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. Drawings The drawing filed on July 21, 2023 has been accepted and reviewed. Specification The specification filed on July 21, 2023 has been accepted and reviewed. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim s 1, 3 , 5, 6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over ZHANG et al. (US 20230229906 A1 --hereinafter—"ZHANG”) in view Van Assel et al. (20210233658 ---hereinafter--- Van Assel). As per claim 1: ZHANG discloses a computer-implemented method, the method comprising: receiving, from a user device, a first set of indicators associated with a condition experienced by a user ([0029] For instance in a medical example, the variables represent different properties of a person or other living being (e.g. animal). One or more of the variables may represent a symptom experienced by the living being, e.g. whether the subject is exhibiting a certain condition such as a cough, sore throat, difficulty breathing, etc. (and perhaps a measure of degree of the condition), or a measured bodily quantity such as blood pressure, heart rate, vitamin D level, etc. One or more of the variables may represent environmental factors to which the subject is exposed, or behavioral factors of the subject, such as whether the subject lives in an area of high pollution (and perhaps a measure of the pollution level), or whether the subject is a smoker (and perhaps how many per day), etc. And/or, one or more of the variables may represent inherent properties of the subject such as a genetic factor); generating a directed acyclic graph (DAG) for the user ([0002-0003] Nodes are arranged into layers with at least an input and an output layer. A “deep” neural network comprises one or more intermediate or “hidden” layers in between the input layer and the output layer. The neural network can take input data and propagate the input data through the layers of the network to generate output data. Certain nodes within the network perform operations on the data, and the result of those operations is passed to other nodes, and so on. Each node is configured to generate an output by carrying out a function on the values input to that node. The inputs to one or more nodes form the input of the neural network, the outputs of some nodes form the inputs to other nodes, and the outputs of one or more nodes form the output of the network. At some or all of the nodes of the network, the input to that node is weighted by a respective weight. A weight may define the connectivity between a node in a given layer and the nodes in the next layer of the neural network. A weight can take the form of a scalar or a probabilistic distribution. When the weights are defined by a distribution, as in a Bayesian model, the neural network can be fully probabilistic and captures the concept of uncertainty. The values of the connections between nodes may also be modelled as distributions. The distributions may be represented in the form of a set of samples or a set of parameters parameterizing the distribution (e.g. the mean μ and standard deviation a or variance σ.sup.2).(The examiner notes: A Directed Acyclic Graph (DAG) is Directed (edges have a specific direction from A to B), Acyclic (no loop back path to the starting point), and Graph ( a collection of vertices (nodes) and edges (connections)). Deep Neural Network (DNN) are a specialized architecture DAG. Most DNNs like feedforward networks, are technically DAGs. They have unique properties that standard DAGs do not). [0040-0042] Possible graphs are constrained to being directed acyclic graphs (DAGs), for the sake of practicality and simplicity of modelling. [0096] In the structural equation modelling (SEM) framework, causal discovery refers to uncovering the directed acyclic graph (DAG) that models causal relationships between variables, whilst causal inference uses this DAG to perform calculations, yielding estimates of (C)ATE. Standard causal inference methods assume that the DAG is already known. The classical approach, then, is to use a causal discovery method to learn the DAG (or its Markov equivalence class), and then plug this into existing methods for causal inference); wherein the DAG comprises: a first layer of nodes that each correspond to an indicator, wherein the first layer of nodes comprises a first node ([0077] FIG. 5 illustrates Observed condition (Xsub2) x.sub.3 is the target x.sub.Y whose treatment effect will be estimated and x.sub.4 is the treatment x.sub.T, where the treatment is the cause of the target effect. In addition, there is another, unobserved cause x.sub.1 of the target effect, and another observable effect x.sub.2 of the unobserved cause x.sub.1. The variable x.sub.2 is to be the observed condition x.sub.C. a second layer of nodes that each correspond to a cause, wherein the second layer of nodes comprises a second node, wherein each node of the first layer of nodes is connected to each node of the second layer of nodes by an edge, and wherein the edge between the first node of the first layer of nodes and the second node of the second layer of nodes is associated with a probability that the indicator corresponding to the first node is indicative of the cause corresponding to the second node ([0077] FIG. 5 illustrates by way of example why estimating the conditional treatment effect is not necessarily straightforward. In this example x.sub.3 is the target x.sub.Y whose treatment effect will be estimated and x.sub.4 is the treatment x.sub.T, where the treatment is the cause of the target effect. In addition, there is another, unobserved cause x.sub.1 of the target effect, and another observable effect x.sub.2 of the unobserved cause x.sub.1. The variable x.sub.2 is to be the observed condition x.sub.C. For instance, in a medical example the target effect x.sub.3 could be some condition or symptom of the subject (e.g. a respiratory problem), the treatment x.sub.4 could be a possible medical intervention (e.g. taking some drug or vitamin), the unobserved cause x.sub.1 may be a genetic factor, and the other observable cause x.sub.2 may be some observable physical quality of the subject's body (e.g. body mass index). In general the unobserved cause could be unobservable, or merely unobserved. [0079] However the causal direction is from x.sub.1.fwdarw.x.sub.2, and the model 104 of FIG. 2 is only configured to learn effects of causes in the direction from cause to effect—it is not configured to learn inferences of effect from cause. I.e. it is not configured to “go against the arrows” in the figure (the directional causal edges); a third layer of nodes that each correspond to a treatment, wherein the third layer of nodes comprises a third node, wherein each node of the second layer of nodes is connected to each node of the third layer of nodes by an edge ([0077] FIG. 5 illustrates by way of example why estimating the conditional treatment effect is not necessarily straightforward. In this example x.sub.3 is the target x.sub.Y whose treatment effect will be estimated and x.sub.4 is the treatment x.sub.T, where the treatment is the cause of the target effect. In addition, there is another, unobserved cause x.sub.1 of the target effect, and another observable effect x.sub.2 of the unobserved cause x.sub.1. The variable x.sub.2 is to be the observed condition x.sub.C. For instance, in a medical example the target effect x.sub.3 could be some condition or symptom of the subject (e.g. a respiratory problem), the treatment x.sub.4 could be a possible medical intervention (e.g. taking some drug or vitamin), the unobserved cause x.sub.1 may be a genetic factor, and the other observable cause x.sub.2 may be some observable physical quality of the subject's body (e.g. body mass index). In general the unobserved cause could be unobservable, or merely unobserved. [0079] However the causal direction is from x.sub.1.fwdarw.x.sub.2, and the model 104 of FIG. 2 is only configured to learn effects of causes in the direction from cause to effect—it is not configured to learn inferences of effect from cause. I.e. it is not configured to “go against the arrows” in the figure (the directional causal edges); traversing the DAG to determine a likely cause (0099] The present disclosure provides a new framework for end-to-end causal inference that offers a practical and flexible method for moving directly from data to (C)ATE estimation, and so to real-world decision making. There is provided both a general framework that consumes any model that consists of both a distribution over possible graph structures, and fitted arrow functions (the functions that map the set of parents to a distribution over the child), and estimates (C)ATE. [0100] There is also provided a specific flow-based model that satisfies these two requirements); traversing the DAG to determine at least one treatment for the likely cause ([0105] Firstly, a general framework for end-to-end causal inference. A Bayesian perspective is taken, that places a posterior distribution over possible DAGs. In treatment effect estimates, the method marginalises over the posterior uncertainty in the DAG. [0106] Secondly, a specific flow-based model for causal modelling. The disclosed model incorporates several novel features that are useful in practical causal problems: [0107] it allows the partial specification of prior information for the DAG, allowing unknown features of the graph to be learned from data, [0108] its allows training with both observational and interventional data, [0109] is uses a flow-based model that can treat a mixture of continuous and discrete data, [0110] it models non-Gaussian additive noise for continuous variables); generating a custom treatment plan for the user based on ([0058] At step T10 the index i is set to that of a target variable x.sub.i whose treatment effect is to be estimated. In addition, the input values of one or more “intervened-on” variables are also set to their known values. The intervened-on variables are variables whose values are set to some specified value, to represent that the property that they represent has been controlled (the treatment, i.e. an intervention on the modelled property). An “intervened-on” variable could also be referred to as a treated variable or controlled variable. For example, in the medical application, the intervened-on variable(s) may represent one or more interventions performed on the subject, and the target variable may represent a possible symptom or condition of the subject (e.g. the presence of a certain disease). Or in the case where the subject is a device or software, the intervened-on variable(s) may represent one or more states that are set to defined values, and the target variable may represent a condition or state of the device or software that is being diagnosed. [0069] Based on the trained model 104, it is possible using statistical methods to estimate an expectation E[p(x.sub.Y)|do(x.sub.T=val.sub.1)] of the probabilistic distribution p of a particular target variable x.sub.i=Y given the intervened value val1 of another, treatment/intervention variable x.sub.i=T. In embodiments, the target variable x.sub.Y may model an outcome of a treatment modelled by x.sub.T. Note that “treatment” as used most broadly herein does not necessarily limit to a medical treatment or a treatment of a living being, though those are certainly possible use cases. In other examples, the treatment may comprise applying a signal, repair, debugging action or upgrade to an electronic, electrical or mechanical device or system, or software, where the effect may be some state of the device, system or software which is to be improved by the system. In embodiments, the actual real-world treatment may be applied in dependence on the estimation (e.g. expectation) of the effect of the modelled treatment, for example on condition that the treatment estimation (e.g. expectation) is above or below a specified threshold or within a specified range); the at least one treatment for the likely cause ([0075] Another type of average which may be determined according to embodiments disclosed herein may be referred to as the conditional ATE (CATE). This is the difference between: a) the expectation of the target variable xv given the treatment x.sub.T=val1. conditional on an observation γ of at least one other of the variables x.sub.C in the set, and b) the expectation of the target variable x.sub.Y without the treatment (either with a different treatment val2 or no treatment) but still conditional on the same observation of x.sub.C. [0082] The inclusion of the inference model makes it possible to estimate a conditional expectation). ZHANG does not explicitly disclose wherein the edge between the second node of the second layer of nodes and the third node of the third layer of nodes is associated with a probability that the treatment corresponding to the third node addresses the cause corresponding to the second node and a cost function . Van Assel, in analogous art however, discloses wherein the edge between the second node of the second layer of nodes and the third node of the third layer of nodes is associated with a probability that the treatment corresponding to the third node addresses the cause corresponding to the second node and a cost function . ([0069] Based on the trained model 104, it is possible using statistical methods to estimate an expectation E[p(x.sub.Y)|do(x.sub.T=val.sub.1)] of the probabilistic distribution p of a particular target variable x.sub.i=Y given the intervened value val1 of another, treatment/intervention variable x.sub.i=T. In embodiments, the target variable x.sub.Y may model an outcome of a treatment modelled by x.sub.T. Note that “treatment” as used most broadly herein does not necessarily limit to a medical treatment or a treatment of a living being, though those are certainly possible use cases. In embodiments, the actual real-world treatment may be applied in dependence on the estimation (e.g. expectation) of the effect of the modelled treatment, for example on condition that the treatment estimation (e.g. expectation) is above or below a specified threshold or within a specified range. [0078] The observable condition x.sub.2 contains information about the unobserved cause x.sub.1, which in turn can reveal information about the desired effect x.sub.3 (=x.sub.Y). For example if it is known that an athlete is in the Olympics and that they are a footballer, this reduces the probability that they are also a rower. In fact for any two variables that are effects of a common cause and whose intersection is not 100%, knowing something about one gives information about the other. E.g. if it is known that a subject has lung cancer and that they were exposed to a carcinogenic chemical other than tobacco, this reduces the probability that they were are smoker. [0118] To this end, there is presented herein an end-to-end (E2E) framework for causal inference. This framework gives the graph G a probabilistic treatment. In real-world settings, the causal relationships among variables can be very complicated. Thus, combining the output of causal discovery methods, that act on observations, with domain knowledge might not be enough to fully determine G. Instead embodiments herein model the uncertainty over the causal relationships that govern the data using a posterior over graphs. Van Assel further discloses the above limitation in ([0120] The prior, p(G) reflects beliefs about the causal graph drawn from domain expertise. This probabilistic formation allows hard constraints about specific sets of edges which may be present, or soft beliefs about roughly how many edges should be active or which groups of edges are likely to appear together. A more informative prior drives inferences closer to the purely causal domain. The graph posterior (eq. 11) may be leveraged to introduce a new type of hybrid causal-probabilistic inference, which combines causal beliefs with probabilistic marginalisation over the parts of the graph not specified by these beliefs. [0153] Determining an average treatment effect, by: estimating a first expectation of a probabilistic distribution of the target variable given the specified value of each intervened-on variable; estimating a second expectation of a probabilistic distribution of the target variable without the specified value of at least one of the one or more intervened-on variables or with a different value of at least one of the one or more intervened-on variables; and determining a difference between the first and second expectations of the probabilistic distribution, thus giving the average treatment effect as the estimated treatment effect of the target variable. [0154] An inference network disposed between an unobserved one of the variables of said set and one or more observable ones of the variables of said set, arranged to infer the unobserved variable from the one or more observables variables. In such embodiments, the average treatment effect being estimated may comprise a conditional average treatment effect. In this case, the first expectation comprises an expectation of a probabilistic distribution of the target variable given the specified value of each intervened-on variable conditional on the input value of at least one of the observed variables other than the intervened-on variable; and the second estimation comprises an expectation of a probabilistic distribution of the target variable without the specified value of at least one of the one or more intervened-on variables, or with a different value of at least one of the intervened-on variables, but still conditional on the input value of said at least one observed variable. [0204] Hybrid approach might be easier and potentially less computationally expensive, in cases when P( X.sub.i|X.sub.S ∪ ) is far from P(X.sub.i|), this will be just a first-order approximation, hence the variance will be higher and we generally need more samples to get a reliable estimate . Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the claimed limitations of wherein the edge between the second node of the second layer of nodes and the third node of the third layer of nodes disclosed by ZHANG to is associated with a probability that the treatment corresponding to the third node addresses the cause corresponding to the second node and a cost function. This modification would have been obvious because a person having ordinary skill in the art would have been motivated by the desire to provide an integrated machine learning model that both models the causal relationships between variables and performs treatment effect estimation as suggested by Van Assel in ([0008]). As per claim 3: ZHANG in view Van Assel discloses the computer-implemented method of claim 1, wherein the directed acyclic graph comprises a recurrent tripartite connected directed acyclic graph (Van Assel [0034] The different variables x.sub.i may have a certain causal relationship between them, which may be expressed as a causal graph. A causal graph may be described as comprising a plurality of nodes and edges (note that these are not the same thing as the nodes and edges mentioned earlier in the context of a neural network). Each node represents a respective one of the variables x.sub.i in question. The edges are directional and represent causation. I.e. an edge from x.sub.i=k to x.sub.i=l represents that x.sub.k causes x.sub.1 (x.sub.1 is an effect of x.sub.k ). A simple example involving three variables is shown in FIG. 3. In this example x.sub.2 causes x.sub.1, and x.sub.1 causes x.sub.3. For example x.sub.3 may represent having a respiratory virus, x.sub.1 may represent a lung condition and x.sub.2 may represent a genetic predisposition). As per claim 5: ZHANG in view Van Assel discloses the computer-implemented method of claim 1, wherein every node of the DAG is stateful and comprises a presence of an indicator as a percentage (Van Assel [0192] The neural network is then trained using a cross entropy loss function in step S207 in a multi-label classification setting to predict the state of all observed and unobserved nodes). As per claims 6 and 8: Claims 6 and 8 are directed to a non-transient computer readable medium containing program instructions for causing a computer to perform a method having substantially similar corresponding limitations of claims 1 and 3 respectively and therefore, claims 6 and 8 are rejected with the same rationale given above to reject claims 1 and 3. Claim s 4 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over ZHANG et al. (US 20230229906 A1 --hereinafter—"ZHANG”) in view Van Assel et al. (20210233658 ---hereinafter--- Van Assel) in further view of KRISHNAMURTHY et al. (US 20210043194 A1 --- hereinafter --- “KRISHNAMURTHY”). As per claims 4 and 9: ZHANG in view Van Assel does not explicitly disclose wherein traversing the DAG comprises a random sample consensus (RANSAC) approach . KRISHNAMURTHY, in analogous art however, discloses wherein traversing the DAG comprises a random sample consensus (RANSAC) approach ([0109] Non-limiting examples of training procedures for adjusting trainable parameters include supervised training (e.g., using gradient descent or any other suitable optimization method), zero-shot, few-shot, unsupervised learning methods (e.g., classification based on classes derived from unsupervised clustering methods), reinforcement learning (e.g., deep Q learning based on feedback) and/or generative adversarial neural network training methods, belief propagation, RANSAC (random sample consensus), contextual bandit methods, maximum likelihood methods, and/or expectation maximization. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the claimed limitations of traversing the DAG disclosed by ZHANG in view Van Assel to include a random sample consensus (RANSAC) approach. This modification would have been obvious because a person having ordinary skill in the art would have been motivated by the desire to exploit a model that has been trained for causal discovery in order to estimate effects and thus enabl ing “end-to-end” causal inference as suggested by ZHANG ([00 10 ]). Allowable Subject Matter Claims 2 and 7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: T he pertinent prior arts of record, either taken alone or in combination neither anticipates nor renders obvious the claimed subject matter of the following claims when taken as a whole together with their respective independent claims As per claims 2 and 7: T racking a second set of indicators from the user following the custom treatment plan; updating the DAG based on the second set of indicators; traversing the updated DAG to determine an updated cause; traversing the updated DAG to determine at least one treatment for the updated cause; and generating an updated treatment plan for the user based on the at least one treatment for the updated cause. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Frieder et al ( US 20220115104 A1 ) discuses techniques to predict the success or failure of a drug used for disease treatment. D etermining drug efficacy that include s , for a plurality of patients, generating a directed acyclic graph from health related information of each patient comprising nodes representing a medical event of the patient, at least one first edge connecting the first node to an additional node, each additional edge connecting nodes representing two consecutive medical events, the edge having a weight based on a time difference between the two consecutive medical events, capturing a plurality of features from each directed acyclic graph, generating a binary graph classification model on captured features of each directed acyclic graph, determining a probability that a drug or treatment will be effective using the binary graph classification model, and determining a drug to be prescribed to a patient based on the determined probability. Gupta et al ( US 12099480 B1 ) describes a graph-based clinical concept mapping algorithm maps ICD-9 (International Classification of Disease, Revision 9) and ICD-10 (International Classification of Disease, Revision 10) codes to unified Systematized Nomenclature of Medicine (SNOMED) clinical concepts to normalize longitudinal healthcare data to thereby improve tracking and the use of such data for research and commercial purposes. The graph-based clinical concept mapping algorithm advantageously combines a novel graph-based search algorithm and natural language processing to map orphan ICD codes (those without equivalents across codebases) by finding optimally relevant shared SNOMED concepts. The graph-based clinical concept mapping algorithm is further advantageously utilized to group ICD-9/10 codes into higher order, more prevalent SNOMED concepts to support clinical interpretation. Gotz et al ( US 20130103719 A1 ) discuses an interactive visualization of temporal event data and correlated outcomes. The temporal event data comprises a plurality of entities undergoing one or more events. The temporal event data is aggregated and a flow graph is generated to represent the aggregated temporal event data. The flow graph comprises a directed acyclic graph having a plurality of nodes connected by edges, wherein each of the nodes represents a group of entities in a given state. A view of the flow graph is generated and then a visualization of the flow graph view can be rendered to a user. The user can interact with the flow graph view and the visualization and/or the flow graph view can be updated based on the user interactions. The flow graph is sliced into layers, wherein a given layer i contains all nodes with i events. 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