the specific recommendation or treatment plan for the patient (interpreted by examiner as feedback pertaining to a treatment rationale of the clinical summary) and the recommendation form can be provided to each of the participating doctors to obtain their approval of the treatment plan in the recommendation form. [0172] teaches recommendation form can be used by medical personnel during a board meeting to document the discussion and treatment decisions for each patient); (d) storing one or more peer-review treatment rationales and clinical outcomes associated with the one or more peer-review treatment rationales in a knowledge base, wherein the one or more peer-reviewed scored treatment rationales are converted into a computer-readable data format prior to the storing ([0272] teaches at tool that allows clinicians to enter treatment response data and [0079] teaches recommendation forms that provides the medical personnel the ability to document patient contextual information, the relevant lab reports and clinical tests results that are presented along with structured board recommendations that can be mined for analyses and future disease patterns. [0078] teaches display patient profiles of patients with similar clinical characteristics, treatments and outcomes. [0111] teaches an informatics platform can retrieve data from database and store data in database (interpreted by examiner as the knowledge base). [0123] teaches PinBoard stores relevant clinical information, by category for a patient. [0134] teaches the workflow tool uses relevant patient and clinical attributes as key words or parameters in formulating a search query to perform a search in one or more databases such as clinicaltrials.gov (interpreted by examiner as the knowledge base for storing one or more peer reviewed treatment rationales and clinical outcomes associated with the one or more peer-review treatment rationales. The one or more peer-review treatment rationales is interpreted by examiner as the one or more scored peer-review treatment rationales of Peták below) for potential clinical trials for participation by the patient and [0172] teaches the interactive recommendation form can receive information on the patient currently displayed in the workflow tool and then retrieve and aggregate the relevant patient information from the database. [0258] teaches the method may be stored on a computer-readable medium and may comprise logical instructions that are executed by a processor to perform operations comprising retrieving aggregated and comprehensive electronic clinical data of a patient (interpreted by examiner as means to convert the one or more peer-reviewed scored treatment rationales into a computer-readable data format prior to the storing)); (e) selecting, using a decision model, a machine learning model from a plurality of generated machine learning models to process information for a second subject having or suspected of having the cancer, wherein the decision model selects the machine learning model based at least in part on the cancer or a characteristic of the second subject (Barnes at [0101] allows a user to select an application, [0102] teaches by selecting one of the software applications, the medical personnel may launch the application, initiate the querying of clinical information from the informatics platform database and execute the application to perform the desired actions with the pre-specified clinical data, so the user would not have to do any additional data entry to obtain the desired output results, [0220] teaches provide an interface for clinicians and researchers to interactively select one or multiple models to explore with the system dataset, [0226] teaches selecting the model that is of interest for example the Survival Analysis: Kaplan Meier (not shown). The system 1010 auto populates relevant clinical values such as Age, Gender, Type of Cancer, biomarkers, etc. (step 1160). Researchers that are more focused on pools of patient types would manually input clinical values that relate to the population of patients that they are interested in further interrogating the data and [0232] teaches the ability to select from any of the available “biostatistical analysis” models that have been integrated with the system software application (interpreted by examiner as means to select, using a decision model, a machine learning model from a plurality of generated machine learning models to process information for a second subject having or suspected of having the cancer, wherein the decision model selects the machine learning model based at least in part on the cancer or a characteristic of the second subject)); (f) querying the knowledge base using the selected machine learning model to determine a predicted outcome of a treatment protocol for treating the second subject for the cancer, wherein the predicted outcome is determined based at least in part on analysis of the stored one or more peer-review treatment rationales and the clinical outcomes associated with the one or more peer-review treatment rationales by the selected machine learning model (at [0057] teaches cancer treatment and [0077] teaches the present application also generally pertains to a workflow tool that enables the preparation, presentation and archiving of information associated with cancer patient treatment plans. [0078] teaches display patient profiles of patients with similar clinical characteristics, treatments and outcomes. [0079] teaches providing medical personnel the ability to document patient contextual information, the relevant lab reports and clinical tests results that are presented along with structured tumor board recommendations. [0106] teaches the server of the system can obtain data and information that can be stored in the database of the server and can then index and store the retrieved information from the EMR system 20 and the information systems 22 in a database that can be accessed by the informatics platform. [0109] teaches the server can extract data from the EMR and the information systems and provide the data to database and an informatics platform to integrate the extracted data and information from the EMR system, the information systems, the third party applications and the third party data sources stored in the database and provide the corresponding tools, interfaces and functionality to permit users of the client devices to retrieve and use the information in the database. [0224] teaches generating analytical graphs that relate to predictive, potential outcome, latency, remission for a patient (interpreted by examiner as providing a registry for collecting outcome data of the captured clinical case to update the knowledge base, wherein the predicted outcome is determined based at least in part on analysis of the stored one or more peer-review treatment rationales and the clinical outcomes associated with the one or more peer-review treatment rationales) [0214], [0218]-[0220], [0224], [0226], [0232] and [0249] teach the use of machine learning models, statistical models, biostatistical models, predictive models, analytical models and so forth to determine a predicted outcome of a treatment protocol (interpreted by examiner as the selected machine learning model)); and (g) selecting the second subject to receive the treatment protocol using the machine learning model, based at least in part on the predicted outcome ([0101] teaches that from this information, the application could map these variables against NCCN (National Comprehensive Cancer Network) clinical guidelines, display how these variables map out to the most appropriate guideline and also display the next line of recommended treatment for a specific patient. [0133] teaches a treatment section that provides a structured interface to document recommended treatments (e.g., surgery, radiotherapy, chemotherapy, clinical trial and other therapy) and additional clinical tests (e.g., radiology, pathology and molecular) that can be performed for the patient.).
Barnes does not explicitly disclose, however Peták discloses:
peer review feedback scores pertaining to one or more treatment rationale and one or more peer-review scored treatment rationales (Peták at [0007] teaches the present application is directed to a method to treat human diseases based on therapy ranking in a linked, learning database of the clinical experience generated by others who have used an adaptive decision support system that obviates one or more of the problems due to limitations and disadvantages of the related art. [0008] teaches that more particularly, it relates to assigning ranks to treatment options based on their expected efficacy and side effects and clinical experience. More particularly, it relates to the clinical annotation of rare genetic variants, treatments, and response to treatments. [0152] teaches receiving feedback about the clinical responses. Accordingly, the user experience and compliance can be enhanced by providing an online patient treatment timeline tool to physicians to easily keep record of appointments, diagnostic tests, treatments and their efficacy. [0012] teaches rank treatment outcomes based on a specific patient physical, biological or clinical profile and diagnosis. [Table 1] teaches Clinical Experience Values. [0132] teaches the treatment rank contains the name of the therapy, next in the same row the average response rate to this drug in patients treated before with the same molecular alteration, next it can be the average response rate of all patients treated before with alterations in the same gene, with the same target, the same tumor type or any other parameter. Next, it can be the evidence level, which support the driver, target targeted by the drug (interpreted by examiner as peer review feedback scores pertaining to one or more treatment rationale and one or more peer-review scored treatment rationales)); and(h) administering the treatment protocol to the second subject, wherein the treatment protocol comprises chemotherapy, radiation therapy, surgery, targeted therapy, hormone therapy, stem cell transplant, or immunotherapy (Fig. 8C teaches patients who received targeted therapy (interpreted by examiner as administering the treatment protocol to the second subject)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the clinical care platform of Barnes to incorporate the score pertaining to treatment rationales as taught by Peták, with the motivation of enables the users to interpret the complex clinical and molecular diagnostic findings themselves, make their own decisions based on the evidence they consider important and on the clinical experience of others, and contribute to the community by sharing their clinical experience instantly in real time, within seconds. (Peták at [0013]).
REGARDING CLAIM 53
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein the clinical outcomes in (d) comprise positive clinical outcomes, negative clinical outcomes, and neutral clinical outcomes (Barnes at [0156] teaches for an automated similar patient search, the search engine uses the search query patterns of medical personnel for a specific cancer type to automate a search query for patients with that specific cancer type based on specific clinical characteristics that include outcomes. Medical personnel have the interactive ability to search on very specific clinical attributes such as, age, gender, clinical stage, biomarkers, histology, previous treatments, genomic alterations, and outcomes (interpreted by examiner as outcomes comprising positive clinical outcomes, negative clinical outcomes, and neutral clinical outcomes)).
REGARDING CLAIM 54
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein the plurality of peer-reviewed treatment rationales in (d) comprises rationales for expert clinicians to select a drug for treatment of the cancer (Barnes at [0173] teaches the interactive recommendation form 64 provides a structure to document treatments (e.g., surgery, radiotherapy, chemotherapy or drug treatment. [0280] teaches the matching therapies may contain information for (FDA) approved drugs, clinical trials, and off-label drugs (interpreted by examiner as rationales for expert clinicians to select a drug for treatment of the cancer). [0298] teaches FDA approved drugs for each of the various genetic alterations that exist for each specific patient can also be displayed. ).
REGARDING CLAIM 55
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein the plurality of peer-reviewed treatment rationales in (d) comprises rationales for expert clinicians to not select a drug for treatment of the cancer (Barnes at [0278] tags assigned to FDA approved drug to indicate the patient's response to the drug (interpreted by examiner as means for peer-reviewed treatment rationales to comprise rationales expert clinician to not select a drug treatment for the cancer)).
REGARDING CLAIM 56
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein the plurality of peer-reviewed treatment rationales and the associated clinical outcomes are weighted (Barnes at [0095] teaches from the automated query, the search engine displays patient profiles with similar clinical characteristics, treatments and outcomes. The list of patients with similar clinical characteristics provides a resource for medical personnel to quickly review how other similar patients have responded to prescribed treatments to better understand how a specific patient, with similar clinical attributes, might possibly respond to a particular treatment plan (interpreted by examiner as means to weigh treatment rationales and the associated clinical outcomes)).
REGARDING CLAIM 68
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, further comprising collecting outcome data of the second subject responsive to receiving the treatment protocol, and updating the knowledge base using the collected outcome data ([0078] teaches display patient profiles of patients with similar clinical characteristics, treatments and outcomes. [0079] teaches providing medical personnel the ability to document patient contextual information, the relevant lab reports and clinical tests results that are presented along with structured tumor board recommendations. [0106] teaches the server of the system can obtain data and information that can be stored in the database of the server and can then index and store the retrieved information from the EMR system 20 and the information systems 22 in a database that can be accessed by the informatics platform. [0109] teaches the server can extract data from the EMR and the information systems and provide the data to database and an informatics platform to integrate the extracted data and information from the EMR system, the information systems, the third party applications and the third party data sources stored in the database and provide the corresponding tools, interfaces and functionality to permit users of the client devices to retrieve and use the information in the database (interpreted by examiner as collecting outcome data of the second subject responsive to receiving the treatment protocol, and updating the knowledge base using the collected outcome data)).
REGARDING CLAIM 69
Claim 69 is analogous to Claim 53 thus Claim 69 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 53.
REGARDING CLAIM 70
Barnes disclose the limitation of claim 68.
Barnes does not explicitly teach training a machine learning algorithm using the updated knowledge base comprising a training data set, wherein the training data set comprises a plurality of input features and the collected outcome data, however Peták further discloses:
The method of claim 68, further comprising training a machine learning algorithm using the updated knowledge base comprising a training data set, wherein the training data set comprises a plurality of input features and the collected outcome data (Peták at [0041] teaches the database is adaptive, and can be populated with sufficient direct real-life experience (interpreted by examiner as the input features) with responses to treatments (interpreted by examiner as the collected outcome data), the adaptive database may rank the most likely effective therapy. [0183] teaches the present invention directly links databases of users and instantly update treatment ranking as data of a new patient is entered. In other words, the present system is an “automated self learning” system. This system can also combine evidence-based and experience-based decision support (interpreted by examiner as training a machine learning algorithm using the updated knowledge base comprising a training data set)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the platform for clinical care of Barnes to incorporate training machine learning as taught by Peták, with the motivation of avoiding the repetition of a non-effective treatment for the next patient with the same medical condition if the system is used to choose treatment, and helping to choose the effective treatment if such experience is already available (Peták at [0010]).
REGARDING CLAIM 71
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein (a) further comprises:(i) presenting a plurality of selectable clinical case templates, wherein each of the plurality of selectable clinical case templates comprises a plurality of adaptive clinical case parameters, wherein each of the plurality of adaptive clinical case parameters comprises one or more selectable values (Barnes at [0111] teaches that the informatics platform can provide interfaces, utilities and tools to permit users, e.g., medical personnel, to retrieve and visualize the data and information in the database and teaches a tool that can generate a visual timeline of patient events (interpreted by examiner as the selectable clinical case templates) that can be filtered by department, procedure or other similar filtering parameter (interpreted by examiner as a plurality of adaptive clinical case parameters, wherein each of the plurality of adaptive clinical case parameters comprises one or more selectable values) to obtain only the patient events of interest. [0111] also teaches a search engine that can provide for automated and manual search queries based on specific, clinical attributes that provides search results of similar populations of patients with matching clinical attributes (also interpreted by examiner as a plurality of adaptive clinical case parameters) to easily visualize previous treatments/outcomes for similar patients [0068] teaches an interface with filters to further update and refine query results. [0102] teaches medical personnel may launch the application, initiate the querying of clinical information from the informatics platform database and execute the application to perform the desired actions with the pre-specified clinical data (interpreted by examiner as the selectable clinical case template)), (ii) receiving user input pertaining to a selectable clinical case template, an adaptive clinical case parameter, and a selectable value to capture a clinical case of the first subject (Barnes at [0110] teaches an input interface (interpreted by examiner as software module for receiving user input pertaining to a selectable clinical case template, an adaptive clinical case parameter, and a selectable value to capture a clinical case of the first subject as explained above)), and (iii) generating the clinical summary for the first subject based at least in part on the captured clinical case ([0012] teaches tools of the informatics platform [0113] teaches a tool that comprises a summary section and [0114] teaches the summary section can also include information on biomarkers and treatment plans (interpreted by examiner as generating the clinical summary for the first subject based at least in part on the captured clinical case)).
REGARDING CLAIM 72
Barnes and Peták disclose the limitation of claim 71.
Barnes further discloses:
The method of claim 71, wherein capturing the clinical case further comprises converting the user input from a Controlled Natural Language (CNL) into a formal logic (Barnes at [0193] teaches the informatics platform can use natural language processing (NLP) (interpreted by examiner as controlled natural language) to extract patient related information and store the information in the corresponding patient health domain in the database and map the extracted information to specific data fields in the database and display information. [0109] teaches server logic controlling operation of the server and [0258] teaches that the method may be stored on a computer-readable medium and may comprise logical instructions that are executed by a processor to perform operations comprising retrieving aggregated and comprehensive electronic clinical data of a patient and displaying an interactive workspace on a display interface that provides patient specific information (interpreted by examiner as converting user input into a formal logic)).
REGARDING CLAIM 75
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein (c) further comprises converting the plurality of peer-reviewed treatment rationales from a Controlled Natural Language (CNL) into a formal logic (Barnes at [0109] teaches that the server also includes a natural language processing (NLP) engine to extract data from the EMR and the information systems and provide the data (interpreted by examiner as to contain the plurality of peer-reviewed treatment rationales) to database and an informatics platform to integrate the extracted data and information stored in the database and provide the corresponding tools, interfaces and functionality to permit users of the client devices to retrieve and use the information in the database. And teaches server logic controlling operation of the server and [0258] teaches that the method may be stored on a computer-readable medium and may comprise logical instructions that are executed by a processor to perform operations comprising retrieving aggregated and comprehensive electronic clinical data of a patient and displaying an interactive workspace on a display interface that provides patient specific information (interpreted by examiner as converting the plurality of peer-reviewed treatment rationales from a Controlled Natural Language (CNL) into a formal logic)).
REGARDING CLAIM 76
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein (f) further comprises receiving user input to select a cohort and a treatment hypothesis (Barnes at [0095] teaches performing an automated similar patient search based on search query patterns of medical personnel for a specific diagnosis, e.g., cancer type. The search engine is able to automate a search query for patients with that specific diagnosis (cancer type) based on specific, but editable, clinical characteristics. The list of patients with similar clinical characteristics provides a resource for medical personnel to quickly review how other similar patients have responded to prescribed treatments to better understand how a specific patient, with similar clinical attributes, might possibly respond to a particular treatment plan (interpreted by examiner as identify cohort and treatment hypothesis) [0160] teaches that in another embodiment, the similar patient search can be a manual one where the user has to manually input specific clinical attributes of interest and initiate the query with the search engine).
REGARDING CLAIM 77
Barnes and Peták disclose the limitation of claim 76.
Barnes further discloses:
The method of claim 76, wherein the cohort is selected based at least in part on one or more of: data source, age, gender, a biomarker, a genetic variant, a tumor type, a cancer stage, a tumor location, a lymph node status, a treatment, a treatment order, a desired evidence threshold, and survival (Barnes at [0095] teaches the search engine is able to automate a search query for patients with that specific diagnosis (cancer type) based on specific, but editable clinical characteristics that may include, but are not limited to, age, gender, biomarkers, staging information (interpreted by examiner as the cohort is selected based at least in part on one or more of: data source, age, gender, at least one biomarker, cancer stage) From the automated query, the search engine displays patient profiles with similar clinical characteristics, treatments and outcomes).
REGARDING CLAIM 80
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein the plurality of peer-reviewed treatment rationales and associated clinical outcomes are mined or crowd-sourced from peer-reviewed literature, conferences, medical data, a physician for the clinical case, an expert in a disease profile of the clinical case, or any combination thereof (Barnes at [0235] teaches that many hospitals and healthcare professionals have focused on increasing multi-disciplinary collaboration in treatment of cancer by convening Multidisciplinary Cancer Conferences (MCCs) also known as Tumor Boards (interpreted by examiner as crowd-sourced from conferences). These conferences are regularly scheduled meetings where each individual patient case is reviewed by a team comprised of medical oncologists, radiation oncologist, surgeons/surgical oncologist, pathologist, radiologists, nurses, and social workers. The primary goal is to ensure that all appropriate tests, treatment options, and recommendations are considered for each patient).
REGARDING CLAIM 81
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein the characteristic of the second subject comprises one or more of: predicted life span, age, gender, one or more previously administered treatments, or one or more presently administered treatments, or any combination thereof (Barnes at [0079] teaches relevant patient information (e.g., age, gender)).
REGARDING CLAIM 82
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein the decision model selects the machine learning model based at least in part on a cancer type, cancer stage, site of origin of the cancer, site of metastasis of the cancer, one or more morphological features of the cancer, tissue type of the cancer, or one or more cancer markers, or any combination thereof (Barnes at [0226] teaches selecting the model that is of interest and the system auto populates relevant clinical values such as Type of Cancer and [0232] teaches that the “Biostatistical Analysis” tool would launch in the workspace of the application. Researcher/clinician would select patient age, gender, type of cancer, specific biomarkers, treatments and any other clinical variable captured within system that would be of interest for research or patient treatment (interpreted by examiner as wherein the decision model selects the machine learning model based at least in part on a cancer type)).
REGARDING CLAIM 84
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, wherein the decision model selects the machine learning model from the plurality of machine learning models based at least in part on determining a predictive ability of the selected machine learning model for the second subject (Barnes at [0218] teaches predictive models and [0224] teaches the biostatistical analytic tool provides automated and semi-automated queries of the curated dataset and leverages biostatistical models to generate analytical graphs that relate to predictive, potential outcome, latency, remission for a patient or pools of patients (interpreted by examiner as wherein the decision model selects the machine learning model from the plurality of machine learning models based at least in part on determining a predictive ability of the selected machine learning model for the second subject)).
REGARDING CLAIM 85
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 84, wherein the predictive ability of the selected machine learning model is determined by the decision model based at least in part on an accuracy, specificity, sensitivity, positive predictive value, negative predictive value, or any combination thereof, of at least a portion of the plurality of generated machine learning models (Barnes at [0226] teaches focusing on a specific patient and having access to a number of open-source and commercially available biostatistic analytical models where the clinician the selects the model that is of interest (interpreted by examiner as wherein the predictive ability of the selected machine learning model is determined by the decision model based at least in part on specificity)).
REGARDING CLAIM 86
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 84, wherein the decision model evaluates the predictive ability of the at least portion of the plurality of generated machine learning models using a data set of one or more independent cases (Barnes at [0226] teaches researchers that are more focused on pools of patient types would manually input clinical values that relate to the population of patients that they are interested in further interrogating the data and [0230] teaches initiate the search for pools of similar patients (interpreted by examiner as wherein the decision model evaluates the predictive ability of the at least portion of the plurality of generated machine learning models using a data set of one or more independent cases)).
REGARDING CLAIM 87
Barnes and Peták disclose the limitation of claim 52.
Barnes further discloses:
The method of claim 52, further comprising determining by the decision model a risk assessment weight and a benefit assessment weight for at least a portion of the plurality of generated machine learning models (Barnes at [0052] teaches assessing a clinical impact of a variant. [0218] teaches biostatistical models & nomograms may include, for example, a risk ratio, an absolute risk reduction, [0212] teaches clinicians would benefit from the ability to interrogate a data set of similar patients (i.e., patients with a similar condition or disease state, medical history, family history, or other characteristics) who have been treated in the past (but were not a part of a clinical trial or other organized study) in order to determine to the extent possible how the treatment of those patients affected their outcomes (either positively or negatively) over time (interpreted by examiner as determining by the decision model a risk assessment weight and a benefit assessment weight for at least a portion of the plurality of generated machine learning models)).
REGARDING CLAIM 88
Claim 88 is analogous to Claim 87 thus Claim 88 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 87.
Claims 58-60 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes (US 2017/0076046), in view of Peták (US 2016/0224760) and in further view of Francois (US 2016/0110523).
REGARDING CLAIM 58
Barnes and Peták disclose the limitation of claim 52.
Barnes and Peták do not explicitly teach, however Francois further discloses:
The method of claim 57, wherein the cancer is selected from the group consisting of breast cancer, ovarian cancer, uterine cancer, cervical cancer, prostate cancer, pancreatic cancer, bladder cancer, acute myeloid leukemia (AML), acute lymphocytic leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, hairy cell leukemia, myelodysplasia, myeloproliferative disorder, acute myelogenous leukemia (AML), chronic myelogenous leukemia (CML), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), myelodysplastic syndrome (MDS), bone cancer, lung cancer, adenocarcinoma, basal cell carcinoma, melanoma, squamous cell carcinoma, liver cancer, kidney cancer, lymphoma, Kaposi's Sarcoma, cervical cancer, astrocytoma, glioblastoma, schwannoma, medulloblastoma, neurofibroma, mesothelioma, oropharyngeal cancer, colorectal cancer, testicular cancer, thymoma, thymic carcinoma, Hodgkin disease, and non-Hodgkin lymphoma (Francois at [0347] teaches a disease is cancer. In some embodiments a cancer is a carcinoma. In some embodiments a cancer is a sarcoma. In some embodiments a cancer is a cancer of the adrenal gland, biliary tract, bladder, bone, breast, brain, cervix, colon, endometrium, esophagus, head or neck, kidney, liver, lung, oral cavity, ovary, pancreas, prostate, rectum, skin, testis, thyroid, or uterus. In some embodiments a cancer is a hematologic cancer, e.g., a leukemia, lymphoma, multiple myeloma, or myeloproliferative neoplasm. In some embodiments a cancer is metastatic. (interpreted by examiner as any of the caner types mentioned above)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the platform for integrated clinical care of Barnes and the score pertaining to treatment rationales of Peták to incorporate the different types of cancers as taught by Francois, with the motivation of streamlined healthcare delivery process, which may significantly reduce overhead, improved quality and/or improved patient outcomes (e.g., reduced readmissions for the same disease episode), increased validity of performance-based reimbursement and improved ability to comply with accreditation standards, laws, and/or regulatory requirements, guidelines, or mandates (Francois at [0360]).
REGARDING CLAIM 59
Barnes and Peták disclose the limitation of claim 58.
Barnes and Peták do not explicitly teach, however Francois further discloses:
The method of claim 58, wherein the cancer is glioblastoma (Francois at [0270] teaches treatment of glioblastoma).
REGARDING CLAIM 60
Barnes and Peták disclose the limitation of claim 57.
Barnes and Peták do not explicitly teach, however Francois further discloses:
The method of claim 52, wherein the treatment protocol comprises chemotherapy, radiation therapy, targeted therapy, immunotherapy, hormone therapy, surgery, stem cell transplant, or a combination thereof (Francois at [0261] teaches radiation therapy).
Claims 61 and 62 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes (US 2017/0076046), in view of Peták (US 2016/0224760) and in further view of De Bruin (US 2011/0119212).
REGARDING CLAIM 61
Barnes and Peták disclose the limitation of claim 52.
Barnes and Peták do not explicitly teach, however De Bruin further discloses:
The method of claim 52, further comprising prioritizing a set of ranked treatment protocols based at least in part on predicted outcomes (De Bruin at [abstract] teaches system to predict a patient's response to a variety of treatments and [0002] teaches the present invention relates to the field of medical treatments and, more specifically, to predicting treatment efficacy and determining optimal treatment for any illness, disease or abnormality. [0077] teaches evidence to confirm the diagnosis, to estimate a number of diagnostic possibilities and to rank order a number of treatment options that might be reasonably considered to treat an illness or condition (interpreted by examiner as prioritizing a set of ranked treatment protocols based at least in part on predicted outcomes)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the platform for integrated clinical care of Barnes and the score pertaining to treatment rationales of Peták to incorporate prioritizing a set of ranked treatment protocols as taught by De Bruin, with the motivation to assist the physician/clinician to make diagnosis and treatment decisions with greater accuracy and efficiency. (De Bruin at [0006]).
REGARDING CLAIM 62
Barnes Peták and De Bruin disclose the limitation of claim 61.
Barnes and De Bruin do not explicitly teach, however Peták further discloses:
The method of claim 61, wherein the prioritizing comprises applying a machine learning algorithm utilizing the knowledge base (Peták at [0007] teaches therapy ranking in a learning database [0008] teaches assigning ranks to treatment options (interpreted by examiner as treatment protocols of De Bruin) based on their expected efficacy and side effects and clinical experience (interpreted by examiner as based on predicted outcomes). [0012] teaches a medical experience register (interpreted by examiner as the knowledge base) storing medical experience data from a plurality of users, the clinical evidence register storing result data, the result data including clinical profiles of respective prior patients, diagnoses of the respective prior patients, treatments administered to the respective prior patients and outcomes of the treatments administered to the respective prior patients; a processor connected to the medical experience register, the processor enabled to rank treatment outcomes. [0159] teaches a therapy ranking algorithms (interpreted by examiner as the machine learning algorithm) to help in the selection of trials (also interpreted by examiner as treatment protocols of De Bruin) that test compounds that are most likely effective in the given patient).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the platform for clinical care of Barnes and prioritizing a set of ranked treatment protocols as taught by De Bruin to incorporate machine learning algorithms as taught by Peták, with the motivation of avoiding the repetition of a non-effective treatment for the next patient with the same medical condition if the system is used to choose treatment, and helping to choose the effective treatment if such experience is already available (Peták at [0010]).
Claims 63-67 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes (US 2017/0076046), in view of Peták (US 2016/0224760), in view of De Bruin (US 2011/0119212) and in further view of Bennett (US 2015/0019241).
REGARDING CLAIM 63
Barnes, Peták and De Bruin disclose the limitation of claim 61.
Barnes, Peták and De Bruin do not explicitly teach, however Bennett further discloses:
The method of claim 61, wherein the prioritizing comprises conducting a Bayesian decision process (Bennett at [0010] teaches decision support for assisting medical treatment decision-making, and determining optimal treatment by evaluating the plurality of decision-outcome nodes with a cost per unit change function and outputting the optimal treatment. [0031] teaches a methods to perform efficient Bayesian inference. [0070] teaches decision making process for patient treatment and uses a dynamic decision network (DDN, a type of dynamic Bayesian network) and [0078] teaches using Bayes rule to derive relationship (interpreted by examiner as prioritizing, of De Bruin, comprises conducting a Bayesian decision process)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the platform for clinical care of Barnes, the score pertaining to treatment rationales of Peták and prioritizing a set of ranked treatment protocols as taught by De Bruin to incorporate Bayesian decision process as taught by Bennett, with the motivation of improving decision-making and the fundamental understanding of the healthcare system and clinical process--its elements, their interactions, and the end result--by playing out numerous potential scenarios in advance (Bennett at [0007]).
REGARDING CLAIM 64
Barnes, Peták, De Bruin and Bennett disclose the limitation of claim 63.
Barnes, Peták and De Bruin do not explicitly teach, however Bennett further discloses:
The method of claim 63, wherein the Bayesian decision process utilizes a Bayesian network or a hill climbing algorithm (Bennett at [0070] teaches decision is modeled as a dynamic decision network (DDN, a type of dynamic Bayesian network) (interpreted by examiner as utilizing a Bayesian network)).
REGARDING CLAIM 65
Barnes, Peták, De Bruin and Bennett disclose the limitation of claim 63.
Barnes, Peták, and Bennett do not explicitly teach, however De Bruin further discloses:
The method of claim 63, wherein the Bayesian decision process is based at least in part on (1) an efficacy of the set of ranked treatment protocols, or (2) a variance or uncertainty of an efficacy of an equipoise set of the one or more ranked treatment protocols (De Bruin at [0002] teaches the present invention relates to the field of medical treatments and, more specifically, to predicting treatment efficacy and determining optimal treatment for any illness, disease or abnormality. [0077] teaches evidence to confirm the diagnosis, to estimate a number of diagnostic possibilities and to rank order a number of treatment options that might be reasonably considered to treat an illness or condition (interpreted by examiner as the one or more ranked treatment protocols) [0035] teaches using a Bayesian learning or decision technique and [0071] teaches using Bayesian modeling and analyzes (interpreted by examiner as the Bayesian decision process). [0088] teaches treatment-efficacy prediction/estimation models and [0135] teaches that the objective is to construct classification, regression and treatment-efficacy prediction models by solving an optimization problem (interpreted by examiner as wherein the Bayesian decision process is based at least in part on (1) an efficacy of the set of ranked treatment protocols)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the platform for integrated clinical care of Barnes, the score pertaining to treatment rationales of Peták and the Bayesian decision process of Bennet to incorporate the Bayesian decision process as taught by De Bruin, with the motivation to assist the physician/clinician to make diagnosis and treatment decisions with greater accuracy and efficiency. (De Bruin at [0006]).
REGARDING CLAIM 66
Barnes, Peták, De Bruin and Bennett disclose the limitation of claim 65.
Barnes, Peták, and Bennett do not explicitly teach, however De Bruin further discloses:
The method of claim 65, wherein the Bayesian decision process is based at least in part on (1) the efficacy of the set of ranked treatment protocols, and (2) the variance or uncertainty of the efficacy of the equipoise set of the one or more ranked treatment protocols (De Bruin at [0077] teaches reviewing various available information including neuro-psycho-biological, clinical, laboratory, physical, and pharmacogenetic data and information and evidence to confirm the diagnosis, to rank order, by likelihood of response, a number of treatment options (interpreted by examiner as wherein the Bayesian decision process is based at least in part on the variance or uncertainty of the efficacy of the equipoise set of the one or more ranked treatment protocols)).
REGARDING CLAIM 67
Barnes, Peták, De Bruin and Bennett disclose the limitation of claim 65.
Barnes, Peták, and De Bruin do not explicitly teach, however Bennett further discloses:
The method of claim 65, wherein the Bayesian decision process prioritizes the equipoise set based at least in part on relative amount of information gained from the predicted outcomes across the equipoise set (Bennett at [0010] teaches decision support for assisting medical treatment decision-making, and determining optimal treatment by evaluating the plurality of decision-outcome nodes with a cost per unit change function and outputting the optimal treatment. [0031] teaches a methods to perform efficient Bayesian inference. [0070] teaches decision making process for patient treatment and uses a dynamic decision network (DDN, a type of dynamic Bayesian network) and [0078] teaches using Bayes rule to derive relationship (interpreted by examiner as the Bayesian decision process prioritizes the equipoise set based at least in part on relative amount of information gained from the predicted outcomes across the equipoise set)).
Claims 73 and 78 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes (US 2017/0076046), in view of Peták (US 2016/0224760) and in further view of Maitra (US 2016/0048655).
REGARDING CLAIM 73
Barnes and Peták disclose the limitation of claim 71.
Barnes and Peták do not explicitly disclose, however Maitra further discloses:
The method of claim 71, wherein capturing the clinical case further comprises using parameters and values selected from Controlled Natural Language (CNL), Biomedical Controlled English (BCE), or a combination thereof (Maitra at [0058] teaches integrating a Unified Medical Language System (UMLS) semantic network which provides a concise compilation of controlled vocabularies for use in the biomedical sciences (interpreted by examiner as parameters and values selected from Biomedical Controlled English (BCE))).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the platform for clinical care of Barnes and the score pertaining to treatment rationales of Peták to incorporate the method of using Biomedical Controlled English (BCE) as taught by Maitra, with the motivation of processing large amounts of medical data quickly and efficiently to identify complex relationships between a particular drug or treatment regimen and the effects experienced by the user (Maitra at [0011]).
REGARDING CLAIM 78
Barnes and Peták disclose the limitation of claim 52.
Barnes and Peták do not explicitly disclose, however Maitra further discloses:
The method of claim 52, wherein (e) further comprises using the plurality of peer-reviewed treatment rationales to generate one or more inferential chains that comprise at least one treatment hypothesis (Maitra at [0033] teaches process documents to extract information and further analyze the extracted information to make medical determinations (interpreted by examiner as the treatment hypothesis of Barnes) and using the extracted information and the inferential determinations that are made to automatically generate reports (interpreted by examiner as the summaries of Barnes). [0112] teaches different rule chains that the rules engine may employ in performing various inferential tasks (interpreted by examiner as model to generate one or more inferential chain)).
Claim 74 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes (US 2017/0076046), in view of Peták (US 2016/0224760) and in further view of Tan (US 2018/0206778).
REGARDING CLAIM 74
Barnes and Peták disclose the limitation of claim 52.
Barnes and Peták do not explicitly disclose, however Tan further discloses:
The method of claim 52, wherein (c) further comprises conducting an adaptive Delphi survey process (Tan at [0027] teaches survey based on Delphi method using a database of subjects (interpreted by examiner as conducting an adaptive Delphi survey process)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the platform for clinical care of Barnes and the score pertaining to treatment rationales of Peták to incorporate Delphi process as taught by Tan, with the motivation of assessing the risk of a condition in patients in need thereof and initiating early treatment to prevent or minimize the condition (Tan at [0007]).
Claim 83 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes (US 2017/0076046), in view of Peták (US 2016/0224760) and in further view of Bennett (US 2015/0019241).
REGARDING CLAIM 83
Barnes and Peták disclose the limitation of claim 52.
Barnes and Peták do not explicitly teach wherein the plurality of generated machine learning models comprises one or more of: supervised machine learning models, unsupervised machine learning models, support vector machine (SVM) models, Naive Bayes classification models, random forest models, artificial neural network models, decision tree models, K-means models, learning vector quantization (LVQ) models, self-organizing map (SOM) models, graphical models, regression algorithm models, penalized logistic regression models, prediction analysis of microarrays (PAM) models, shrunken centroids models, or regularized linear discriminant analysis models, or any combination thereof, however Bennet further discloses:
The method of claim 52, wherein the plurality of generated machine learning models comprises one or more of: supervised machine learning models, unsupervised machine learning models, support vector machine (SVM) models, Naive Bayes classification models, random forest models, artificial neural network models, decision tree models, K-means models, learning vector quantization (LVQ) models, self-organizing map (SOM) models, graphical models, regression algorithm models, penalized logistic regression models, prediction analysis of microarrays (PAM) models, shrunken centroids models, or regularized linear discriminant analysis models, or any combination thereof (Bennet at [0029] teaches decision tree model. ).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the platform for clinical care of Barnes and the score pertaining to treatment rationales of Peták to incorporate decision tree model as taught by Bennett, with the motivation of improving decision-making and the fundamental understanding of the healthcare system and clinical process--its elements, their interactions, and the end result--by playing out numerous potential scenarios in advance (Bennett at [0007]).
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 52-80, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
Claim 52 is patent-eligible, for at least the reason that they encompass effecting a particular treatment-namely the treatment protocol comprising chemotherapy, radiation therapy, surgery, targeted therapy, hormone therapy, stem cell transplant, or immunotherapy - administered to treat a particular disease: cancer. Applicant respectfully submits that claim 52, which recites "administering the treatment protocol to the second subject, wherein the treatment protocol comprises chemotherapy, radiation therapy, surgery, targeted therapy, hormone therapy, stem cell transplant, or immunotherapy", encompasses a particular treatment for a particular disease and thus is patent- eligible.
Regarding 1, The Examiner respectfully disagrees. The claim does not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or a medical condition (see the Vanda memo). MPEP 2106.04(d)(2) indicates that a practical application may be present where the abstract idea effects a particular treatment or provides particular prophylaxis for a disease or medical condition. A particular treatment/prophylaxis is present where: (a) there is a particular (i.e., named/described) treatment/prophylaxis that occurs when the claim is implemented; (b) the treatment/prophylaxis has more than a nominal connection/correlation to the abstract idea; and (c) the administration is more than extra-solution activity or a field of use.
Applicant’s claimed invention does not provide for a particular treatment/prophylaxis because no treatment/prophylaxis is recited in the claim or because, while the claim recite that a treatment/prophylaxis is provided/administered to the patient, there is no particularity to the treatment; the claim does not state what the actual treatment/prophylaxis is, how often it is applied, the amount/concentration of treatment, the length of treatment, etc. Because a particular treatment/prophylaxis is not present in the claims, a practical application is not present.
Rejection under 35 U.S.C. § 102
Regarding the rejection of claims 52-80, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
Barnes is silent on selecting a machine learning model from a plurality of generated machine learning models, much less querying a knowledge base using the selected machine learning model, much less selecting a second subject to receive a treatment protocol using the selected machine learning model, much less administering a treatment protocol to the selected second subject. Barnes merely describes "using a machine learning system trained based on at least one of past concordance checks and past resolutions of discordance" (Barnes at claim 12). Petik does not cure the deficiencies of Barnes with respect to claim 52. Petik does not teach, disclose, or suggest at least selecting a machine learning model from a plurality of generated machine learning models, much less using a decision model to select the machine learning model, much less selecting the machine learning model based on the disease or a characteristic of the second subject, much less querying a knowledge base using the selected machine learning model, much less selecting a second subject to receive a treatment protocol using the selected machine learning model, much less administering a treatment protocol to the selected second subject. Petik merely describes an "adaptive database model" for filling in a database (Petik at paragraph [0041])… Barnes does not teach, disclose, or suggest all elements of claim 52, much less the combination of elements of claims 58-60… Each of claims 61 and 62 depends upon claim 52 and recites additional elements. As discussed, Barnes and Petik, alone or in combination do not teach, disclose, or suggest all elements of claim 52, much less the combination of elements of claims 61 and 62... As discussed, Barnes, Petik, and De Bruin alone or in combination do not teach, disclose, or suggest all elements of claim 52, much less the combination of elements of claims 63-67… Each of claims 73 and 78 depends upon claim 52 and recites additional elements. As discussed, Barnes and Petik, alone or in combination do not teach, disclose, or suggest all elements of claim 52, much less the combination of elements of claims 73 and 78… Claim 74 depends upon claim 52 and recites additional elements. As discussed, Barnes and Petik, alone or in combination do not teach, disclose, or suggest all elements of claim 52, much less the combination of elements of claim 74.
Regarding 1, The Examiner respectfully disagrees. submits that Barnes and Petik teaches all limitations of claim 52. Please refer to the new rejection under 35 U.S.C. § 103. And in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Given the broadest reasonable interpretation, the cited references teach the argued feature(s).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 7:30am-5:30pm ET.
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/L.T.K./Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683