DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/17/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/04/2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Amendments
Claims 1, 3-7, 9-11, and 13-23 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Amendment to the Claims and Remarks filed on 03/17/2026.
Claims 1, 11, and 22 are amended claims.
Claims 4-7, 10, 13, 15-19, and 21 are original claims.
Claims 3, 9, 14, 20, and 23 are previously presented.
Claims 2, 8, and 12 have been cancelled and will not be considered at this time.
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, 3-7, 9-11, and 13-23 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1, 11, and 22 are drawn to a method, a system, and an article of manufacture, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method for obtaining data that comprises treatment history and clinical data of a cohort of patients; receiving a user-defined parameter for each of: (i) one or more qualifying events;(ii) one or more responses states to one or more of the one or more qualifying events; and (iii) one or more collapsible events, wherein the user-defined parameter for the one or more collapsible events comprises an indication that an order of two or more consecutive events is immaterial such that the two or more consecutive events are collapsible into one combined event for simplifying an individual treatment pathway; generating individual treatment pathways for individual patients of the cohort of patients using the treatment history and clinical data for the individual patients; wherein the individual treatment pathways are generated using user-defined parameters comprising: one or more qualifying events, comprising one or more treatment regimens; one or more response states to the one or more qualifying events; and one or more collapsible events each comprising a collapse of two or more similar qualifying events or two more similar response states, wherein an order of the two or more similar qualifying events or an order of the two or more similar response states is immaterial and are collapsed into a single collapsible event; constructing a state transition graph using the individual treatment pathways, the state transition graph comprising multiple aligned and merged individual treatment pathways comprising the one or more qualifying events, the one or more response states to the one or more qualifying events, and the one or more collapsible events, wherein the state transition graph comprises a plurality of subgraphs of the individual treatment pathways comprising one or more qualifying events and one or more response states to the one or more qualifying events; receiving a user selection of at least one of the plurality of sub graphs; and displaying, in response to receiving the user selection, the selected at least one of the plurality of sub graphs.
Independent claim 11 recites a system for obtaining data that comprises treatment history and clinical data of a cohort of patients; receive a user-defined parameter for each of: (i) one or more qualifying events;(ii) one or more responses states to one or more of the one or more qualifying events; and (iii) one or more collapsible events, wherein the user-defined parameter for the one or more collapsible events comprises an indication that an order of two or more consecutive events is immaterial such that the two or more consecutive events are collapsible into one combined event for simplifying an individual treatment pathway; generate individual treatment pathways for individual patients of the cohort of patients using the treatment history and clinical data for the individual patients using user- defined parameters comprising: one or more qualifying events, comprising one or more treatment regimens; one or more response states to the one or more qualifying events; and one or more collapsible events each comprising a collapse of two or more similar qualifying events or two more similar response states, wherein an order of the two or more similar qualifying events or an order of the two or more similar response states is immaterial and are collapsed into a single collapsible event; construct a state transition graph using the individual treatment pathways, the state transition graph comprising multiple aligned and merged individual treatment pathways comprising the one or more qualifying events, the one or more response states to the one or more qualifying events and the one or more collapsible events, wherein the state transition graph comprises a plurality of subgraphs of the individual treatment pathways comprising one or more qualifying events and one or more response states to the one or more qualifying events; receive a user selection of at least one of the plurality of subgraphs; and display, in response to receiving the user selection, the selected at least one of the plurality of subgraphs.
Independent claim 22 recites a non-transitory machine-readable medium for obtaining data that comprises treatment history and clinical data of a cohort of patients; receiving a user-defined parameter for each of: (i) one or more qualifying events;(ii) one or more responses states to one or more of the one or more qualifying events; and (iii) one or more collapsible events, wherein the user-defined parameter for the one or more collapsible events comprises an indication that an order of two or more consecutive events is immaterial such that the two or more consecutive events are collapsible into one combined event for simplifying an individual treatment pathway; generating, by the one or more computing devices, individual treatment pathways for individual patients of the cohort of patients using the treatment history and clinical data for the individual patients; wherein the individual treatment pathways are generated using user-defined parameters comprising: one or more qualifying events, comprising one or more treatment regimens; one or more response states to the one or more qualifying events; and one or more collapsible events each comprising a collapse of two or more similar qualifying events or two more similar response states, wherein an order of the two or more similar qualifying events or an order of the two or more similar response states is immaterial and are collapsed into a single collapsible event; constructing a state transition graph using the individual treatment pathways, the state transition graph comprising multiple aligned and merged individual treatment pathways comprising the one or more qualifying events, the one or more response states to the one or more qualifying events and the one or more collapsible events wherein the state transition graph comprises a plurality of subgraphs of the individual treatment pathways comprising one or more qualifying events and one or more response states to the one or more qualifying events; receiving a user selection of at least one of the plurality of subgraphs; and displaying, in response to receiving the user selection, the selected at least one of the plurality of subgraphs.
These steps amount to methods of organizing human activity which includes
functions relating to interpersonal and intrapersonal activities, such as managing
relationships or transactions between people, social activities, and human behavior;
satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or
behaviors; and managing human activity (MPEP § 2106.04(a)(2)(II)(C) citing the
abstract idea grouping for methods of organizing human activity for managing personal
behavior or relationships or interactions between people – also note October 2019
Update: Subject Matter Eligibility on p. 5 and MPEP § 2106.04(a)(2)(II) stating certain
activity between a person and a computer may fall within the “certain methods of
organizing human activity” grouping).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1, 11, and 22 recite one or more computing devices. Claims 11 and 22 recite a processor. Claim 11 recites a memory. Claim 22 recites a non-transitory machine-readable medium.
These elements are recited at a high-level of generality such that it amounts to mere instructions to apply the exception because this is an example of applying the abstract idea by use of general-purpose computer which does not integrate the abstract idea into a practical application.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 1, 11, and 22 recite one or more computing devices. Claims 11 and 22 recite a processor. Claim 11 recites a memory. Claim 22 recites a non-transitory machine-readable medium.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
For the reasons stated, these claims are consequently rejected under 35 U.S.C. § 101.
Analysis of Dependent Claims
Dependent claim 3 and 13 recites the one or more treatment regimens is
selected from the group consisting of a drug regimen, a surgical protocol, a collection of
eligible interventions, or combinations thereof.
Dependent claims 4 and 14 recites the one or more response states is selected from the group consisting of a response status after a treatment; and a subtype of the patient based on a specific gene signature.
Dependent claims 5 and 16 recites the one or more response states is linked to one or more reports selected from the group consisting of a clinical report, a radiology report, a pathology report, a genomics report, or combinations thereof.
Dependent claims 6 and 15 recites the constructing comprises adding individual treatment pathways one at a time to the state transition graph.
Dependent claims 7 and 18 recites the state transition graph comprises edges that correspond to treatments of a similar nature.
Dependent claims 9 and 20 recites wherein receiving a user selection of at least one of the plurality of sub graphs comprises identification of a qualifying event or a response state in the state transition graph, and wherein displaying the selected at least one of the plurality of sub graphs comprises removing, from the display, each of the one or more qualifying events, the one or more response states to the one or more qualifying events, and the one or more collapsible events not traversed by the sub graph.
Dependent claims 10 and 21 recites receiving a new individual pathway to add to the state transition graph; identifying the largest possible matching sequence of state-event-state units between the new individual pathway and the state transition graph as anchor points; and adding the new individual pathway to the state transition graph, wherein the resulting state transition graph remains acyclic and has the least number of additional response states and edges.
Dependent claim 17 recite to link the one or more response states to one or more genomics reports.
Dependent claim 19 recites to collapse edges corresponding to treatments of a similar nature.
Dependent claim 23 recites wherein the received user-defined parameter for the one or more response states to the one or more qualifying events comprises splitting one or more response states into immediate response status, comprising complete response, partial response, and no response.
Each of these steps of the preceding dependent claims 3-7, 9-10, 13-21, and 23 only serve to further limit or specify the features of independent claims 1, 11, and 22 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
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.
Claim(s) 1, 3-7, 11, 13-19, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal (US 20100125462 A1) in view of Hu (US 20180082025 A1) in view of De La Torre (US 20170277857 A1).
As per Claim 1, Aggarwal teaches a computer-implemented method for constructing a state transition graph for treatment, procedure and progression workflows, wherein the method comprises:
obtaining, by one or more computing devices, data that comprises treatment history and clinical data of a cohort of patients; ([Para. 0010] a user of the system interacts with the system using the graphical user interface (GUI). The user provides input parameters (related to a patient) such as patient history (e.g., age, gender, family history, grade of tumor, size of tumor, ethnicity etc.), current state of the patient, treatment administered so far, and the like. [Para. 0026] the user may provide input parameters related to historical information of a patient and one or more treatment protocols that may be used for the treatment of the patient. [Para. 0029] input parameters may include, for example, age of the patient, sex of the patient, ethnicity, type of the disease, stage of the disease, case history, treatments administered, family history, the region where the patient resides, and the like. Examiner interprets the use of a GUI to be indicative of a computing device.])
receiving a user-defined parameter for each of: (i) one or more qualifying events;(ii) one or more responses states to one or more of the one or more qualifying events; and (iii) one or more collapsible events; ([Para. 0034] ARBE 202 receives input parameters related to the patient such as personal details, case history, and treatment protocols (i.e. one or more qualifying events) that have been administered so far. [Para. 0029] Input parameters may include, for example, age of the patient, sex of the patient, ethnicity, type of the disease, stage of the disease, case history (i.e. response states), treatments administered (i.e. qualifying events), family history, the region where the patient resides, and the like. [Para. 0011] The analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort (i.e. collapsible events) represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters.)
generating, by the one or more computing devices, individual treatment pathways for individual patients of the cohort of patients using the treatment history and clinical data for the individual patients; ([Para. 0012] the analytics engine uses a state-transition graph to represent the current state of the patient. Furthermore, the user views this state-transition graph through the GUI. Each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. [Para. 0022] FIG. 5a and FIG. 5b illustrate an exemplary state-transition graph for treatment of breast cancer in accordance with an embodiment of the invention. [Para. 0026] The user may provide input parameters related to historical information of a patient and one or more treatment protocols that may be used for the treatment of the patient. Further, output of the analysis performed by system 100 is displayed to the user via GUI 102. Analytics engine 104 performs statistical and computational analysis based on the input parameters and generates (as well as regularly updates) a state-transition graph representing the different stages of different treatment protocols along with potential outcomes.)
wherein the individual treatment pathways are generated using user-defined parameters comprising:
one or more qualifying events, comprising one or more treatment regimens; ([Para. 0012] each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. Hence, by moving from one vertex to another, the user can view the entire state-transition graph for a particular disease (and the treatment protocols that are included in the system for this disease). [Para. 0046] further teaches analytics engine 104 represents different stages of the treatment protocols in a state-transition graph. [Para. 0023] The terms `treatment regimen`, `treatment course`, and `treatment protocol` have been used interchangeably.)
one or more response states to the one or more qualifying events; ([Para. 0012] Each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. [Para. 0046] paths originating from each vertex of the state-transition graph represent different options or treatments that are available at that stage. Further, each vertex represents a state of the patient at that particular point of time (during the treatment).)
and one or more collapsible events each comprising a collapse of two or more similar qualifying events or two more similar response states, wherein an order of the two or more similar qualifying events or an order of the two or more similar response states are collapsed into a single collapsible event; ([Para. 0011] the analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters. [Para. 0026] The close cohort is computed by using the information related to case histories of patients, clinical research information, market research information, and related information stored in databases 108 and 110. [Para. 0036] Creates close cohorts by combining "near identical cohorts. Examiner interprets the combination of close cohorts to be indicative of collapsible events. Examiner interprets that since the close cohorts are created based on similar patient histories and experiences that would be indicative of two or more qualifying event or response states.)
constructing, by the one or more computing devices, a state transition graph using the individual treatment pathways, the state transition graph comprising multiple aligned and merged individual treatment pathways comprising the one or more qualifying events, the one or more response states to the one or more qualifying events, and the one or more collapsible events, ([Para. 0011] the analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters. [Para. 0036] the analytical rule-based engine (ARBE) creates close cohorts by combining "near identical cohorts. [Para. 0026] analytics engine 104 performs statistical and computational analysis based on the input parameters and generates (as well as regularly updates) a state-transition graph representing the different stages of different treatment protocols along with potential outcomes. The statistical and computational analysis includes computing a close cohort for the patient, and based on this close cohort, analytics engine 104 estimates the likelihood of various output parameters (i.e., potential outcomes) and the statistical significance related to these outcomes. [Para. 0062] The set of output parameters and corresponding costs for the cancer treatment protocols are displayed to the user through GUI 102. The cancer treatment protocols (which include the set of output parameters calculated from the close cohort) and different options are displayed to the user in the form of a state-transition graph.)
Aggarwal does not explicitly disclose, however Hu discloses
wherein the user-defined parameter for the one or more collapsible events comprises an indication that an order of two or more consecutive events is immaterial such that the two or more consecutive events are collapsible into one combined event for simplifying an individual treatment pathway; ([Para.0016 - 0018] A state transition learner, to parse published clinical guidelines and to extract probabilities of transition between a number of states of a medical condition as a state transition model; a clinical finding learner, to extract typical findings of the medical condition from domain knowledge as a finding model and to compute the probability of a particular finding for a particular state and to save the probabilities in an overall episode model including the state transition model and the finding model; and an episode grouper, to use the overall episode model, and the set of medical records to discover a sequence of events, to group the sequence of events into an episode of the medical condition, and to differentiate the medical condition from apparently similar medical conditions or co-morbidities, or both. [Para. 0031] For example, the episode grouper is to match the powerset of the sequence of events in the set of medical records to the overall episode model, with the exception of the empty set and/or any sets including a number of events below a threshold. It is important that the events are kept in the correct order in the powerset, but events in the time line may be omitted (and for instance included later in an episode of another condition). The threshold may be 2 events, so that a “sequence” of a single event is not assessed for a match with an episode. The various subsets of sequences can be assessed in any order, for example in random order or fully/partially in parallel. Examiner interprets the assessment of the subsets of sequences to be indicative of an order of the two or more consecutive events is immaterial. [Para. 0091] Identify the most appropriate sequence of patient's treatments or other findings (documented in his/her medical records) that can be grouped into one episode characterized by E(d)=π.sub.0.sup.d, . . . , π.sub.m.sup.d where dεD is a disease and D is the set of diseases whose progression trajectories are known or can be learnt from existing data. This gives benefit of fully understanding the episode (then one can group treatments together for analysis/diagnosis/payment management). [Para. 0120] The overall model contains states and findings. A patient record contains events. These events are projected on the overall model so as to be grouped. The outcomes of the process are that such events are grouped into episode(s) for better understanding of the connections between the events in a patient record. [Para. 0121] The system inputs an electronic patient record. It scans through the record and identifies the key events that can be aligned.)
an order of the two or more qualifying events or an order of the two or more response states is immaterial ([Para.0016 - 0018] a state transition learner, to parse published clinical guidelines and to extract probabilities of transition between a number of states of a medical condition as a state transition model (Examiner interprets this to be indicative of qualifying events); a clinical finding learner, to extract typical findings of the medical condition from domain knowledge as a finding model and to compute the probability of a particular finding for a particular state and to save the probabilities in an overall episode model including the state transition model and the finding model (Examiner interprets this to be indicative of response state); and an episode grouper, to use the overall episode model, and the set of medical records to discover a sequence of events, to group the sequence of events into an episode of the medical condition, and to differentiate the medical condition from apparently similar medical conditions or co-morbidities, or both. [Para. 0031] For example, the episode grouper is to match the powerset of the sequence of events in the set of medical records to the overall episode model, with the exception of the empty set and/or any sets including a number of events below a threshold. It is important that the events are kept in the correct order in the powerset, but events in the time line may be omitted (and for instance included later in an episode of another condition). The threshold may be 2 events, so that a “sequence” of a single event is not assessed for a match with an episode. The various subsets of sequences can be assessed in any order, for example in random order or fully/partially in parallel. Examiner interprets the assessment of the subsets of sequences to be indicative of an order of the two or more qualifying events or an order of the two or more response states is immaterial.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of cost-benefit analysis in real-time for any well-defined medical treatment protocol as taught by Aggarwal and incorporate accessing the various subsets of medical episode sequences in any order as taught by Hu, with the motivation of reducing human error when it comes to defining or grouping an episode of a certain disease and accounting variances in the human condition (Hu Para. 0010).
Aggarwal/ Hu do not explicitly disclose, however De La Torre discloses
wherein the state transition graph comprises a plurality of subgraphs of the individual treatment pathways comprising one or more qualifying events and one or more response states to the one or more qualifying events; ([Para. 0009] a healthcare risk engine to provide a healthcare risk knowledge graph from the open data and clinician input by using clinician input of risk-related terms to retrieve relevant documents from the open data and by extracting the healthcare risk knowledge graph as entities from the documents corresponding to the clinician's terms, as well as the links between the entities; a patient risk graph prediction module to predict risks for a specific patient by combining information in a Patient Clinical Object, PCO, with entities in the healthcare risk knowledge graph to produce a patient risk graph; and an impact estimator module to estimate the impact of a potential specific treatment by taking the specific treatment and adjacent nodes from the healthcare risk knowledge graph to form a healthcare treatment subgraph and finding one or more corresponding entities and (their) adjacent nodes in the patient risk graph to form a patient treatment subgraph, and providing an impact graph by combining the patient treatment subgraph and the healthcare treatment subgraph and retaining the resultant linked nodes as the impact graph. [Para. 0104] FIG. 6 depicts each one of the predictors and their relation with the Patient Clinical Object, Healthcare Risks Knowledge graph and the output, which is the patient risk sub graph prediction. This prediction indicates which risks are valid for a particular patient. Nodes of the subgraph can include risks, risk factors and treatments.)
receiving, by the one or more computing devices, a user selection of at least one of the plurality of sub graphs; ([Para. 0144] the computing device also includes a network interface 999 for communication with other such computing devices of embodiments. For example, an embodiment may be composed of a network of such computing devices. [Para. 0015] The system may further comprise a knowledge graph curator, to display the risk knowledge graph and to accept clinician input to manually curate the generated graph. [Para. 0128] The impact estimator module can use any suitable number of entities (nodes) in the healthcare risk graph to form the healthcare treatment subgraph, and any suitable selection means. For example, the impact estimator module can take the entity or node for the specific treatment, and can include further nodes including at least one entity in the risk or risk factor category to form the healthcare treatment subgraph. [Para. 0138] Identification of risks for a given treatment, in which the module extracts the factors associated with a given treatment, and looks for the risks (of the extracted factors) in the Healthcare Risks Knowledge Graph. Here, the relevant section of the graph around the treatment is selected, for example by including the terms (nodes) that are directly linked to the treatment in the graph, or also including more nodes, for instance the neighbor and the next node(s) along the path.)
and displaying, in response to receiving the user selection, the selected at least one of the plurality of sub graphs. ([Para. 0146] The display unit 997 displays a representation of data stored by the computing device and displays a cursor and dialog boxes and screens enabling interaction between a user and the programs and data stored on the computing device. [Para. 0138] Identification of risks for a given treatment, in which the module extracts the factors associated with a given treatment, and looks for the risks (of the extracted factors) in the Healthcare Risks Knowledge Graph. Here, the relevant section of the graph around the treatment is selected, for example by including the terms (nodes) that are directly linked to the treatment in the graph, or also including more nodes, for instance the neighbor and the next node(s) along the path.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of cost-benefit analysis in real-time for any well-defined medical treatment protocol as taught by Aggarwal, accessing the various subsets of medical episode sequences in any order as taught by Hu, and incorporate the healthcare risk knowledge graph as taught by De La Torre, with the motivation of understanding the risks affecting a given patient and helping clinicians to decide to the best treatment to apply to patients (De La Torre Para. 0005).
As per Claim 3 , Aggarwal/ Hu/ De La Torre discloses the method of claim 1, wherein Aggarwal further discloses the one or more treatment regimens is selected from the group consisting of a drug regimen, a surgical protocol, a collection of eligible interventions, or combinations thereof. ([Para. 0063] the description of deterministic variables associated with the breast cancer treatment and various lab tests, pathological tests, imaging tests, surgical procedures, therapeutic treatments, supportive therapy, medical consultation, check-up, hospitalization, etc.)
As per Claim 4, Aggarwal/ Hu/ De La Torre discloses the method of claim 1, Aggarwal further discloses wherein the one or more response states is selected from the group consisting of a response status after a treatment; ([Para. 0012] each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. Hence, by moving from one vertex to another, the user can view the entire state-transition graph for a particular disease (and the treatment protocols that are included in the system for this disease). [Para. 0046] analytics engine 104 represents different stages of the treatment protocols in a state-transition graph. Paths originating from each vertex of the state-transition graph represent different options or treatments that are available at that stage. Further, each vertex represents a state of the patient at that particular point of time (during the treatment).)
and a subtype of the patient based on a specific gene signature. ([Para.0081] Her 2 neu: Positive or negative, which is indicative of cell growth. Para. 0086 teaches Oncotype Dx Assay (Gene expression profile to assess the need for chemotherapy). Para. 0087 teaches other potential parameters (e.g., other genetic markers).])
As per Claim 5, Aggarwal/ Hu/ De La Torre discloses the method of claim 1, Aggarwal further discloses wherein the one or more response states is linked to one or more reports selected from the group consisting of a clinical report, a radiology report, a pathology report, a genomics report, or combinations thereof. ([Para. 0046] analytics engine 104 represents different stages of the treatment protocols in a state-transition graph. Paths originating from each vertex of the state-transition graph represent different options or treatments that are available at that stage. Further, each vertex represents a state of the patient at that particular point of time (during the treatment). [Para. 0029] input parameters may include, for example, age of the patient, sex of the patient, ethnicity, type of the disease, stage of the disease, case history, treatments administered, family history, the region where the patient resides, and the like. [Para. 0063] the description of deterministic variables associated with the breast cancer treatment and various lab tests, pathological tests, imaging tests, surgical procedures, therapeutic treatments, supportive therapy, medical consultation, check-up, hospitalization, etc. [Para. 0086] Oncotype Dx Assay (Gene expression profile to assess the need for chemotherapy).)
As per Claim 6, Aggarwal/ Hu/ De La Torre discloses the method of claim 1, wherein Aggarwal further discloses the constructing comprises adding individual treatment pathways one at a time to the state transition graph. ([Para. 0015] the system allows the user at any stage of the treatment regimen to provide details, if any, that are related to complications or unexpected events. [Para. 0026] analytics engine 104 performs statistical and computational analysis based on the input parameters and generates (as well as regularly updates) a state-transition graph representing the different stages of different treatment protocols along with potential outcomes. The statistical and computational analysis includes computing a close cohort for the patient, and based on this close cohort, analytics engine 104 estimates the likelihood of various output parameters (i.e., potential outcomes) and the statistical significance related to these outcomes.)
As per Claims 7, Aggarwal/ Hu/ De La Torre discloses the method of claim 1, wherein Aggarwal further discloses the state transition graph comprises edges that correspond to treatments of a similar nature. ([Para. 0033] the state-transition graph has vertices and directed edges. The directed edges originating from a vertex indicate different options that are available at a particular stage of the treatment protocol. [Para. 0049] the connecting arc or arcs originating from one vertex to another vertex or vertices represent the alternatives that are available to the user at that instance of time. Examiner interprets the arcs to be indicative of edges. [Para. 0011] the analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters.)
As per Claim 11, Aggarwal teaches a system for processing treatment and clinical data, comprising:
a memory configured to store instructions; ([Para. 0196] the computer also includes a memory, which may include Random Access Memory (RAM) and Read Only Memory (ROM).)
a processor configured to execute the instructions to: ([Para. 0196] the computer also comprises a microprocessor or processor)
obtain data that comprises treatment history and clinical data of a cohort of patients; ([Para. 0010] a user of the system interacts with the system using the graphical user interface (GUI). The user provides input parameters (related to a patient) such as patient history (e.g., age, gender, family history, grade of tumor, size of tumor, ethnicity etc.), current state of the patient, treatment administered so far, and the like. [Para. 0026] the user may provide input parameters related to historical information of a patient and one or more treatment protocols that may be used for the treatment of the patient. [Para. 0029] input parameters may include, for example, age of the patient, sex of the patient, ethnicity, type of the disease, stage of the disease, case history, treatments administered, family history, the region where the patient resides, and the like. Examiner interprets the use of a GUI to be indicative of a computing device.])
receive a user-defined parameter for each of: (i) one or more qualifying events;(ii) one or more responses states to one or more of the one or more qualifying events; and (iii) one or more collapsible events; ([Para. 0034] ARBE 202 receives input parameters related to the patient such as personal details, case history, and treatment protocols (i.e. one or more qualifying events) that have been administered so far. [Para. 0029] Input parameters may include, for example, age of the patient, sex of the patient, ethnicity, type of the disease, stage of the disease, case history (i.e. response states), treatments administered (i.e. qualifying events), family history, the region where the patient resides, and the like. [Para. 0011] The analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort (i.e. collapsible events) represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters.)
generate individual treatment pathways for individual patients of the cohort of patients using the treatment history and clinical data for the individual patients using user- defined parameters comprising: ([Para. 0012] the analytics engine uses a state-transition graph to represent the current state of the patient. Furthermore, the user views this state-transition graph through the GUI. Each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. [Para. 0022] FIG. 5a and FIG. 5b illustrate an exemplary state-transition graph for treatment of breast cancer in accordance with an embodiment of the invention. [Para. 0026] The user may provide input parameters related to historical information of a patient and one or more treatment protocols that may be used for the treatment of the patient. Further, output of the analysis performed by system 100 is displayed to the user via GUI 102. Analytics engine 104 performs statistical and computational analysis based on the input parameters and generates (as well as regularly updates) a state-transition graph representing the different stages of different treatment protocols along with potential outcomes.)
one or more qualifying events, comprising one or more treatment regimens; ([Para. 0012] each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. Hence, by moving from one vertex to another, the user can view the entire state-transition graph for a particular disease (and the treatment protocols that are included in the system for this disease). [Para. 0046] Analytics engine 104 represents different stages of the treatment protocols in a state-transition graph. [Para. 0023] The terms `treatment regimen`, `treatment course`, and `treatment protocol` have been used interchangeably.)
one or more response states to the one or more qualifying events; ([Para. 0012] Each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. [Para. 0046] paths originating from each vertex of the state-transition graph represent different options or treatments that are available at that stage. Further, each vertex represents a state of the patient at that particular point of time (during the treatment).)
and one or more collapsible events each comprising a collapse of two or more similar qualifying events or two more similar response states, wherein an order of the two or more similar qualifying events or an order of the two or more similar response states is immaterial and are collapsed into a single collapsible event; ([Para. 0011] the analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters. [Para. 0026] The close cohort is computed by using the information related to case histories of patients, clinical research information, market research information, and related information stored in databases 108 and 110. [Para. 0036] Creates close cohorts by combining "near identical cohorts. Examiner interprets the combination of close cohorts to be indicative of collapsible events. Examiner interprets that since the close cohorts are created based on similar patient histories and experiences that would be indicative of two or more qualifying event or response states. )
construct a state transition graph using the individual treatment pathways, the state transition graph comprising multiple aligned and merged individual treatment pathways comprising the one or more qualifying events, the one or more response states to the one or more qualifying events and the one or more collapsible events, ([Para. 0011] the analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters. [Para. 0036] the analytical rule-based engine (ARBE) creates close cohorts by combining "near identical cohorts. [Para. 0026] analytics engine 104 performs statistical and computational analysis based on the input parameters and generates (as well as regularly updates) a state-transition graph representing the different stages of different treatment protocols along with potential outcomes. The statistical and computational analysis includes computing a close cohort for the patient, and based on this close cohort, analytics engine 104 estimates the likelihood of various output parameters (i.e., potential outcomes) and the statistical significance related to these outcomes. [Para. 0062] The set of output parameters and corresponding costs for the cancer treatment protocols are displayed to the user through GUI 102. The cancer treatment protocols (which include the set of output parameters calculated from the close cohort) and different options are displayed to the user in the form of a state-transition graph.)
Aggarwal does not explicitly disclose, however Hu discloses
wherein the user-defined parameter for the one or more collapsible events comprises an indication that an order of two or more consecutive events is immaterial such that the two or more consecutive events are collapsible into one combined event for simplifying an individual treatment pathway; ([Para.0016 - 0018] A state transition learner, to parse published clinical guidelines and to extract probabilities of transition between a number of states of a medical condition as a state transition model; a clinical finding learner, to extract typical findings of the medical condition from domain knowledge as a finding model and to compute the probability of a particular finding for a particular state and to save the probabilities in an overall episode model including the state transition model and the finding model; and an episode grouper, to use the overall episode model, and the set of medical records to discover a sequence of events, to group the sequence of events into an episode of the medical condition, and to differentiate the medical condition from apparently similar medical conditions or co-morbidities, or both. [Para. 0031] For example, the episode grouper is to match the powerset of the sequence of events in the set of medical records to the overall episode model, with the exception of the empty set and/or any sets including a number of events below a threshold. It is important that the events are kept in the correct order in the powerset, but events in the time line may be omitted (and for instance included later in an episode of another condition). The threshold may be 2 events, so that a “sequence” of a single event is not assessed for a match with an episode. The various subsets of sequences can be assessed in any order, for example in random order or fully/partially in parallel. Examiner interprets the assessment of the subsets of sequences to be indicative of an order of the two or more consecutive events is immaterial. [Para. 0091] Identify the most appropriate sequence of patient's treatments or other findings (documented in his/her medical records) that can be grouped into one episode characterized by E(d)=π.sub.0.sup.d, . . . , π.sub.m.sup.d where dεD is a disease and D is the set of diseases whose progression trajectories are known or can be learnt from existing data. This gives benefit of fully understanding the episode (then one can group treatments together for analysis/diagnosis/payment management). [Para. 0120] The overall model contains states and findings. A patient record contains events. These events are projected on the overall model so as to be grouped. The outcomes of the process are that such events are grouped into episode(s) for better understanding of the connections between the events in a patient record. [Para. 0121] The system inputs an electronic patient record. It scans through the record and identifies the key events that can be aligned.)
an order of the two or more qualifying events or an order of the two or more response states is immaterial ([Para.0016 - 0018] a state transition learner, to parse published clinical guidelines and to extract probabilities of transition between a number of states of a medical condition as a state transition model (Examiner interprets this to be indicative of qualifying events); a clinical finding learner, to extract typical findings of the medical condition from domain knowledge as a finding model and to compute the probability of a particular finding for a particular state and to save the probabilities in an overall episode model including the state transition model and the finding model (Examiner interprets this to be indicative of response state); and an episode grouper, to use the overall episode model, and the set of medical records to discover a sequence of events, to group the sequence of events into an episode of the medical condition, and to differentiate the medical condition from apparently similar medical conditions or co-morbidities, or both. [Para. 0031] For example, the episode grouper is to match the powerset of the sequence of events in the set of medical records to the overall episode model, with the exception of the empty set and/or any sets including a number of events below a threshold. It is important that the events are kept in the correct order in the powerset, but events in the time line may be omitted (and for instance included later in an episode of another condition). The threshold may be 2 events, so that a “sequence” of a single event is not assessed for a match with an episode. The various subsets of sequences can be assessed in any order, for example in random order or fully/partially in parallel. Examiner interprets the assessment of the subsets of sequences to be indicative of an order of the two or more qualifying events or an order of the two or more response states is immaterial.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of cost-benefit analysis in real-time for any well-defined medical treatment protocol as taught by Aggarwal and incorporate accessing the various subsets of medical episode sequences in any order as taught by Hu, with the motivation of reducing human error when it comes to defining or grouping an episode of a certain disease and accounting variances in the human condition (Hu Para. 0010).
Aggarwal/ Hu do not explicitly disclose, however De La Torre discloses
wherein the state transition graph comprises a plurality of subgraphs of the individual treatment pathways comprising one or more qualifying events and one or more response states to the one or more qualifying events; ([Para. 0009] a healthcare risk engine to provide a healthcare risk knowledge graph from the open data and clinician input by using clinician input of risk-related terms to retrieve relevant documents from the open data and by extracting the healthcare risk knowledge graph as entities from the documents corresponding to the clinician's terms, as well as the links between the entities; a patient risk graph prediction module to predict risks for a specific patient by combining information in a Patient Clinical Object, PCO, with entities in the healthcare risk knowledge graph to produce a patient risk graph; and an impact estimator module to estimate the impact of a potential specific treatment by taking the specific treatment and adjacent nodes from the healthcare risk knowledge graph to form a healthcare treatment subgraph and finding one or more corresponding entities and (their) adjacent nodes in the patient risk graph to form a patient treatment subgraph, and providing an impact graph by combining the patient treatment subgraph and the healthcare treatment subgraph and retaining the resultant linked nodes as the impact graph. [Para. 0104] FIG. 6 depicts each one of the predictors and their relation with the Patient Clinical Object, Healthcare Risks Knowledge graph and the output, which is the patient risk sub graph prediction. This prediction indicates which risks are valid for a particular patient. Nodes of the subgraph can include risks, risk factors and treatments.)
receive, by the one or more computing devices, a user selection of at least one of the plurality of subgraphs; ([Para. 0144] the computing device also includes a network interface 999 for communication with other such computing devices of embodiments. For example, an embodiment may be composed of a network of such computing devices. [Para. 0015] The system may further comprise a knowledge graph curator, to display the risk knowledge graph and to accept clinician input to manually curate the generated graph. [Para. 0128] The impact estimator module can use any suitable number of entities (nodes) in the healthcare risk graph to form the healthcare treatment subgraph, and any suitable selection means. For example, the impact estimator module can take the entity or node for the specific treatment, and can include further nodes including at least one entity in the risk or risk factor category to form the healthcare treatment subgraph. [Para. 0138] Identification of risks for a given treatment, in which the module extracts the factors associated with a given treatment, and looks for the risks (of the extracted factors) in the Healthcare Risks Knowledge Graph. Here, the relevant section of the graph around the treatment is selected, for example by including the terms (nodes) that are directly linked to the treatment in the graph, or also including more nodes, for instance the neighbor and the next node(s) along the path.)
and display, in response to receiving the user selection, the selected at least one of the plurality of subgraphs. ([Para. 0146] The display unit 997 displays a representation of data stored by the computing device and displays a cursor and dialog boxes and screens enabling interaction between a user and the programs and data stored on the computing device. [Para. 0138] Identification of risks for a given treatment, in which the module extracts the factors associated with a given treatment, and looks for the risks (of the extracted factors) in the Healthcare Risks Knowledge Graph. Here, the relevant section of the graph around the treatment is selected, for example by including the terms (nodes) that are directly linked to the treatment in the graph, or also including more nodes, for instance the neighbor and the next node(s) along the path.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of cost-benefit analysis in real-time for any well-defined medical treatment protocol as taught by Aggarwal, accessing the various subsets of medical episode sequences in any order as taught by Hu, and incorporate the healthcare risk knowledge graph as taught by De La Torre, with the motivation of understanding the risks affecting a given patient and helping clinicians to decide to the best treatment to apply to patients (De La Torre Para. 0005).
As per Claim 13, Claim(s) 13 is/are analogous to Claim(s) 3, thus Claim(s) 13 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
As per Claim 14, Claim(s) 14 is/are analogous to Claim(s) 4, thus Claim(s) 14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4.
As per Claim 15, Claim(s) 15 is/are analogous to Claim(s) 6, thus Claim(s) 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 6.
As per Claim 16, Claim(s) 16 is/are analogous to Claim(s) 5, thus Claim(s) 16 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5.
As per Claim 17, Aggarwal/ Hu/ De La Torre discloses the system of claim 16, wherein Aggarwal further discloses the processor is configured to link the one or more response states to one or more genomics reports. ([Para. 0046] analytics engine 104 represents different stages of the treatment protocols in a state-transition graph. Paths originating from each vertex of the state-transition graph represent different options or treatments that are available at that stage. Further, each vertex represents a state of the patient at that particular point of time (during the treatment). [Para. 0029] input parameters may include, for example, age of the patient, sex of the patient, ethnicity, type of the disease, stage of the disease, case history, treatments administered, family history, the region where the patient resides, and the like. [Para. 0063] teaches the description of deterministic variables associated with the breast cancer treatment and various lab tests, pathological tests, imaging tests, surgical procedures, therapeutic treatments, supportive therapy, medical consultation, check-up, hospitalization, etc. [Para. 0086] teaches Oncotype Dx Assay (Gene expression profile to assess the need for chemotherapy).)
As per Claim 18, Claim(s) 18 is/are analogous to Claim(s) 7, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
As per Claim 19, Aggarwal/ Hu/ De La Torre discloses the system of claim 18, Aggarwal further teaches wherein the processor is configured to collapse edges corresponding to treatments of a similar nature. ([Para. 0033] the directed edges originating from a vertex indicate different options that are available at a particular stage of the treatment protocol. [Para. 0049] the connecting arc or arcs originating from one vertex to another vertex or vertices represent the alternatives that are available to the user at that instance of time. Examiner interprets the arcs to be indicative of edges. [Para. 0011] the analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters.)
As per Claim 22, Aggarwal teaches a non-transitory, machine-readable medium storing instructions for controlling a processor to perform operations which comprise:
obtaining, by one or more computing devices, data that comprises treatment history and clinical data of a cohort of patients; ([Para. 0010] a user of the system interacts with the system using the graphical user interface (GUI). The user provides input parameters (related to a patient) such as patient history (e.g., age, gender, family history, grade of tumor, size of tumor, ethnicity etc.), current state of the patient, treatment administered so far, and the like. [Para. 0026] the user may provide input parameters related to historical information of a patient and one or more treatment protocols that may be used for the treatment of the patient. [Para. 0029] input parameters may include, for example, age of the patient, sex of the patient, ethnicity, type of the disease, stage of the disease, case history, treatments administered, family history, the region where the patient resides, and the like. Examiner interprets the use of a GUI to be indicative of a computing device.])
receiving a user-defined parameter for each of: (i) one or more qualifying events;(ii) one or more responses states to one or more of the one or more qualifying events; and (iii) one or more collapsible events; ([Para. 0034] ARBE 202 receives input parameters related to the patient such as personal details, case history, and treatment protocols (i.e. one or more qualifying events) that have been administered so far. [Para. 0029] Input parameters may include, for example, age of the patient, sex of the patient, ethnicity, type of the disease, stage of the disease, case history (i.e. response states), treatments administered (i.e. qualifying events), family history, the region where the patient resides, and the like. [Para. 0011] The analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort (i.e. collapsible events) represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters.)
generating, by the one or more computing devices, individual treatment pathways for individual patients of the cohort of patients using the treatment history and clinical data for the individual patients; ([Para. 0012] the analytics engine uses a state-transition graph to represent the current state of the patient. Furthermore, the user views this state-transition graph through the GUI. Each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. [Para. 0022] FIG. 5a and FIG. 5b illustrate an exemplary state-transition graph for treatment of breast cancer in accordance with an embodiment of the invention. [Para. 0026] The user may provide input parameters related to historical information of a patient and one or more treatment protocols that may be used for the treatment of the patient. Further, output of the analysis performed by system 100 is displayed to the user via GUI 102. Analytics engine 104 performs statistical and computational analysis based on the input parameters and generates (as well as regularly updates) a state-transition graph representing the different stages of different treatment protocols along with potential outcomes.)
wherein the individual treatment pathways are generated using user-defined parameters comprising:
one or more qualifying events, comprising one or more treatment regimens; ([Para. 0012] each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. Hence, by moving from one vertex to another, the user can view the entire state-transition graph for a particular disease (and the treatment protocols that are included in the system for this disease). [Para. 0046] further teaches analytics engine 104 represents different stages of the treatment protocols in a state-transition graph. [Para. 0023] The terms `treatment regimen`, `treatment course`, and `treatment protocol` have been used interchangeably.)
one or more response states to the one or more qualifying events; ([Para. 0012] Each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. [Para. 0046] paths originating from each vertex of the state-transition graph represent different options or treatments that are available at that stage. Further, each vertex represents a state of the patient at that particular point of time (during the treatment).)
and one or more collapsible events each comprising a collapse of two or more similar qualifying events or two more similar response states, wherein an order of the two or more similar qualifying events or an order of the two or more similar response states are collapsed into a single collapsible event; ([Para. 0011] the analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters. [Para. 0026] The close cohort is computed by using the information related to case histories of patients, clinical research information, market research information, and related information stored in databases 108 and 110. [Para. 0036] Creates close cohorts by combining "near identical cohorts. Examiner interprets the combination of close cohorts to be indicative of collapsible events. Examiner interprets that since the close cohorts are created based on similar patient histories and experiences that would be indicative of two or more qualifying event or response states.)
constructing, by the one or more computing devices, a state transition graph using the individual treatment pathways, the state transition graph comprising multiple aligned and merged individual treatment pathways comprising the one or more qualifying events, the one or more response states to the one or more qualifying events and the one or more collapsible events; ([Para. 0011] the analysis also takes into account information related to a "close cohort" corresponding to a selected treatment protocol. A close cohort represents a group of patients that have been subjected to a treatment protocol substantially similar to the selected treatment protocol and that have substantially similar input and output parameters. [Para. 0036] the analytical rule-based engine (ARBE) creates close cohorts by combining "near identical cohorts. [Para. 0026] analytics engine 104 performs statistical and computational analysis based on the input parameters and generates (as well as regularly updates) a state-transition graph representing the different stages of different treatment protocols along with potential outcomes. The statistical and computational analysis includes computing a close cohort for the patient, and based on this close cohort, analytics engine 104 estimates the likelihood of various output parameters (i.e., potential outcomes) and the statistical significance related to these outcomes. [Para. 0062] The set of output parameters and corresponding costs for the cancer treatment protocols are displayed to the user through GUI 102. The cancer treatment protocols (which include the set of output parameters calculated from the close cohort) and different options are displayed to the user in the form of a state-transition graph.)
Aggarwal does not explicitly disclose, however Hu discloses
wherein the user-defined parameter for the one or more collapsible events comprises an indication that an order of two or more consecutive events is immaterial such that the two or more consecutive events are collapsible into one combined event for simplifying an individual treatment pathway; ([Para.0016 - 0018] A state transition learner, to parse published clinical guidelines and to extract probabilities of transition between a number of states of a medical condition as a state transition model; a clinical finding learner, to extract typical findings of the medical condition from domain knowledge as a finding model and to compute the probability of a particular finding for a particular state and to save the probabilities in an overall episode model including the state transition model and the finding model; and an episode grouper, to use the overall episode model, and the set of medical records to discover a sequence of events, to group the sequence of events into an episode of the medical condition, and to differentiate the medical condition from apparently similar medical conditions or co-morbidities, or both. [Para. 0031] For example, the episode grouper is to match the powerset of the sequence of events in the set of medical records to the overall episode model, with the exception of the empty set and/or any sets including a number of events below a threshold. It is important that the events are kept in the correct order in the powerset, but events in the time line may be omitted (and for instance included later in an episode of another condition). The threshold may be 2 events, so that a “sequence” of a single event is not assessed for a match with an episode. The various subsets of sequences can be assessed in any order, for example in random order or fully/partially in parallel. Examiner interprets the assessment of the subsets of sequences to be indicative of an order of the two or more consecutive events is immaterial. [Para. 0091] Identify the most appropriate sequence of patient's treatments or other findings (documented in his/her medical records) that can be grouped into one episode characterized by E(d)=π.sub.0.sup.d, . . . , π.sub.m.sup.d where dεD is a disease and D is the set of diseases whose progression trajectories are known or can be learnt from existing data. This gives benefit of fully understanding the episode (then one can group treatments together for analysis/diagnosis/payment management). [Para. 0120] The overall model contains states and findings. A patient record contains events. These events are projected on the overall model so as to be grouped. The outcomes of the process are that such events are grouped into episode(s) for better understanding of the connections between the events in a patient record. [Para. 0121] The system inputs an electronic patient record. It scans through the record and identifies the key events that can be aligned.)
an order of the two or more qualifying events or an order of the two or more response states is immaterial ([Para.0016 - 0018] a state transition learner, to parse published clinical guidelines and to extract probabilities of transition between a number of states of a medical condition as a state transition model (Examiner interprets this to be indicative of qualifying events); a clinical finding learner, to extract typical findings of the medical condition from domain knowledge as a finding model and to compute the probability of a particular finding for a particular state and to save the probabilities in an overall episode model including the state transition model and the finding model (Examiner interprets this to be indicative of response state); and an episode grouper, to use the overall episode model, and the set of medical records to discover a sequence of events, to group the sequence of events into an episode of the medical condition, and to differentiate the medical condition from apparently similar medical conditions or co-morbidities, or both. [Para. 0031] For example, the episode grouper is to match the powerset of the sequence of events in the set of medical records to the overall episode model, with the exception of the empty set and/or any sets including a number of events below a threshold. It is important that the events are kept in the correct order in the powerset, but events in the time line may be omitted (and for instance included later in an episode of another condition). The threshold may be 2 events, so that a “sequence” of a single event is not assessed for a match with an episode. The various subsets of sequences can be assessed in any order, for example in random order or fully/partially in parallel. Examiner interprets the assessment of the subsets of sequences to be indicative of an order of the two or more qualifying events or an order of the two or more response states is immaterial.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of cost-benefit analysis in real-time for any well-defined medical treatment protocol as taught by Aggarwal and incorporate accessing the various subsets of medical episode sequences in any order as taught by Hu, with the motivation of reducing human error when it comes to defining or grouping an episode of a certain disease and accounting variances in the human condition (Hu Para. 0010).
Aggarwal/ Hu do not explicitly disclose, however De La Torre discloses
wherein the state transition graph comprises a plurality of subgraphs of the individual treatment pathways comprising one or more qualifying events and one or more response states to the one or more qualifying events ([Para. 0009] a healthcare risk engine to provide a healthcare risk knowledge graph from the open data and clinician input by using clinician input of risk-related terms to retrieve relevant documents from the open data and by extracting the healthcare risk knowledge graph as entities from the documents corresponding to the clinician's terms, as well as the links between the entities; a patient risk graph prediction module to predict risks for a specific patient by combining information in a Patient Clinical Object, PCO, with entities in the healthcare risk knowledge graph to produce a patient risk graph; and an impact estimator module to estimate the impact of a potential specific treatment by taking the specific treatment and adjacent nodes from the healthcare risk knowledge graph to form a healthcare treatment subgraph and finding one or more corresponding entities and (their) adjacent nodes in the patient risk graph to form a patient treatment subgraph, and providing an impact graph by combining the patient treatment subgraph and the healthcare treatment subgraph and retaining the resultant linked nodes as the impact graph. [Para. 0104] FIG. 6 depicts each one of the predictors and their relation with the Patient Clinical Object, Healthcare Risks Knowledge graph and the output, which is the patient risk sub graph prediction. This prediction indicates which risks are valid for a particular patient. Nodes of the subgraph can include risks, risk factors and treatments.)
receiving, by the one or more computing devices, a user selection of at least one of the plurality of subgraphs; ([Para. 0144] the computing device also includes a network interface 999 for communication with other such computing devices of embodiments. For example, an embodiment may be composed of a network of such computing devices. [Para. 0015] The system may further comprise a knowledge graph curator, to display the risk knowledge graph and to accept clinician input to manually curate the generated graph. [Para. 0128] The impact estimator module can use any suitable number of entities (nodes) in the healthcare risk graph to form the healthcare treatment subgraph, and any suitable selection means. For example, the impact estimator module can take the entity or node for the specific treatment, and can include further nodes including at least one entity in the risk or risk factor category to form the healthcare treatment subgraph. [Para. 0138] Identification of risks for a given treatment, in which the module extracts the factors associated with a given treatment, and looks for the risks (of the extracted factors) in the Healthcare Risks Knowledge Graph. Here, the relevant section of the graph around the treatment is selected, for example by including the terms (nodes) that are directly linked to the treatment in the graph, or also including more nodes, for instance the neighbor and the next node(s) along the path.)
and displaying, in response to receiving the user selection, the selected at least one of the plurality of subgraphs. ([Para. 0146] The display unit 997 displays a representation of data stored by the computing device and displays a cursor and dialog boxes and screens enabling interaction between a user and the programs and data stored on the computing device. [Para. 0138] Identification of risks for a given treatment, in which the module extracts the factors associated with a given treatment, and looks for the risks (of the extracted factors) in the Healthcare Risks Knowledge Graph. Here, the relevant section of the graph around the treatment is selected, for example by including the terms (nodes) that are directly linked to the treatment in the graph, or also including more nodes, for instance the neighbor and the next node(s) along the path.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of cost-benefit analysis in real-time for any well-defined medical treatment protocol as taught by Aggarwal, accessing the various subsets of medical episode sequences in any order as taught by Hu, and incorporate the healthcare risk knowledge graph as taught by De La Torre, with the motivation of understanding the risks affecting a given patient and helping clinicians to decide to the best treatment to apply to patients (De La Torre Para. 0005).
Claim(s) 9 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal (US 20100125462 A1) in view of Hu (US 20180082025 A1) in view of De La Torre (US 20170277857 A1) in view of De Peuter (US 20110148880 A1).
As per Claim 9, Aggarwal/ Hu/ De La Torre discloses the method of claim 1, wherein Aggarwal discloses the method further comprises:
identification of a qualifying event or a response state in the state transition graph ([Para. 0012] each vertex of the state-transition graph represents the state of the patient at each stage of the treatment protocol. Hence, by moving from one vertex to another, the user can view the entire state-transition graph for a particular disease (and the treatment protocols that are included in the system for this disease). [Para. 0046] analytics engine 104 represents different stages of the treatment protocols in a state-transition graph. Paths originating from each vertex of the state-transition graph represent different options or treatments that are available at that stage. Further, each vertex represents a state of the patient at that particular point of time (during the treatment).)
Aggarwal does not explicitly disclose, however De Peuter discloses
receiving a user selection of at least one of the plurality of subgraphs comprises, and wherein displaying the selected at least one of the plurality of subgraphs comprises removing, from the display, each of the one or more qualifying events, the one or more response states to the one or more qualifying events, and the one or more collapsible events not traversed by the subgraph.([Para. 0044] a selection is received from a user, selecting a portion of the graph to be collapsed. [Para. 0004] collapsing the service graph to remove nodes of no interest is typically done with multiple mouse clicks, expanding one level at a time. [Para. 0012] Subgraphs may be collapsed with a single user interaction, retaining the important service context information and relations, while minimizing the temporal "noise" in the service model. Users may collapse critical paths in the service model graph by collapsing subgraphs, even if the subgraph contains multiple nodes or vertices that meet criteria for remaining visible in the collapsed subgraph. [Para. 0017] Each nodes or vertex in the graph may be displayed with as much or as little information as may be considered appropriate for that node, and in some embodiments, selecting the node may cause additional or less information to be displayed, depending on the current state of the display. The format and information displayed for the nodes, as well as for the connectors or edges between nodes is outside of the scope of the present invention. The service model is a directed acyclic graph containing service components. The directed association between service components in the graph defines a relationship between the service components. Service components may represent system components or any logical or physical resource, function, process or service necessary for efficient business operation. )
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of constructing a state transition graph for treatment, procedure, and progression as taught by Aggarwal/ Hu/ De La Torre and incorporate allowing the collapse or expansion of user selected subgraphs as taught by De Peuter, with the motivation of retaining the important service context information and relations, while minimizing information of lesser importance in the service model (De Peuter Abstract).
As per Claim 20, Claim(s) 20 is/are analogous to Claim(s) 9, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 9.
Claim(s) 10 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal (US 20100125462 A1) in view of Hu (US 20180082025 A1) in view of De La Torre (US 20170277857 A1) in view of Van der Koijk (US 20180052962 A1).
As per Claim 10, Aggarwal/ Hu/ De La Torre discloses the method of claim 1, Hu discloses further comprising
identifying the largest possible matching sequence of state-event-state units between the new individual pathway and the state transition graph as anchor points; ([Para. 0030] the episode grouper may match sequences of events in the set of medical records to the overall episode model and detect the best match between a sequence of events and the overall episode model.)
wherein the resulting state transition graph remains acyclic and has the least number of additional response states and edges. ([Para. 0033] The episode grouper may process remaining events in the set of medical records once a sequence of events has been grouped into an episode, by matching sequences of remaining events. This allows other conditions to be identified on the basis of co-morbidities which might otherwise have been categorized as part of the same condition. [Para. 0106] the relationship between disease states and observations is modeled with a bi-partite noisy-or Bayesian network (a directed acyclic graph in which variables are linked by edges including probabilities: the graph can be used to derive further probabilities)).
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of performing a real time cost-benefit analysis for treatment of chronic diseases such as cancer as taught by Aggarwal and incorporate grouping and matching of medical events experienced by a patient as taught by Hu, with the motivation of reducing human error when it comes to defining or grouping an episode of a certain disease and accounting variances in the human condition (Hu Para. 0010).
Hu does not explicitly disclose, however Van der Koijk discloses
receiving a new individual pathway to add to the state transition graph; ([Para.0030] the clinical objective, or goal for treatment, may be based on clinical information 103 gathered from workflows (state transition graph) which depend on concurrency (e.g., multiple different activities that are temporally and/or sequentially connected to the same snapshot of patient information at a given point in time, for example, a case). For example, clinical information 103 may include data gathered from a currently active treatment session, such as data gathered by one or more image processing devices (e.g., image data showing the contours of organs at risk (OAR) and a target treatment location). [Para. 0031] medical professional 102 may re-define the clinical objective to create a new clinical objective 107 based on newly available intermediate information. For example, imaging information may indicate that a tumor is not responding to the first clinical treatment 106 (e.g., the tumor fails to shrink based on the amount of radiation prescribed by the treatment fraction). In another example, lab and vital information may indicate the occurrence of undesired side effects of treatment 106 (e.g., organs at risk are responding negatively). [Para. 0032] the medical professional 102 may open a new path (subcase) in the case having one or more new clinical objectives 107. Examiner interprets the opening of new path to be indicative of receiving a new individual pathway to add to the state transition graph.)
and adding the new individual pathway to the state transition graph, ([Para. 0032] the medical professional 102 may open a new path (subcase) in the case having one or more new clinical objectives 107. Examiner interprets the opening of new path to be indicative of receiving a new individual pathway to add to the state transition graph. [Para. 0029] FIG. 1 depicts the current linear workflow (e.g., radiotherapy decision process) in an advanced clinical environment in which a medical professional integrates information learned from a previous treatment into the specific treatment (e.g., current treatment fraction) and application of a new clinical objective determined by workflows, which is indicative of adding a new pathway to the state transition graph.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of constructing a state transition graph for treatment , procedure, and progression as taught by Aggarwal and incorporate adaptive treatment management system that provides independent treatment modeling, delivery, and assessment as taught by Van der Koijk, with the motivation of provide workflow planning and support for a variety of therapeutic tasks included in a particular treatment such as, among other things, diagnosis, treatment planning, monitoring, and record keeping (Van der Koijk Para. 0003).
As per Claim 21, Claim(s) 21 is/are analogous to Claim(s) 10, thus Claim(s) 21 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 10.
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal (US 20100125462 A1) in view of Hu (US 20180082025 A1) in view of De La Torre (US 20170277857 A1) in view of Pellini (US 20140337052 A1).
As per Claim 23, Aggarwal/ Hu/ De La Torre disclose the method of claim 1, however teaches wherein the received user-defined parameter for the one or more response states to the one or more qualifying events comprises splitting one or more response states into immediate response status, comprising complete response, partial response, and no response. ([Para. 0012] The outcome information includes information regarding a status of a patient's cancer. The status of the patient's cancer includes one or more of complete response, partial response, stable disease (i.e. no response), a progressive disease The course of treatment includes information regarding how patient is responding to treatment over time.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of cost-benefit analysis in real-time for any well-defined medical treatment protocol as taught by Aggarwal, accessing the various subsets of medical episode sequences in any order as taught by Hu, the healthcare risk knowledge graph as taught by De La Torre, and incorporate capturing outcome information associated with various cancer treatments as taught by Pellini, with the motivation of providing physicians and other medical personnel a tool that would allow them to locate more quickly appropriate treatment and outcome information (Pellini Para. 0008).
Response to Arguments
Applicant's arguments, see pgs. 9-19 “Claim Rejections – 35 U.S.C. 101” filed 03/17/2026 have been fully considered but they are not persuasive.
Applicant argues that the claims are not directed to an abstract idea and do not fall into the abstract grouping of organizing human activity. The claims do not recite a method that is performed by a human following instructions (whether alone, with other people, or with a computer). The present claims do not recite any interaction between the user [a clinician] and the subject [patients].
Examiner respectfully disagrees. The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to obtain and read medical data to support clinical outcomes. The Examiner notes that Applicant’s background (Para. 0002) suggests that the instant response describes the invention as “construct a graph of genomes, where each sample is represented by a path in the graph, to allow clinicians to capture and discover the diversity of genotypes or haplotypes - and, importantly, complex ones - in the human population, enabling more accurate read alignment”- these behaviors are best categorized as a human task. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instructions that a person or person would follow to obtain and extract medical data to support clinical outcomes, the claimed invention is directed to an abstract idea.
Applicant argues that the claims at issue do not align with any of the examples (i.e., In re Marco Guldenaar Holding B. V., In re Brown, and Bilski v. Kappos) provided in MPEP § 2106.04(a)(2)(II)(C), as the claims at issue do not recite "social activities, teaching, and following rules or instructions."
Examiner respectfully disagrees. The cited court cases and the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to obtain and read medical data to support clinical outcomes. The cited court cases and the claimed invention recite limitations that are best characterized as human tasks, and therefore fall under the abstract grouping of organizing human activity. The Examiner notes that Applicant’s detailed description (Para. 0023) suggests that the instant response describes the invention as “enabling the building of statistical models which can make use of that data for aggregated studies, for example, on the effectiveness of certain drugs or treatment courses, or pathway guidance for individual patients optimized for variables such as specific clinical outcomes, cost and minimum side effects.”- these behaviors are best categorized as human task. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instructions that a person or person would follow to obtain and extract medical data to support clinical outcomes, the claimed invention is directed to an abstract idea.
Applicant argues that the claims do indeed comprise a practical application of the alleged abstract idea. Regarding the analysis of a claim under Step 2A, Prong 2 of the subject matter eligibility analysis (i.e., practical application), the PTAB in Ex parte Chen held that the claim integrated the alleged abstract idea into a practical application for at least four reasons which are discussed below in relation to the present claims (which will addressed individually):
Applicant argues that the present claims address a technical problem in the field of medicine. Applicant argues that the output of the present claims (namely, displaying the user selected subgraphs) is a medical procedure similar to Ex parte Chen presenting to a user the at least one candidate puncture pathway trajectory for determination of the puncture trajectory for use in the medical procedure -which is the output is a medical procedure. Applicant notes that the PTAB holding of Ex parte Chen acknowledges that the fields of medical procedures are technical fields.
Examiner respectfully disagrees. Examiner notes that examination of 101 subject matter eligibility follows a process laid out in the MPEP and does not consider particular non-precedential cases (i.e. Ex parte Chen).
Applicant's arguments, see pgs.19-33 “Claim Rejections – 35 U.S.C. 103”, filed 03/17/2023 have been fully considered but they are not persuasive.
Applicant argues that with regard to independent claims 1, 11, and 11, Aggarwal in view of one or more of Hu, De La Torre, De Peuter, and Van der Koijk fails to suggest or render obvious a method or system for constructing a state transition graph for treatment, procedure, and progression workflow, specifically because Aggarwal fails to disclose one or more collapsible events each comprising a collapse of two or more similar qualifying events or two more similar response states. There is no generation of a state transition graph for treatment, procedure, and progression workflows, no inclusion of collapsible events in a state transition graph. Furthermore, the cited prior art does not teach the newly added limitation of wherein the user-defined parameter for the one or more collapsible events comprises an indication that an order of two or more consecutive events is immaterial such that the two or more consecutive events are collapsible into one combined event for simplifying an individual treatment pathway.
Examiner respectfully disagrees. Aggarwal collects input parameters (i.e. patient case history) from patients that are processed by an analytics engine that generates a state-transition graph. Aggarwal teaches a rule-based engine that receives inputs parameters about the patient such as case histories, treatment protocols, and administered treatments. These inputs parameters are indicative of one or more response states, one or more qualifying events, and one or more collapsible events. This is indicative of the use of the close cohort (i.e. collapsible event) to help with the construction of the state-transition graph. Therefore, Aggarwal teaches the one or more collapsible events each comprising a collapse of two or more similar qualifying events or two more similar response states, and therefore teaches constructing a state transition graph for treatment, procedure, and progression workflow.
Hu teaches at Para.0016 – 0018 that a state transition learner, to parse published clinical guidelines and to extract probabilities of transition between a number of states of a medical condition as a state transition model; a clinical finding learner, to extract typical findings of the medical condition from domain knowledge as a finding model and to compute the probability of a particular finding for a particular state and to save the probabilities in an overall episode model including the state transition model and the finding model; and an episode grouper, to use the overall episode model, and the set of medical records to discover a sequence of events, to group the sequence of events into an episode of the medical condition, and to differentiate the medical condition from apparently similar medical conditions or co-morbidities, or both. Para. 0031 teaches the episode grouper is to match the powerset of the sequence of events in the set of medical records to the overall episode model, with the exception of the empty set and/or any sets including a number of events below a threshold. It is important that the events are kept in the correct order in the powerset, but events in the time line may be omitted (and for instance included later in an episode of another condition). The threshold may be 2 events, so that a “sequence” of a single event is not assessed for a match with an episode. The various subsets of sequences can be assessed in any order, for example in random order or fully/partially in parallel. Examiner interprets the assessment of the subsets of sequences to be indicative of an order of the two or more consecutive events is immaterial. Para. 0091 further teaches to identify the most appropriate sequence of patient's treatments or other findings (documented in his/her medical records) that can be grouped into one episode characterized by E(d)=π.sub.0.sup.d, . . . , π.sub.m.sup.d where dεD is a disease and D is the set of diseases whose progression trajectories are known or can be learnt from existing data. This gives benefit of fully understanding the episode (then one can group treatments together for analysis/diagnosis/payment management). Para. 0120 teaches the overall model contains states and findings. A patient record contains events. These events are projected on the overall model so as to be grouped. The outcomes of the process are that such events are grouped into episode(s) for better understanding of the connections between the events in a patient record. Para. 0121 further teaches the system inputs an electronic patient record. It scans through the record and identifies the key events that can be aligned.). This is indicative of wherein the user-defined parameter for the one or more collapsible events comprises an indication that an order of two or more consecutive events is immaterial such that the two or more consecutive events are collapsible into one combined event for simplifying an individual treatment pathway.
Applicant argues that Aggarwal fails to disclose "one or more qualifying events," the "one or more response states to the one or more qualifying events," and the "one or more collapsible events" are generated based on user-defined parameters received by the system.
Examiner respectfully disagrees. Aggarwal teaches a rule-based engine that receives inputs parameters about the patient such as case histories, treatment protocols, and administered treatments. These inputs parameters are indicative of one or more response states, one or more qualifying events, and one or more collapsible events.
Applicant argues that there is no motivation to combine Aggarwal, Hu, and De La Torre to teach on the claimed invention.
Examiner respectfully disagrees. Aggarwal teaches an analysis that acquires input parameters from a patient and performs statistical and computational analysis of case histories of the patient and other patient related to one or more disease treatment protocols to generate potential outcome of treatment protocols on a patient (Aggarwal Abstract). Hu teaches using the overall episode model, and the set of medical records to discover a sequence of events, to group the sequence of events into an episode of the medical condition, and to differentiate the medical condition from apparently similar medical conditions or co-morbidities (Hu Para. 0039). This provides the benefits of helping to differentiate apparently similar disease or co-morbidities by clearly defining progress and episode of each to give more accurate diagnosis and treatment. In addition, episode grouping help to define and develop more personalized treatment based on the progression of the disease and provide better health service at an early stage and project a patient's progress along established trajectories (Hu Para. 0163-0165). Therefore, the combination of these references would be obvious because all the references obtain patient medical data from medical records and organize patient data into grouping or graphing models to identify patient data (i.e. outcomes, treatments, symptoms, personal information) belonging to a specific medical disease or condition in order to determine optimal treatment options for patients. De La Torre teaches on assessing patient treatment risk by collecting patient medical data, clinician input, and open source information to create a patient risk graph (De La Torre Abstract). The patient risk graph provides specific information for a patient, in a way that is easily comprehensible and can help a clinician or other user to foresee risks. The impact estimator module then uses this patient-specific information in combination with the open data in the healthcare risks knowledge graph to estimate risks which are not currently available from the patient's own data, but are associated with the treatment that the patient is receiving (De La Torre Para. 0009-0010). Therefore, the combination of these references would be obvious because all the references obtain patient medical data and organize the data into a graph or model, in order to determine the impact of treatments on the patient(s) and to combine the user-defined, order-immaterial collapsing of similar events/ states into a single collapsible event.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Patricia K Edouard whose telephone number is (571)272-6084. The examiner can normally be reached Monday - Friday 7:30 AM - 5:00 PM.
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/P.K.E./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
